Car-pedestrian accidents account for a considerable number of automobile accidents in industrialized countries. Head injury continues to be more concerned in Automobile impacts.
Because the head is the most seriously injured part in many collisions including in pedestrian automobile collisions. Head injuries are the most common cause of pedestrian deaths in car to pedestrian collisions.
DESIGN & STRUCTURAL PERFORMANCE ANALYSIS OF SUPRA SAE CAR CHASSISPrashant Sahgal
This document summarizes the design and structural analysis of a racing car chassis. It describes using CATIA software to model the chassis and ANSYS for finite element analysis to simulate various loads on the chassis, including front impact, side impact, torsion, bumps, and rollovers. The analysis found maximum deformations between 1-5 mm and stresses below 300 MPa across the tests, with safety factors above 1.5 in all cases. The document concludes the finite element analysis provided valuable insights for designing a chassis that can withstand the forces of racing.
Time v Frequency Domain Analysis For Large Automotive SystemsAltair
CAEfatigue VIBRATION is a second generation frequency domain random response and fatigue analysis solver that uses transfer functions from solvers like Nastran as input and outputs random response and fatigue damage results. It allows for more flexible loads like mixed random and deterministic loads and the ability to apply multiple simultaneous inputs. CAEfatigue VIBRATION provides a simpler implementation, more robust solutions, and is suitable for very large models compared to first generation tools.
Volkswagen has invested 4000 crores to set up its state-of-the-art manufacturing facility in Chakan, Pune covering 527 acres. The tour visited the body shop where car bodies are manufactured using robots and the assembly hall where accessories are installed on cars within 117 seconds. The Chakan plant has a production capacity of 130,000 vehicles per year and employs 3700 people across direct, indirect and contracted roles. Volkswagen exports cars to over 50 countries from the Chakan plant.
Roof Crush Analysis using Test Protocols of FMVSS 216Vaibhav porwal
Validation of Roof Crush analysis involving passenger dummy, side-airbag, and steering wheel. Optimized the roof strength to improve the design of the B-pillar and material property.
This document discusses automotive aerodynamics and provides an overview of key concepts. It defines aerodynamics as the study of moving air and its effects on objects in motion. Some key aerodynamic principles for vehicles are explained, including lift, thrust, weight, and drag. The document also discusses downforce, aerodynamic devices used in cars like wings and spoilers, and methods of aerodynamic analysis including wind tunnels and software. It emphasizes that improving a vehicle's aerodynamics through design can significantly increase its fuel efficiency.
Car-pedestrian accidents account for a considerable number of automobile accidents in industrialized countries. Head injury continues to be more concerned in Automobile impacts.
Because the head is the most seriously injured part in many collisions including in pedestrian automobile collisions. Head injuries are the most common cause of pedestrian deaths in car to pedestrian collisions.
DESIGN & STRUCTURAL PERFORMANCE ANALYSIS OF SUPRA SAE CAR CHASSISPrashant Sahgal
This document summarizes the design and structural analysis of a racing car chassis. It describes using CATIA software to model the chassis and ANSYS for finite element analysis to simulate various loads on the chassis, including front impact, side impact, torsion, bumps, and rollovers. The analysis found maximum deformations between 1-5 mm and stresses below 300 MPa across the tests, with safety factors above 1.5 in all cases. The document concludes the finite element analysis provided valuable insights for designing a chassis that can withstand the forces of racing.
Time v Frequency Domain Analysis For Large Automotive SystemsAltair
CAEfatigue VIBRATION is a second generation frequency domain random response and fatigue analysis solver that uses transfer functions from solvers like Nastran as input and outputs random response and fatigue damage results. It allows for more flexible loads like mixed random and deterministic loads and the ability to apply multiple simultaneous inputs. CAEfatigue VIBRATION provides a simpler implementation, more robust solutions, and is suitable for very large models compared to first generation tools.
Volkswagen has invested 4000 crores to set up its state-of-the-art manufacturing facility in Chakan, Pune covering 527 acres. The tour visited the body shop where car bodies are manufactured using robots and the assembly hall where accessories are installed on cars within 117 seconds. The Chakan plant has a production capacity of 130,000 vehicles per year and employs 3700 people across direct, indirect and contracted roles. Volkswagen exports cars to over 50 countries from the Chakan plant.
Roof Crush Analysis using Test Protocols of FMVSS 216Vaibhav porwal
Validation of Roof Crush analysis involving passenger dummy, side-airbag, and steering wheel. Optimized the roof strength to improve the design of the B-pillar and material property.
This document discusses automotive aerodynamics and provides an overview of key concepts. It defines aerodynamics as the study of moving air and its effects on objects in motion. Some key aerodynamic principles for vehicles are explained, including lift, thrust, weight, and drag. The document also discusses downforce, aerodynamic devices used in cars like wings and spoilers, and methods of aerodynamic analysis including wind tunnels and software. It emphasizes that improving a vehicle's aerodynamics through design can significantly increase its fuel efficiency.
Pushkaraj Bhagwat is seeking a full-time position in product design and development utilizing his 2 years of experience in finite element analysis, computational solid mechanics, modeling and simulation. He has a Master's in Mechanical Engineering from the University of Cincinnati and experience with ANSYS, Abaqus, CATIA, SolidWorks and other engineering tools. His project experience includes structural design of a Hyperloop pod, crash analysis, fluid dynamics simulations, and optimization of diaper manufacturing equipment.
Case Study of Toyota Unintended Acceleration and Software SafetyPhilip Koopman
Investigations into potential causes of Unintended Acceleration (UA) for Toyota vehicles have made news several times in the past few years. Some blame has been placed on floor mats and sticky throttle pedals. But, a jury trial verdict was based on expert opinions that defects in Toyota's Electronic Throttle Control System (ETCS) software and safety architecture caused a fatal mishap. This talk outlines key events in the still-ongoing Toyota UA litigation process, and pull together the technical issues that were discovered by NASA and other experts. The results paint a picture that should inform future designers of safety critical software in automobiles and other systems.
Author Bio:
Prof. Philip Koopman has served as a Plaintiff expert witness on numerous cases in Toyota Unintended Acceleration litigation, and testified in the 2013 Bookout trial. Dr. Koopman is a member of the ECE faculty at Carnegie Mellon University, where he has worked in the broad areas of wearable computers, software robustness, embedded networking, dependable embedded computer systems, and autonomous vehicle safety. Previously, he was a submarine officer in the US Navy, an embedded CPU architect for Harris Semiconductor, and an embedded system researcher at United Technologies. He is a senior member of IEEE, senior member of the ACM, and a member of IFIP WG 10.4 on Dependable Computing and Fault Tolerance. He has affiliations with the Carnegie Mellon Institute for Software Research (ISR) and the National Robotics Engineering Center (NREC).
Presentation Date: September 18, 2014.
- A normal modes analysis was performed on a finite element model of a clamping set to determine its vibration mode shapes. The model was imported into HyperMesh and material properties and constraints were applied.
- An eigenvalue extraction was specified to calculate the first 6 modes. The results were viewed in HyperView and showed the component deforming in different patterns for each mode.
An Automated Head Impact Process Setup for Automobile Instrument Panel (IP) A...Altair
Calsonic Kansei developed an automated head impact process using HyperMesh to position virtual head forms on instrument panels for crash simulation. The new process automates tasks that were previously done manually, reducing setup time from 8 hours to 30 minutes. Key benefits include improved efficiency, reduced errors, and the ability for engineers to complete more design projects within time and budget constraints. The automation helps Calsonic Kansei evaluate head impact performance faster and reduce vehicle development costs.
