Fused deposition modelling (FDM) has become a prevalent technique to additively manufacture polymer that can provide design freedom and creativity. However, like any other AM technologies, FDM has its own challenges. The inherent void, surface roughness and dimensional accuracy during manufacturing can lead to tolerance rejection, cracking or failure during the product life. Also, the resultant anisotropic material properties are inherent in the layer-by-layer manufacturing features. Therefore, it is important to understand and simulate FDM process, in which complex thermo-mechanical interaction takes place due to rapid heating/cooling.
The current work will first provide a brief overview of the current polymer AM simulation solutions, validation protocols, and standardization efforts. Secondly, a simulation framework using Abaqus will be presented to replicate the FDM process that can provide insight into how the process parameters affect product quality. Currently, simulation solutions has the capability to model the polymer extrusion process with that can capture the layer-by-layer element activation feature and varying free surface in heating and cooling. Finally, an example study will be presented to show how modifying process parameters such as raster angle, contour width and layer thickness affect the transient thermal, deformation and residual stress field. Accordingly, optimal processing parameters can be identified based on the criterion of minimizing distortion and residual stresses.
With the advent of efficient and robust numerical analysis techniques such as finite element analysis (FEA) and the increased computing power of today’s hardware solutions, it has become practical to simulate thermo-mechanical behaviors of plastics and rubbers that can capture the physical reality of such materials with high fidelity. Replicating physical behavior of highly complex and non-linear materials such as plastics have positioned FEA tools to create life like models that will behave like the real part or product. Such simulation can compare very well with the physical test. Therefore, using FEA techniques one can perform virtual testing to design plastic products and also can use simulation techniques to optimize design based on a mathematically robust approach instead of heuristic, experience based approach only. Using FEA one can address the needs of the product development lifecycle from concept through detailed design capturing realistic simulation of underlying complex physics.
Simulation can help in both design and process optimization for additive manufacturing industry by getting the product right the first time. Cost saving by reducing print iterations can be tremendous. The presentation covers some overview of the AM industry and specifically discusses both metal and polymer AM simulation solutions.
VIAS undertook a design optimization study to optimize the design of a modular steel-plate composite (SC) primary shield wall (PSW). This PSW is used for support and protection of nucear reactor pressure vessel. Using initial design variables and non-linear material models, 3D FEA model was simulated. Using process automation, Response Surface method was implemented by automating 35 FEA runs. This study resulted in optimized dimensions for the PSW.
We are a Houston based Dassault Systemes partner providing software solutions, training and Finite Elements Analysis(FEA) / Computational fluid dynamics(CFD) based consulting services.
With the advent of efficient and robust numerical analysis techniques such as finite element analysis (FEA) and the increased computing power of today’s hardware solutions, it has become practical to simulate thermo-mechanical behaviors of plastics and rubbers that can capture the physical reality of such materials with high fidelity. Replicating physical behavior of highly complex and non-linear materials such as plastics have positioned FEA tools to create life like models that will behave like the real part or product. Such simulation can compare very well with the physical test. Therefore, using FEA techniques one can perform virtual testing to design plastic products and also can use simulation techniques to optimize design based on a mathematically robust approach instead of heuristic, experience based approach only. Using FEA one can address the needs of the product development lifecycle from concept through detailed design capturing realistic simulation of underlying complex physics.
Simulation can help in both design and process optimization for additive manufacturing industry by getting the product right the first time. Cost saving by reducing print iterations can be tremendous. The presentation covers some overview of the AM industry and specifically discusses both metal and polymer AM simulation solutions.
VIAS undertook a design optimization study to optimize the design of a modular steel-plate composite (SC) primary shield wall (PSW). This PSW is used for support and protection of nucear reactor pressure vessel. Using initial design variables and non-linear material models, 3D FEA model was simulated. Using process automation, Response Surface method was implemented by automating 35 FEA runs. This study resulted in optimized dimensions for the PSW.