Rotating machinery can be found in every industry: automotive, aerospace, energy, etc. The generated vibration environment is typically made of harmonic tones superimposed on background noise. Components mounted on rotating machinery must be designed to survive such mechanical environment over their entire service life. This presentation will concentrate on calculating the fatigue life from sine-on-random excitations using Finite Element Analysis (FEA). It is proposed to derive the statistical rainflow cycle histogram from a sine-on-random spectrum of stress or strain data and then use the appropriate material fatigue curve to obtain the estimated life. This new analysis is complementary to existing features such as SineDwell, SineSweep and (uni- or multi-axes) random PSD. It is part of extensive research work that includes the influence of sigma clipping or the effects of a high kurtosis.
Speakers
Frédéric Kihm, Application Engineer, HBM-nCode
This document summarizes the capabilities of seat evaluation simulations including frontal/rear impact, seatbelt anchorage tests, child restraint systems, head restraint strength, and other tests. The simulations use standardized procedures and load cases to evaluate compliance with standards. Comprehensive reports are generated identifying critical areas, displacement/stress outputs, and countermeasures. Correlation with physical test results is also provided.
The team designed a suspension system for an SAE Mini Baja vehicle with 11 inches of travel front and rear. The suspension uses a dual A-arm design to allow full travel without interference while maintaining optimal camber. It is adjustable for ride height and stiffness via the pushrod connection on cams. Analysis and modeling showed the design can withstand impacts of 4 feet drops and 2000 pound horizontal impacts on each wheel, with a minimum safety factor of 2.34. The presentation provided details on the final specifications, component designs, analysis results, budget and schedule.
Developing Commercial Vehicles Inspired by NatureAltair
As Germany's largest independent engineering partner to the worldwide automotive industry, EDAG is continuously seeking for new technologies and innovative processes to streamline vehicle development. EDAG has a profound expertise in integrated development and the optimization of vehicles, production facilities, derivatives, and modules. To meet fuel efficiency and emission reduction goals, structurally efficient lightweight designs are demanded in the development of passenger cars and commercial vehicles alike. To fulfill customer demands and to deliver lighter and yet fully functional and validated components in shorter time, EDAG is leveraging its engineering knowledge to combine state-of-the-art computer aided engineering tools, in this case Altair's OptiStruct, with new production technologies such as additive manufacturing. OptiStruct enabled the EDAG engineers to design lightweight and, by being inspired by nature, yet stiff structures of a cabin and a chassis. The components were then manufactured using additive manufacturing methods. To find the optimal solution for the final design the engineers later also conducted multi-physical optimizations, combining strength and crash demands of the vehicle, using an equivalent linear approach. The entire development and manufacturing process for the cabin and chassis structures will be subject of this presentation, showing how a combination of topology optimization and additive manufacturing leads to lighter and stiffer products. The project is a prime example of how mature CAE technology can be adjusted and used in combination with new manufacturing methods to introduce revolutionary structural enhancements within the transportation sector.
Speakers
Andreas Pfeiffer, Development Engineer, EDAG
This document outlines various safety standards for vehicles from different organizations. It includes standards for frontal impact, bumpers, side impact protection, rear impact, headrests, seats, pedestrian protection, steering columns, roof crush resistance, and rollover protection. The standards are from the FMVSS (Federal Motor Vehicle Safety Standards) in the US, ECE (Economic Commission for Europe) regulations, and AIS (Abbreviated Injury Scale) assessments.
Formula 1 cars rely heavily on aerodynamics to produce downforce, using large front and rear wings. They are single-seater, open cockpit vehicles powered by 3.0-liter engines. Teams construct the cars themselves, which use advanced carbon fiber materials and finely tuned fuel blends. Aerodynamic features like the front wings, rear wing, barge boards, and specialized suspensions and wheels are crucial for performance and reducing drag. Major Formula 1 teams include Ferrari, McLaren, Williams, and Renault.
This document outlines the aerodynamic design process for a Formula Student race car. It discusses theory, conceptual wing designs, theoretical lift calculations, and experimental testing. The goal is to prove the benefits of front and rear wings for improving stability, braking, and cornering at low speeds. The design process involves baseline testing, flow visualization, selecting airfoil profiles, sizing wings, and conducting coast down and wind tunnel tests to evaluate downforce. Computational fluid dynamics simulations are also used to analyze pressure and velocity contours. The results will help determine the most effective wing designs for the low speeds of the Formula Student car.
The document discusses diesel emissions regulations and exhaust after-treatment technologies for modern diesel engines. It covers:
1) Changes in US emissions standards over time that have driven new technologies.
2) Key technologies developed to reduce emissions include advanced fuel injection systems, alternative fuels like biodiesel, and exhaust after-treatment devices.
3) Common exhaust after-treatment methods mentioned are diesel particulate filters, NOx adsorber catalysts, selective catalytic reduction using urea injection, and catalyzed diesel particulate filters.
This document discusses engineering design and testing for head impacts. It outlines establishing head impact zones, determining locations that could experience hits, performing head impact tests at impact points with various angles, and evaluating parts to determine if they pass or fail standards.
This document evaluates the effectiveness of virtual validation methods like finite element analysis (FEA) for testing automotive seating systems. It discusses the various challenges in seating system design given the need for comfort, safety and health. A variety of physical and analytical validation tests are described, including head restraint performance, seat anchorage strength, and fatigue resistance testing. The document achieves correlations of 87-92% between physical test and FEA simulation results, demonstrating the effectiveness of virtual validation methods for seating system testing.
DESIGN AND DEVELOPMENT OF A TRANSMISSION SYSTEM FOR AN ALL TERRAIN VEHICLEIAEME Publication
The main function of a transmission system is to transfer the required torque and power generated by the engine to the wheels as and when required by the driver. In automobiles this is done with the help of a gearbox and a final drive alternative. The objective of this work is to design and develop a transmission system which is reliable, safe and cost effective. It should be able to transmit sufficient power and torque to generate the required traction at the wheels at any particular rpm. As the vehicle under consideration is an All-Terrain Vehicle (ATV), which is subjected to varying and rugged road conditions, the power transmission should be constant and uninterrupted.
Aimil is an ISO 9001 certified instrumentation company founded in 1932 with over 750 professionals. It aims to provide optimal quality and customer service through innovation and integration of leading edge global technologies. The company serves various industries including aerospace, automotive, construction, and healthcare. It provides a range of instrumentation products and services for noise, vibration, and harshness measurement and analysis.
“CONCEPT VALIDATION AND DESIGN SYNTHESIS OF CAR DASHBOARD AS PER PLASTIC TRIM...Jayesh Sarode
The evolution of dashboard has led to increased
vehicle occupant comfort and convenience as new systems
become available. The project work aims to develop the work
to apply theoretical and practical tools/techniques to solve
real life problems related to industry and current research in
this project automobile (Car) dashboard upper cover is
selected for a design. For doing so conceptual design tool is
used. I am created a five different concepts using different
benchmarking & brainstorming. Select a best option using
different tools in six sigma like trade off analysis. Threedimensional
CAD software (such as CATIA) is used to develop a
CAD model same as concept. The aim is to achieve the essential
function at the lowest overall cost while maintaining
customers’ optimum value assurance. In this project I try to
develop a such dashboard design which follow a design
guidelines, And analyze and discuss the results to Obtain valid
conclusions which follows a design Standards.
Finite Element Analysis (FEA) is a numerical method for solving complex engineering problems. The document discusses conducting FEA on a fixed-free cantilever beam to study the effect of mesh density on solution accuracy. Analytical solutions are derived and used to validate FEA results. A beam model is created in ABAQUS with varying element sizes. As element count increases, FEA results converge towards analytical solutions, though with increased computation time. An element count of 4125 provided an optimal balance between accuracy and cost.
Modification of airflow around a FSAE Race car using sidepods to increase the...EditorIJAERD
Aerodynamics pertaining to vehicles focuses on improving the drive-ability of the vehicle while also reducing
losses due to air drag. This paper focuses on maximizing the cornering performance of the formula student race car with
slight modifications to the airflow around the vehicle and meagre addition of weight. The undertray produces downloads
by altering the velocity of air flowing underneath it. The sidepods act to reduce flow velocity above the undertray, thus
increasing the pressure above it. This leads to an increased pressure difference over the surface of the undertray which
translates to increase in downforce. The car is able to have a 10% decrease in lap times on a 500m racetrack.
Design for 6θ by Valcon - an introduction to the process and methodsmartinebro
6Theta has proven to be a powerful and successful process for obtaining robust designs that perform consistently at high quality. This presentation gives an introduction to what the process contains and provides simple case examples of how it is used.
The document describes how computer aided engineering was used to rapidly explore 60 variations of a stent design overnight. Using nonlinear optimization techniques, design variables like crown radius, strut length, and thickness were assessed. This identified an optimal design with a 125% stiffer radial strength and lower stresses than the baseline design. Nonlinear finite element analysis of stent deployment was accelerated using parallel computing on a cluster. This represented a significant improvement over traditional single analysis verification of stent designs.
Pushkaraj Bhagwat is seeking a full-time position in product design and development utilizing his 2 years of experience in finite element analysis, computational solid mechanics, modeling and simulation. He has a Master's in Mechanical Engineering from the University of Cincinnati and experience with ANSYS, Abaqus, CATIA, SolidWorks and other engineering tools. His project experience includes structural design of a Hyperloop pod, crash analysis, fluid dynamics simulations, and optimization of diaper manufacturing equipment.
Case Study of Toyota Unintended Acceleration and Software SafetyPhilip Koopman
Investigations into potential causes of Unintended Acceleration (UA) for Toyota vehicles have made news several times in the past few years. Some blame has been placed on floor mats and sticky throttle pedals. But, a jury trial verdict was based on expert opinions that defects in Toyota's Electronic Throttle Control System (ETCS) software and safety architecture caused a fatal mishap. This talk outlines key events in the still-ongoing Toyota UA litigation process, and pull together the technical issues that were discovered by NASA and other experts. The results paint a picture that should inform future designers of safety critical software in automobiles and other systems.
Author Bio:
Prof. Philip Koopman has served as a Plaintiff expert witness on numerous cases in Toyota Unintended Acceleration litigation, and testified in the 2013 Bookout trial. Dr. Koopman is a member of the ECE faculty at Carnegie Mellon University, where he has worked in the broad areas of wearable computers, software robustness, embedded networking, dependable embedded computer systems, and autonomous vehicle safety. Previously, he was a submarine officer in the US Navy, an embedded CPU architect for Harris Semiconductor, and an embedded system researcher at United Technologies. He is a senior member of IEEE, senior member of the ACM, and a member of IFIP WG 10.4 on Dependable Computing and Fault Tolerance. He has affiliations with the Carnegie Mellon Institute for Software Research (ISR) and the National Robotics Engineering Center (NREC).
Presentation Date: September 18, 2014.
- A normal modes analysis was performed on a finite element model of a clamping set to determine its vibration mode shapes. The model was imported into HyperMesh and material properties and constraints were applied.
- An eigenvalue extraction was specified to calculate the first 6 modes. The results were viewed in HyperView and showed the component deforming in different patterns for each mode.
An Automated Head Impact Process Setup for Automobile Instrument Panel (IP) A...Altair
Calsonic Kansei developed an automated head impact process using HyperMesh to position virtual head forms on instrument panels for crash simulation. The new process automates tasks that were previously done manually, reducing setup time from 8 hours to 30 minutes. Key benefits include improved efficiency, reduced errors, and the ability for engineers to complete more design projects within time and budget constraints. The automation helps Calsonic Kansei evaluate head impact performance faster and reduce vehicle development costs.
Rotating machinery can be found in every industry: automotive, aerospace, energy, etc. The generated vibration environment is typically made of harmonic tones superimposed on background noise. Components mounted on rotating machinery must be designed to survive such mechanical environment over their entire service life. This presentation will concentrate on calculating the fatigue life from sine-on-random excitations using Finite Element Analysis (FEA). It is proposed to derive the statistical rainflow cycle histogram from a sine-on-random spectrum of stress or strain data and then use the appropriate material fatigue curve to obtain the estimated life. This new analysis is complementary to existing features such as SineDwell, SineSweep and (uni- or multi-axes) random PSD. It is part of extensive research work that includes the influence of sigma clipping or the effects of a high kurtosis.
Speakers
Frédéric Kihm, Application Engineer, HBM-nCode
This document summarizes the capabilities of seat evaluation simulations including frontal/rear impact, seatbelt anchorage tests, child restraint systems, head restraint strength, and other tests. The simulations use standardized procedures and load cases to evaluate compliance with standards. Comprehensive reports are generated identifying critical areas, displacement/stress outputs, and countermeasures. Correlation with physical test results is also provided.
The team designed a suspension system for an SAE Mini Baja vehicle with 11 inches of travel front and rear. The suspension uses a dual A-arm design to allow full travel without interference while maintaining optimal camber. It is adjustable for ride height and stiffness via the pushrod connection on cams. Analysis and modeling showed the design can withstand impacts of 4 feet drops and 2000 pound horizontal impacts on each wheel, with a minimum safety factor of 2.34. The presentation provided details on the final specifications, component designs, analysis results, budget and schedule.
Developing Commercial Vehicles Inspired by NatureAltair
As Germany's largest independent engineering partner to the worldwide automotive industry, EDAG is continuously seeking for new technologies and innovative processes to streamline vehicle development. EDAG has a profound expertise in integrated development and the optimization of vehicles, production facilities, derivatives, and modules. To meet fuel efficiency and emission reduction goals, structurally efficient lightweight designs are demanded in the development of passenger cars and commercial vehicles alike. To fulfill customer demands and to deliver lighter and yet fully functional and validated components in shorter time, EDAG is leveraging its engineering knowledge to combine state-of-the-art computer aided engineering tools, in this case Altair's OptiStruct, with new production technologies such as additive manufacturing. OptiStruct enabled the EDAG engineers to design lightweight and, by being inspired by nature, yet stiff structures of a cabin and a chassis. The components were then manufactured using additive manufacturing methods. To find the optimal solution for the final design the engineers later also conducted multi-physical optimizations, combining strength and crash demands of the vehicle, using an equivalent linear approach. The entire development and manufacturing process for the cabin and chassis structures will be subject of this presentation, showing how a combination of topology optimization and additive manufacturing leads to lighter and stiffer products. The project is a prime example of how mature CAE technology can be adjusted and used in combination with new manufacturing methods to introduce revolutionary structural enhancements within the transportation sector.
Speakers
Andreas Pfeiffer, Development Engineer, EDAG
This document outlines various safety standards for vehicles from different organizations. It includes standards for frontal impact, bumpers, side impact protection, rear impact, headrests, seats, pedestrian protection, steering columns, roof crush resistance, and rollover protection. The standards are from the FMVSS (Federal Motor Vehicle Safety Standards) in the US, ECE (Economic Commission for Europe) regulations, and AIS (Abbreviated Injury Scale) assessments.