We are a Houston based Dassault Systemes partner providing software solutions, training and Finite Elements Analysis(FEA) / Computational fluid dynamics(CFD) based consulting services.
Additive manufacturing (AM) or 3D printing is maturing rapidly as a viable solution of make optimized parts for “real engineering” applications. The freedom of design that is achievable using AM process is un parallel in terms of reducing structural weight, reducing material cost, generating complex shapes and connections and introducing directional properties in a component. However, understanding of AM process and utilizing process parameters to optimize a design comes with many challenges. Currently, one of the emphasize is to use physics based realistic simulation to replicate the AM process numerically and relate process parameters to the concept of functional generative design that relates design with manufacturing process.
Current work, through a typical build example, discusses an integrated numerical solution on a digital platform that involves the following.
Generative Design involving topology optimization that creates parts in context of the manufacturing process and automatically generate variants of conceptual and detailed organic shapes that helps make informed business decisions based on physics-based analytic tools. Process planning that defines and customizes manufacturing environment including nesting parts automatically on the build tray, designing and generating optimal support structures, and creating machine specific slicing and scan path which is ready for print. Process simulation that automatically includes machine inputs for energy, material and supports into the simulation at layer, part and build levels for any additive manufacturing process and accurately predicts part distortions, residual stresses and as-built material behavior. Finally, the platform involves post processing to perform shape optimization where simulation is used to guide support-structure strategy for enhanced build yield, compensate distortion effects without the need to redesign the product tooling, produce high-quality morphed surface geometry with unchanged topology, and perform final in-service performance validations of manufactured part.
Overview of the Exascale Additive Manufacturing Projectinside-BigData.com
In this video from the HPC User Forum in Santa Fe, John Turner from ORNL presents: Overview of the Exascale Additive Manufacturing Project.
"Fully exploiting future exascale architectures will require a rethinking of the algorithms used in the large scale applications that advance many science areas vital to DOE and NNSA, such as global climate modeling, turbulent combustion in internal combustion engines, nuclear reactor modeling, additive manufacturing, subsurface flow, and national security applications. The newly established Center for Efficient Exascale Discretizations (CEED) in DOE’s Exascale Computing Project (ECP) aims to help these DOE/NNSA applications to take full advantage of exascale hardware by using state-of-the-art ‘high-order discretizations’ that provide an order of magnitude performance improvement over traditional methods."
Watch the video: http://wp.me/p3RLHQ-gHb
Learn more: https://exascaleproject.org/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Metal Additive Manufacturing - Basics Zero to One - June 2018bMatthew Burris
A brief on metal additive manufacturing. Covering the hype, realities, industry growth, where companies have found value with metal additive manufacturing, the value levers of metal additive manufacturing with case studies, and considerations of adopting metal additive manufacturing.
Is Additive Metal Manufacturing the Next Technological Wonder Drug? An article in Canadian Metalworking Magazine reviewing AMM's success with their two (2) EOS Model M290 e-Manufacturing DMLS Systems.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
Peter Zimm - MRO WORKSHOP - SPOTLIGHT: Additive manufacturing (3D printing) is expected to have a profound impact on global supply chains, including in the aviation industry. What does 3D printing mean for the future of manufacturers and MROs?
Please find our presentation for the SPE ABC 2017: FEA based realistic simulation for packaging qualification.
Please do not hesitate to contact us if you would like to discuss any particular topic in detail.
Additive manufacturing (AM) or 3D printing is maturing rapidly as a viable solution of make optimized parts for “real engineering” applications. The freedom of design that is achievable using AM process is un parallel in terms of reducing structural weight, reducing material cost, generating complex shapes and connections and introducing directional properties in a component. However, understanding of AM process and utilizing process parameters to optimize a design comes with many challenges. Currently, one of the emphasize is to use physics based realistic simulation to replicate the AM process numerically and relate process parameters to the concept of functional generative design that relates design with manufacturing process.
Current work, through a typical build example, discusses an integrated numerical solution on a digital platform that involves the following.