Formula 1 cars rely heavily on aerodynamics to produce downforce, using large front and rear wings. They are single-seater, open cockpit vehicles powered by 3.0-liter engines. Teams construct the cars themselves, which use advanced carbon fiber materials and finely tuned fuel blends. Aerodynamic features like the front wings, rear wing, barge boards, and specialized suspensions and wheels are crucial for performance and reducing drag. Major Formula 1 teams include Ferrari, McLaren, Williams, and Renault.
This document outlines the aerodynamic design process for a Formula Student race car. It discusses theory, conceptual wing designs, theoretical lift calculations, and experimental testing. The goal is to prove the benefits of front and rear wings for improving stability, braking, and cornering at low speeds. The design process involves baseline testing, flow visualization, selecting airfoil profiles, sizing wings, and conducting coast down and wind tunnel tests to evaluate downforce. Computational fluid dynamics simulations are also used to analyze pressure and velocity contours. The results will help determine the most effective wing designs for the low speeds of the Formula Student car.
The document discusses diesel emissions regulations and exhaust after-treatment technologies for modern diesel engines. It covers:
1) Changes in US emissions standards over time that have driven new technologies.
2) Key technologies developed to reduce emissions include advanced fuel injection systems, alternative fuels like biodiesel, and exhaust after-treatment devices.
3) Common exhaust after-treatment methods mentioned are diesel particulate filters, NOx adsorber catalysts, selective catalytic reduction using urea injection, and catalyzed diesel particulate filters.
This document discusses engineering design and testing for head impacts. It outlines establishing head impact zones, determining locations that could experience hits, performing head impact tests at impact points with various angles, and evaluating parts to determine if they pass or fail standards.
This document evaluates the effectiveness of virtual validation methods like finite element analysis (FEA) for testing automotive seating systems. It discusses the various challenges in seating system design given the need for comfort, safety and health. A variety of physical and analytical validation tests are described, including head restraint performance, seat anchorage strength, and fatigue resistance testing. The document achieves correlations of 87-92% between physical test and FEA simulation results, demonstrating the effectiveness of virtual validation methods for seating system testing.
DESIGN AND DEVELOPMENT OF A TRANSMISSION SYSTEM FOR AN ALL TERRAIN VEHICLEIAEME Publication
The main function of a transmission system is to transfer the required torque and power generated by the engine to the wheels as and when required by the driver. In automobiles this is done with the help of a gearbox and a final drive alternative. The objective of this work is to design and develop a transmission system which is reliable, safe and cost effective. It should be able to transmit sufficient power and torque to generate the required traction at the wheels at any particular rpm. As the vehicle under consideration is an All-Terrain Vehicle (ATV), which is subjected to varying and rugged road conditions, the power transmission should be constant and uninterrupted.
Aimil is an ISO 9001 certified instrumentation company founded in 1932 with over 750 professionals. It aims to provide optimal quality and customer service through innovation and integration of leading edge global technologies. The company serves various industries including aerospace, automotive, construction, and healthcare. It provides a range of instrumentation products and services for noise, vibration, and harshness measurement and analysis.
“CONCEPT VALIDATION AND DESIGN SYNTHESIS OF CAR DASHBOARD AS PER PLASTIC TRIM...Jayesh Sarode
The evolution of dashboard has led to increased
vehicle occupant comfort and convenience as new systems
become available. The project work aims to develop the work
to apply theoretical and practical tools/techniques to solve
real life problems related to industry and current research in
this project automobile (Car) dashboard upper cover is
selected for a design. For doing so conceptual design tool is
used. I am created a five different concepts using different
benchmarking & brainstorming. Select a best option using
different tools in six sigma like trade off analysis. Threedimensional
CAD software (such as CATIA) is used to develop a
CAD model same as concept. The aim is to achieve the essential
function at the lowest overall cost while maintaining
customers’ optimum value assurance. In this project I try to
develop a such dashboard design which follow a design
guidelines, And analyze and discuss the results to Obtain valid
conclusions which follows a design Standards.
Finite Element Analysis (FEA) is a numerical method for solving complex engineering problems. The document discusses conducting FEA on a fixed-free cantilever beam to study the effect of mesh density on solution accuracy. Analytical solutions are derived and used to validate FEA results. A beam model is created in ABAQUS with varying element sizes. As element count increases, FEA results converge towards analytical solutions, though with increased computation time. An element count of 4125 provided an optimal balance between accuracy and cost.
Modification of airflow around a FSAE Race car using sidepods to increase the...EditorIJAERD
Aerodynamics pertaining to vehicles focuses on improving the drive-ability of the vehicle while also reducing
losses due to air drag. This paper focuses on maximizing the cornering performance of the formula student race car with
slight modifications to the airflow around the vehicle and meagre addition of weight. The undertray produces downloads
by altering the velocity of air flowing underneath it. The sidepods act to reduce flow velocity above the undertray, thus
increasing the pressure above it. This leads to an increased pressure difference over the surface of the undertray which
translates to increase in downforce. The car is able to have a 10% decrease in lap times on a 500m racetrack.
Design for 6θ by Valcon - an introduction to the process and methodsmartinebro
6Theta has proven to be a powerful and successful process for obtaining robust designs that perform consistently at high quality. This presentation gives an introduction to what the process contains and provides simple case examples of how it is used.
The document describes how computer aided engineering was used to rapidly explore 60 variations of a stent design overnight. Using nonlinear optimization techniques, design variables like crown radius, strut length, and thickness were assessed. This identified an optimal design with a 125% stiffer radial strength and lower stresses than the baseline design. Nonlinear finite element analysis of stent deployment was accelerated using parallel computing on a cluster. This represented a significant improvement over traditional single analysis verification of stent designs.
- Enventive Growth is a 10-year-old company that delivers mechanical computer-aided design (MCAD) software focused on tolerance analysis and optimization.
- The company has experienced average annual growth of 70% over the last 7 years and is profitable with no debt.
- Enventive's software allows engineers to conduct tolerance analysis earlier in the design process compared to traditional CAD tools, helping optimize designs for robustness and reduce problems during production.
Performance based Seismic Design of RCC BuildingIRJET Journal
This document presents a study on the performance-based seismic design of a reinforced concrete building. It describes performing pushover analysis on a G+5 building located in seismic zones 3, 4 and 5 of India. The building is designed according to Indian codes IS 1893 and IS 456 for maximum considered earthquake and design based earthquake conditions. Nonlinear static (pushover) analysis is conducted using ETABS to obtain the capacity curve, demand spectrum, and performance point for each zone and earthquake level. Results for storey displacement, drift, and plastic hinge formation are presented and compared. It is concluded that displacement and drift increase with zone but load capacity also increases. The performance-based design approach allows evaluating how the building will actually perform se
Shanghai Automotive - Application of Process Automation and OptimisationAltair ProductDesign
The Application of Process Automation and Optimisation in the Rapid Development of New Passenger Vehicles at SAIC Motors - a Technical Engineering & Analysis Paper from Altair ProductDesign
IRJET-Design Optimization of Free Standing Communication Tower using Genetic ...IRJET Journal
This document describes research optimizing the design of free standing communication towers using a genetic algorithm approach. It summarizes the objectives of minimizing tower weight while satisfying structural constraints. The study develops a genetic algorithm using MATLAB to optimize 7 configurations of communication towers. Results show the genetic algorithm yields a 1-2% reduction in optimal tower weight compared to other optimization methods like particle swarm optimization. The genetic algorithm is validated on benchmark problems and applied to optimize the member sizing of communication towers with different bracing configurations. Overall, the research demonstrates genetic algorithms can effectively optimize structural designs like tower configurations for reduced weight.
Management of Evolving Constraints in a Computerised Engineering Design Envir...Gihan Wikramanayake
1) The document discusses managing evolving constraints in engineering design. Constraints often change during the iterative design process due to changes in requirements, technology, costs, or performance goals.
2) It proposes a framework using Constraint Version Objects (CVOs) to independently capture changing constraints over time without modifying class definitions. Each CVO contains a set of constraints that versions of a design must satisfy.
3) The latest CVO created becomes the default CVO, and new versions are automatically validated against it. This allows different versions to adhere to different constraint sets over the evolution of the design process.
This document provides information about Ambe Engineering, including their expertise in cost, operational and management improvement initiatives primarily for the automotive and heavy truck industries. It details their staff experience and locations. It then outlines their mission to improve profitability through problem solving, resource support, cost reduction, quality improvement, and other initiatives. Several case studies and areas of expertise are described related to warranty analysis, competitive cost analysis, and their problem solving methodology.
A study on six sigma techniques and its application in reduction of seat reje...Hitesh Kothari
This document provides an overview of a study conducted on applying Six Sigma techniques to reduce seat rejection rates at Bosch Ltd. It includes an introduction to Six Sigma that defines key terms like sigma levels and the DMAIC process. It also describes the specific problem of seat rejections in injector and nozzle assemblies. The goals are to identify causes of variations and implement solutions to improve quality. Data will be collected and analyzed to determine root causes and develop improvement plans using DMAIC methodology.
This document summarizes simulation work done to optimize the design of a power liftgate subsystem for an SUV. The optimization aimed to reduce weight while meeting structural requirements. Topology optimization of the inner panel identified critical areas for reinforcement. Gauge optimization of reinforcements and plates reduced weight by 1.5 kg compared to the baseline, while still satisfying requirements for both manual and power liftgate loading conditions. The optimized design merged two reinforcements into one component, saving on manufacturing costs.
Design for reliability (DFR) is an industry-wide practice and a philosophy of considering reliability in an early stage of product design and development, to achieve a highly-reliable product while with sustainable cost. Physical of Failure (PoF) is recognized as a key approach of implementing DFR in a product design and development process. The author will present a case study to illustrate predicting and identifying product failure early in the design phase with the help of a quantitative PoF model based analysis tool.
The document discusses developing well-formed engineering requirements. It outlines that by the end of the chapter, the reader should understand properties of engineering requirements like being abstract, verifiable, unambiguous and traceable. It also discusses developing a complete requirements specification by identifying requirements from customers, environment and technical standards. Advanced analysis methods like tradeoff analysis are examined to refine requirements.
The document provides information about process capability and Cpk. It discusses that:
- Process capability is the ability of a process to make a feature within its tolerance. It is impacted by factors like the process average, standard deviation, and how close they are to specification limits.
- Cpk is a measure used to evaluate process capability. It compares the distance from the process average to the nearest specification limit (A) to 3 times the standard deviation (B). A higher Cpk is better.
- Six Sigma philosophy teaches designing processes and products robustly so there is a safety margin between the process variation and specifications. This improves quality and reduces costs from defects.
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Jaguar Land Rover - Robust Design Optimization of a Knee Bolster
1. Signpost the Future: Simultaneous Robust and
Design Optimization of a Knee Bolster
Tayeb Zeguer
Jaguar Land Rover
W/1/012, Engineering Centre, Abbey Road, Coventry, Warwickshire, CV3 4LF
tzeguer@Jaguar.com
Stuart Bates
Altair ProductDesign
Imperial House, Holly Walk, Royal Leamington Spa, CV32 4JG
Andy.burke@uk.altair.com
www.altairproductdesign.com
copyright Altair Engineering, Inc. 2011
2. www.altairproductdesign.com
Abstract
The future of engineering design optimization is robust design optimization whereby a design
is optimized for real world conditions and not just for one particular set of ideal conditions (i.e.
nominal). There is no practical point trying to get to the peak of a mountain to get the best
view when a slight gust of wind can blow you off, what is practical is to find the highest plateau
where the view is unaffected. The same is true for engineering design, there is no point in
coming up with a design which is optimized for a set of ideal conditions when in reality there
exists uncertainty in the materials, manufacturing and operating conditions.
This paper introduces a practical process to simultaneously optimize the robustness of a
design and its performance i.e. finds the plateau rather than the peak. The process is applied
to two examples, firstly to a composite cantilever beam and then to the design of an
automotive knee bolster system whereby the design is optimized to account for different sized
occupants, impact locations, material variation and manufacturing variation.
Keywords: Optimization, HyperStudy, Stochastic, Uncertainty, LS-DYNA
1.0 Introduction
The competitive nature of the automotive industry demands continual innovation to enable
significant reductions in the design cycle time while satisfying ever increasing design
functionality requirements (e.g. minimising mass, maximising stiffness etc). The challenges
for computer-aided engineering (CAE) to overcome are:
Development cycle must be reduced.
Failure modes have to be found and resolved earlier.
The enablers for CAE are: Faster model creation, CPU, Automation, Material property
Identification, Robustness Optimisation and Validation. The aim of this work is to show that
Altair HyperStudy [1] can be used as powerful CAE enabler to facilitate robust design.
Over the last decade industry has been indoctrinated into the philosophy of manufacturing
quality to six sigma. This paper presents increasing applications of designing systems to
sigma levels of quality. Thus ensuring that designs or numerical models perform within
specified limits of statistical variation.
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DEFINE
CHARACTERIZE
OPTIMIZE
Robust VERIFY
Optimized
Design
Figure 1: Design for Six-Sigma Process
An overview of each stage of the Design for Six Sigma (DFSS) process is given below.
1.1 Define
The first step is to carry out brainstorming to define the system inputs, outputs, controllable
and uncontrollable factors. The Parameter Diagram or p-diagram (Figure 2) is a useful tool for
such a purpose.
DEFINE
CHARACTERIZE
Uncontrollable
OPTIMIZE
Factors
VERIFY
INPUT OUTPUT
Performance DESIGN Performance
Targets
Controllable
Factors
Figure 2: Define – P-Diagram
1.2 Characterize
The characterization phase involves the following :-
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Key parameter identification: identifies the parameters which have the most
significant effect on the performance (output) of the design. This is done typically
through the use of design of experiments (DoE) and statistics (e.g. analysis of variance
ANOVA).
Surrogate Model generation: Typically, in CAE the analysis of a non-linear design
will require simulation times ranging from one hour to a day, making the use of full
analyses for iterative design optimisation computationally expensive and a robustness
assessment requiring hundreds or thousands of Monte Carlo simulations impractical.
To overcome these problems a response surface approximation or surrogate model is
required. This is done using the information generated by the DoE together with
advanced surface-fitting algorithms. The surrogate model gives the value of a key
output variable in the design space, e.g. peak deceleration, as a function of the design
variables. Thousands of simulations of the surrogate model can be run in a few
minutes.
1.3 Optimize
Figure 3 shows a typical design space (response surface) for two design variables. If you
assume there is no variation in the operating and manufacturing conditions then point A is the
optimum solution. However, in reality there are variations in the manufacturing and operating
conditions such that it is very easy to fall off this optimum point (A). A “better” or robust
optimum is point B since the design space is flatter in that region i.e. the performance of the
design is less sensitive to real life variations.
The aim of this optimization phase is to identify the most robust solution in the design space.
• SIMPLE OPTIMUM POINT
• Absolute highest peak ignored due to
sharp gradients surrounding it,
reflecting the non-robust nature of the
solution
• A small change in input (X or Y) will
result in a rapid change in output (Z)
• ROBUST OPTIMUM CLOUD
• Peak B has value lower than Peak A
• The flatter landscape in the region of
the peak results in more robust
solutions in that area
• The output (Z) will not be highly
sensitive to small changes X or Y
Figure 3: Robust Optimum Identification
The process developed here is shown in Figure 4 and consists of the following three stages:
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Stage 1: Assessment & Optimization of the baseline design performance under ideal
conditions (i.e. deterministic optimization). This enables a rapid judgment as to
whether an improved/feasible design exists within the bounds of the design i.e. for
the initial structural layout within the allowable thickness ranges.
Stage 2: Robustness assessment: assessment of the mean and variation in the performance
of a design when subjected to real conditions.
Stage 3: Optimization under real conditions (robustness optimization) – simultaneously
optimize the mean and variation of performance when subjected to real life variations.
Previous studies have performed deterministic optimization followed by robustness
assessments [2]. However, this study presents the first HyperStudy applications of
simultaneous robust optimization.
Baseline
Stage 1
Design Design Assessment &
Optimization – Under Ideal Suitable
Conditions Design ?
Yes
No
Stage 2
Design Assessment – Under
Real Conditions
Stage 3
Robustness Optimization:
Robust
Design Optimization – Under
Optimized
Real Conditions
Design
Figure 4: Simultaneous Robust and Design Optimization Process
1.4 Verify
The staged optimization process (section 1.3) provides invaluable sensitivity data in order to
understand which variables are driving the robustness or optimization of the system. This
inevitably will produce better design. In addition, since a consistent virtual environment is
used for all three stages of this optimization process, a high degree of self checking is
automatically performed.
However, the true verification of the process is the production of the physical design which
exhibits a robust performance in any experimental testing programme and ultimately reduced
warranty claims from the field.
The methodology for generating optimal robust designs that has been developed in this work
is primarily focused on the “optimize” phase of the DFSS loop (Figure 1). It is described
through use of two examples described in Sections 2 & 3. The first is a composite cantilever
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beam, on which the methodology was developed and the second is an industrial example: the
design of a knee bolster system.
2.0 Composite Beam Design
This example is concerned with the minimization of the weight of a cantilevered composite
beam (Figure 5) subjected to a parabolic distributed load (q) with uncontrollable and
controllable factors such as manufacturing or material variation. The DFSS process has been
applied to the problem and is described below.
Figure 5: Composite Beam Subjected to a Parabolic Distributed Load
2.1 Define
Figure 6 shows the p-diagram for the composite beam.
The performance targets for the beam are as follows:
Deflection at the free end of the beam < 1 (normalized).
Maximum bending stress in the beam < 1 (normalized).
Height < 10 times the width (to avoid torsional lateral buckling).
DEFINE
NOISE
CHARACTERIZE •fiber volume fraction ± 0.03
•Young’s modulus of the fiber ± 2%
OPTIMIZE •Young’s modulus of the resin ± 2%
•Density of the fiber ± 2%
VERIFY •Density of the resin ± 2%
•Width ± 0.3mm
•Height ± 0.3mm
INPUT OUTPUT
Deflection & Stress COMPOSITE Max Deflection, Max
Targets BEAM Bending Stress,
Height to width ratio
PARAMETERS
•Beam Height
•Beam Width
•Fibre Volume Fraction
Figure 6: P-Diagram for the Composite Beam
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2.2 Characterize
The key parameters for the beam and their variations are as given in the p-diagram in Figure
6. The analysis of the beam is via an analytical expression, as such there is not a requirement
to replace the analysis with a surrogate model as is the case for the knee bolster analysis in
Section 3.
2.3 Optimize
2.3.1 Stage 1: Design Assessment & Optimization – Under Ideal Conditions
Typically, during an engineering design process once a baseline design has been generated
(e.g. from a topology optimization) it is assessed to determine whether or not it meets the
performance criteria.
The baseline design performance is given in Table 2, it can be seen that the design meets the
targets and has a weight of 4.8N. The next stage is to determine the minimum weight design
which meets the targets.
In order to reduce complexity, ideal conditions are assumed at this stage and optimization is
carried out on perturbations of the initial structural layout and thicknesses. This stage rapidly
provides information as to whether or not an improved/feasible design exists within these
design bounds. The engineer can then make a judgment as to whether or not the design is
suitable for further development and can be taken forward to stage 3 or if a modified baseline
design is required.
The optimization of the beam is set up is as follows:
Objective:
o Minimize Weight
Constraints:
o Maximum deflection at the free end of the beam (normalized) < 1
o Maximum bending stress in the beam (normalized) < 1
o Height to 10 x Width ratio (normalized) < 1 (to avoid torsional lateral buckling)
Design Variables:
o 4mm ≤ Beam Width ≤ 20mm
o 20mm ≤ Beam Height ≤ 50mm
o 0.4 ≤ Fibre Volume Fraction ≤ 0.91
Altair HyperStudy is used for the optimization and the results are shown in Table 1. It can be
seen that, the optimum design (for ideal conditions) meets the targets and represents a 39%
weight reduction over the baseline design.
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Optimum
Baseline Under Ideal
Design variables Min Max design Conditions
width [mm] 4 20 10.0 4.5
height [mm] 20 50 30.0 44.8
Fibre volume fraction 0.4 0.91 0.79 0.52
Objective (min): weight [N] - - 4.82 2.95
Constraints
Normalized stress constraint <=1 - - 1 1
Normalized displacement constraint <=1 - - 1 1
Normalized Height to 10 x width ratio <=1 - - 0.3 1
Table 1: Performance of the Baseline and Optimum Designs Under Ideal Conditions
2.3.2 Stage 2: Design Assessment - Under Real Conditions
At this stage, the design is subjected to variations in the uncontrollable/controllable factors
present in a real system. The mean and variation of the performance is assessed via a
“stochastic study” in HyperStudy. For the beam example the variations imposed on the design
are material and manufacturing tolerances. Note, the variations are assumed to be normally
distributed and ±3σ covers the interval of the tolerance where σ is the standard deviation of
the distribution. Table 2 identifies the tolerances and their assumed variations.
Material related tolerances Variation
fiber volume fraction ± 0.03
Young’s modulus of the fiber ± 2%
Young’s modulus of the resin ± 2%
Density of the fiber ± 2%
Density of the resin ± 2%
Geometric related tolerances
Width ± 0.3mm
Height ± 0.3mm
Table 2: Variations on Manufacturing and Material Tolerances
The mean and variation (σ) in the performance of a design is determined by executing a
10,000 Monte Carlo (MC) simulation run using a random Latin Hypercube DoE (RLH) (Figure
7(a)) on the design with the imposed variations listed in Table 1. Note, a 500 MC simulation
run (Figure 7(b)) was also carried out and the resulting statistics were similar to the 10,000
MC simulation as can be seen in Table 3, therefore for a more computationally expensive
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analysis it can be reasonably assumed that the resulting statistics (using a RLH) will be
practically the same with a reduced number of runs.
(a) 10,000 runs (b) 500 runs
Figure 7: Comparison of Monte Carlo Simulation Run Plots for the Baseline Design
Stochastic Assessment
Mean Standard Deviation
500 runs 10000 runs 500 runs 10000 runs
weight [N] 4.8240 4.8240 0.07300 0.07430
Normalized stress 1.0000 1.0000 0.01200 0.01200
Normalized displacement 1.0000 1.0000 0.04070 0.04060
Normalized height to width ratio 0.3000 0.3000 0.00316 0.00317
Table 3: Comparison of Statistics for the Monte Carlo Simulations on the Baseline
Design
The results of the stochastic studies carried out on the baseline and deterministic optimum
designs are given in Figure 8 and Table 4. Each point on the plots represents a run in the MC
simulation and the resulting “cloud” of points gives the resulting mean and variation in
performance of a particular design. The green circle (Figure 8) represents the boundary of
3σ i.e. 3-sigma design. Hence, if an engineer is aiming for a 3-sigma performance (99.73 %
reliability) then this circle must lie in the feasible region.
It can be seen, that the clouds for both the baseline and deterministic optimum designs are
centred on the point where the stress and displacement = 1 i.e. the mean performance is the
target value of 1, however it can also be seen that approximately 75% of the runs for both
designs are infeasible since their values >1 i.e. the 3σ boundary lies in the infeasible zone.
Note also, that the cloud for the deterministic optimum has a greater scatter than the baseline
i.e. it is less robust since it’s variation in performance is greater. As a result neither design can
be considered as “robust”.
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2.3.3 Stage 3: Simultaneous Robustness Optimization Under Real Conditions
In order for a design to be simultaneously robust and optimized the centre of the performance
cloud (i.e. mean performance) must be as close to the constraint boundaries as possible
whilst ensuring that, for 3-sigma performance, the 3-sigma boundary remains in the feasible
region i.e. 99.73% of the points in the cloud are in the feasible region. Similarly, for 6-sigma
designs the 6-sigma boundary remains in the feasible region.
The robustness optimization of the beam for 3-sigma performance is set up is as follows (note
the mean and σ are calculated as in Stage 2) and carried out using HyperStudy.
Objective:
o Minimize Mean Weight
Constraints:
o σweight ≤ 3σ (assume σweight = 0.1)
o Mean Normalized Stress + 3σ ≤1 (assume σstress= 0.1)
o Mean Normalized Displacement + 3σ ≤ 1 (assume σdisp= 0.1)
o Mean Normalized height to width ratio + 3σ ≤ 1 (assume σh2w= 0.1)
Design Variables:
o 4mm ≤ Beam Width ≤ 20mm
o 20mm ≤ Beam Height ≤ 50mm
o 0.4 ≤ Fibre Volume Fraction ≤ 0.91
where σ is the standard deviation.
The results of the robustness optimization are given in Figure 8 and Table 4. The robust
optimum represents a 29% weight reduction over the baseline design. It can be seen, that the
cloud for the robust optimum design is centred within the feasible stress-displacement region
and the 3σ boundary lies in the feasible zone.
Baseline Deterministic Robust
Optimum Optimum
Indicates 3 sigma
boundary
Figure 8: Results of the Stochastic Studies
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Baseline Deterministic Robust
Design variables Min Max design Optimum Optimum
Mean width [mm] 4.0 20.0 10.0 4.5 4.8
Mean height [mm] 20.0 50.0 30.0 44.8 44.8
Mean Fiber volume fraction v f 0.40 0.91 0.79 0.52 0.57
Objective (min): Mean Weight [N] - - 4.8246 2.9535 3.4474
Constraints
Mean Normalized stress constraint + 3 sigma <=1 - - 1.0361 1.0736 0.9965
Mean Normalized displacement constraint + 3 sigma <=1 - - 1.1234 1.1885 0.9959
Mean Normalized Height to 10 x width ratio + 3 sigma <=1 - - 0.3095 1.0674 0.9952
meets targets
fails targets
Table 4: Performance of Baseline, Deterministic Optimum and Robust Optimum
2.4 Verify
Since all of the performance calculations are carried out using the full analysis of the beam i.e.
an analytical equation, the verification phase is completed at the optimization stage.
3.0 Knee Bolster Study
3.1 Introduction
The aim of this study was to apply the same process as in Section 2 to determine a robust
and optimized design of a knee bolster.
The study has been carried out on a sub-system model of the knee bolster (Figure 9a). The
dynamic finite element analysis code LS-DYNA [3] was used to compute the response of the
system to various design inputs. The objective of the study was to automatically vary various
design variables to optimize the energy absorbing characteristics of the system whilst
satisfying various force and displacement limiting constraints based on federal requirements:
FMVSS 208 [4]; final verification was carried out using full occupant / interior model
simulations using LS-DYNA (Figure 9b).
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Knee
Bolster
(a) Sub-System Model (b) Full Model
Figure 9: LS-DYNA Analysis of the Knee Bolster Design
The Design for Six-Sigma (DFSS) process (Section 1) has been applied to the knee bolster
design as is described in this section.
3.2 Define
The knee bolster system is defined through the p-diagram shown in Figure 10.
DEFINE NOISE
CHARACTERIZE •Material Yield Stress
•Manufactured Thickness
OPTIMIZE •Manufactured Shape
•Impactor type (5th%, 50th%)
VERIFY •Impactor position variation
INPUT
OUTPUT
FMVSS208 Knee Force-displacement
USNCAP
Bolster Pulse
EURONCAP
PARAMETERS
•Thickness
•Shape
•Material Properties
•Impactor position
P-Diagram
Figure 10: P-Diagram for the Knee Bolster System
It can be seen, that the inputs are the legislative targets for the system which are based on the
force-displacement and energy absorption of the knee bolster. Hence the output is the force-
displacement pulse measured from the LS-DYNA simulation. The targets for the knee bolster
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are that the normalized force and displacement values are less than 1. The normalization is
done according to FMVSS 208 [4]. A set of typical force-displacement pulses for the 5th
Left/Right & 50th Left/Right impactors is shown in Figure 11. It can be seen, that this solution
is feasible since the corresponding normalized force and displacement values are less than
one i.e. in the feasible region.
DEFINE
CHARACTERIZE
OPTIMIZE
VERIFY
Feasible
Region
Figure 11: Typical Force-Displacement Output
The thickness and shape parameters are identified in Figure 12. The thickness ranges are
assumed to vary between 1 and 10mm. The shape factor varies between -1 and 1. Figure 13
shows the assumed variation of ±25mm in the centre point of the 5th and 50th impactors.
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DEFINE
CHARACTERIZE
OPTIMIZE
Thickness 1
VERIFY
Thickness 2
PARAMETERS
•Thickness Thickness 3
•Shape
•Material Properties
•Impactor position
Thickness 4
Shape Variable
Note: the thickness and shape variables are the same for each knee bolster
Figure 12: Thickness and Shape Parameters for the Knee Bolster
DEFINE
CHARACTERIZE
OPTIMIZE
VERIFY
PARAMETERS
•Thickness
•Shape
•Material Properties
•Impactor position
(a) 5th Percentile Impactors (b) 50th Percentile Impactors
Figure 13: Impactor Position Variation
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3.3 Characterize
The next stage was to identify the key parameters which have the greatest effect on the knee
bolster performance. This was done using Altair HyperStudy using the following process:
1 Run a DoE with all the parameters
2 Create an approximation of the responses
3 Carry out a statistical analysis of the approximation using Analysis of Variance
(ANOVA)
Figure 14 shows the results of the ANOVA study for the displacement of the 5th left Impactor.
This is typical of the results for the other responses. It can be seen, that the position of the
impactors, the shape and thickness variables and the yield stress contribute the most to the
response. It is assumed that changes to these parameters are sufficient to characterize the
knee bolster system.
DEFINE
CHARACTERIZE
20 ANOVA plot
OPTIMIZE
% Contributions of the Parameters
Contributing %
Impactor position Horizontal
VERIFY to Displacement of 5th Left Impactor
Impactor position vertical
Thickness 1
Thickness 2
Thickness 3
Thickness 4
Yield Stress
Shape
Other less significant parameters
0
Contributing Source
Figure 14: Key Parameter Identification – Typical ANOVA Plot
Following on from this, a response surface of the LS-DYNA analysis was generated for use in
the optimization phase. There are a number of possibilities available in HyperStudy for doing
this. However, the recommended approach (used here) is to carry out a DoE study using the
optimal design filling algorithm – Optimal Latin Hypercube, and then use this data to create a
surrogate model via the moving least squares method. Figure 15 shows a typical response
surface generated for the force response in the 50th left impactor.
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DEFINE
CHARACTERIZE Typical Response Surface
OPTIMIZE
VERIFY
Force 50th Left
Im
pa l
cto ica
rP ert
os nV
it ion s itio
Ho r Po
rizo cto
nta pa
l Im
Figure 15: Typical Response Surface
With the knee bolster system define and characterized the next step is then to optimize the
design.
3.4 Optimize
As described earlier the optimize phase has 3 stages which are shown in Figure 4, these are
described in this section.
3.4.1 Stage 1: Design Assessment & Optimization– Under Ideal Conditions
The first stage is to assess and optimize the design under ideal conditions i.e. no noise is
imposed on the system. Therefore, the only parameters under consideration are the thickness
and shape variables (Figure 12). The response surface generated in the characterization
phase is used for the analysis. The setup is as follows:
Objective:
o Maximize Sum Normalized Energies
Constraints:
o Normalized Force: 1.0
o Normalized Displacement: 1.0
Design Variables (Figure 12):
o 1mm ≤ 4 Thicknesses ≤ 10mm
o -1 ≤ Shape variable Scale Factor ≤ 1
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The optimization is carried out using the gradient-based optimizer in Altair HyperStudy. The
results are given in Table 5 and Figure 16, it can be seen that for ideal conditions the solution
meets the performance targets. The question arises at this point: how does this solution
behave in reality? This is addressed in the next section.
Design Optimized for IDEAL
Design variables Min. Max conditions
Shape Variable -1.0 1.0 0.2
Thickness 1 [mm] 1.0 10.0 3.4
Thickness 2 [mm] 1.0 10.0 4.5
Thickness 3 [mm] 1.0 10.0 5.0
Thickness 4 [mm] 1.0 10.0 5.7
Objective (max): Sum of Normalized Energy - - 0.983
Constraints 5th 50th
left right left right
Normalized displacement constraint <=1 - - 0.70 0.84 0.78 0.77
Normalized force <=1 - - 1.00 0.97 0.91 0.89
Table 5: Assessment of the Design Optimized for Ideal Conditions
DEFINE 5th Left 5th Right
CHARACTERIZE
OPTIMIZE
VERIFY
50th Left 50th Right
Figure 16: Assessment of the Design Optimized for Ideal Conditions
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3.4.2 Stage 2: Design Assessment - Under Real Conditions
In order to assess real life performance a robustness assessment (stochastic study) of the
design “optimized for ideal conditions” is carried out. This is done with a Monte Carlo
simulation carried out on the response surface, here a 500 run random Latin Hypercube is
used. The parameters and assumed real life variations imposed on the system are identified
in Table 6. Note, the following assumptions have been made: the variations are normally
distributed and ±3σ covers the interval of the tolerance where σ is the standard deviation of
the distribution.
Material related tolerances Variation
Yield Stress ± 10%
Geometric related tolerances
Thickness ± 0.1mm
Shape Scale Factor ± 0.01
Impactor position variation
Position ± 25mm
Table 6: Knee Bolster Noise Parameters and Variations
The results of the robustness assessment performed on the design optimized under ideal
conditions are shown in Figure 17. It can be seen from the resulting “performance clouds” that
there are a large number of solutions which fail the force performance targets and the design
is considered non-robust.
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DEFINE 5th Left 5th Right
CHARACTERIZE
OPTIMIZE
VERIFY
50th Left 50th Right
Figure 17: Design Optimized for Ideal Conditions - Robustness Assessment
3.4.3 Stage 3: Robustness Optimization: Design Optimization – Under Real Conditions
At this stage the robustness assessment is incorporated in the optimization loop. The output
from the robustness assessment used in the optimization loop is the mean and standard
deviation of the responses. The optimization is set up as follows:
Objective:
o Maximize Mean of the Summed Normalized Energies
Constraints:
o Normalized Displacement: Mean + 3σ 1.0
o Normalized Force: Mean + 3σ 1.0
Design Variables (Figure 12):
o 1mm ≤ 4 Thicknesses ≤ 10mm
o -1 ≤ Shape variable Scale Factor ≤ 1
The results of the simultaneous robustness and design optimization are shown in Figure 18. It
can be seen the “performance clouds” have been shifted into the feasible region, Although
there are a small number of solutions which fail the performance targets, the design is
considered as robust as possible for the current knee bolster structural layout.
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DEFINE 5th Left 5th Right
CHARACTERIZE
OPTIMIZE
VERIFY
50th Left 50th Right
Figure 18: Design Optimized for Real Conditions - Robustness Assessment
3.5 Verify
At this stage of the DFSS process significant information about the performance of the knee
bolster has been generated. The next step is then to “plug” the design back into the full
vehicle model which has been concurrently updated with other optimized components of the
car.
It is a design challenge to produce a virtual design that can achieve the constraint targets
within ±3σ due to the conservative nature of this numerical test environment (e.g. totally rigid
backing structure, conservative impact velocity etc.). This technology can be efficiently used
to determine the most efficient design for the specified design variations.
The design determined by this process is similar to a production component used on a recent
vehicle. However, this design was achieved in a fraction of the design time with an increased
understanding of the performance drivers.
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4.0 Conclusions
The future of engineering design optimization is robust design optimization whereby a design
is optimized for real world conditions and not just for one particular set of ideal conditions.
There is no point in coming up with a design which is optimized for a set of ideal conditions
when in reality there exists uncertainty in the materials, manufacturing and operating
conditions.
Altair HyperStudy has been used to simultaneously optimize the robustness and performance
of a real world component (i.e. automotive knee bolster). The resulting design was similar to
an existing production component. However, this design was achieved in a fraction of the
design time with an increased understanding of the performance drivers. A unique process
has been developed which is computationally efficient for complex non-linear systems. This
process can be further enhanced and automated. The study has shown that Altair HyperStudy
can be used as a key CAE enabler.
Achieving robust design is inherent in the quality philosophy of many companies. It will
become an increasing requirement to demonstrate that digital designs achieve the required
quality levels. This will initially be achieved on a component level and gradually migrate to
complex systems. The initial requirement will be to understand the probabilistic variation of
various parameters. This will require an increasing amount of measurement and an increased
understanding of the physical drives of the component / system. Robustness can only be
achieved by understanding the variation of the various factors.
Adding noise factors during optimisation is the best way in obtaining a robust solution the use
of DFSS principle helps identify failure modes and eliminate them earlier in the design
process.
For certain parameters, suppliers are already instructed to deliver product within specific
sigma quality levels. This technology can identify parameters which drive the quality and help
develop guidelines to control the variation of these quantities. This control will be
accompanied by an associated cost penalty.
Increased availability of inexpensive powerful computing and improvements to software
integration and the predictive algorithms heralds the new development of producing digital
designs to sigma levels of quality.
5.0 References
[1] ‘Altair HyperStudy 8.0’ Altair Engineering Inc. (2006).
[2] ‘Design Optimization and Probabilistic Assessment of a Vented Airbag Landing
System for the ExoMars Space Mission’, Richard Slade and Andrew Kiley, 5th Altair
UK Technology Conf., April 2007.
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[3] 'LS-DYNA Version 970’, Livermore Software Technologies Corporation, LSTC
Technical Support, 2006.
[4] ‘FMVSS 208 – Occupant Crash Protection’, Federal Motor Vehicle Safety Standards
and Regulations.
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