Generative Design involving topology optimization that creates parts in context of the manufacturing process and automatically generate variants of conceptual and detailed organic shapes that helps make informed business decisions based on physics-based analytic tools. Process planning that defines and customizes manufacturing environment including nesting parts automatically on the build tray, designing and generating optimal support structures, and creating machine specific slicing and scan path which is ready for print. Process simulation that automatically includes machine inputs for energy, material and supports into the simulation at layer, part and build levels for any additive manufacturing process and accurately predicts part distortions, residual stresses and as-built material behavior. Finally, the platform involves post processing to perform shape optimization where simulation is used to guide support-structure strategy for enhanced build yield, compensate distortion effects without the need to redesign the product tooling, produce high-quality morphed surface geometry with unchanged topology, and perform final in-service performance validations of manufactured part.
Overview of the Exascale Additive Manufacturing Projectinside-BigData.com
In this video from the HPC User Forum in Santa Fe, John Turner from ORNL presents: Overview of the Exascale Additive Manufacturing Project.
"Fully exploiting future exascale architectures will require a rethinking of the algorithms used in the large scale applications that advance many science areas vital to DOE and NNSA, such as global climate modeling, turbulent combustion in internal combustion engines, nuclear reactor modeling, additive manufacturing, subsurface flow, and national security applications. The newly established Center for Efficient Exascale Discretizations (CEED) in DOE’s Exascale Computing Project (ECP) aims to help these DOE/NNSA applications to take full advantage of exascale hardware by using state-of-the-art ‘high-order discretizations’ that provide an order of magnitude performance improvement over traditional methods."
Watch the video: http://wp.me/p3RLHQ-gHb
Learn more: https://exascaleproject.org/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Metal Additive Manufacturing - Basics Zero to One - June 2018bMatthew Burris
A brief on metal additive manufacturing. Covering the hype, realities, industry growth, where companies have found value with metal additive manufacturing, the value levers of metal additive manufacturing with case studies, and considerations of adopting metal additive manufacturing.
Is Additive Metal Manufacturing the Next Technological Wonder Drug? An article in Canadian Metalworking Magazine reviewing AMM's success with their two (2) EOS Model M290 e-Manufacturing DMLS Systems.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
Peter Zimm - MRO WORKSHOP - SPOTLIGHT: Additive manufacturing (3D printing) is expected to have a profound impact on global supply chains, including in the aviation industry. What does 3D printing mean for the future of manufacturers and MROs?
Please find our presentation for the SPE ABC 2017: FEA based realistic simulation for packaging qualification.
Please do not hesitate to contact us if you would like to discuss any particular topic in detail.
The current work focuses on simulation based optimization of a complex, safety critical component where it is prohibitively expensive to carry out finite element analysis (FEA) simulations for all possible sample realizations and therefore requires statistical or machine learning techniques for a timely yet accurate solution. The applicability of machine learning further brings the opportunity of performing in-service monitoring using sensor data and thereby performing predictive maintenance.
Riccardo Bianco
Topology optimization - Altair suite
tecnologia, scenari e scelte strategiche per la transizione digitale dell'industria manifatturiera
Today's fast paced product market has shorter lifecycles and tighter budgetary concerns. Tolerance analysis software provides an ideal solution to reduce the number of crucial steps needed to optimize a product at the design step itself. 3DCS Variation Analyst is the world's most used tolerance analysis software that is fully integrated into NX/ CATIA V5/ Creo and CAD Neutral Multi-CAD. 3DCS Variation Analyst is designed to use a consistent format and set of mathematical formulae that create reliable results, enabling engineers to gain a complete insight into their design. The software empowers design engineers to control variation and optimize their designs to account for inherent process and part variation, which in turn reduces non-conformance, scrap, rework and other associated costs.
3DCS Variation Analyst
Used by the world’s leading manufacturing OEM’s to reduce the cost of quality, 3DCS Variation Analyst comes in two flavours:
1) 3DCS Variation Analyst (NX / CAA V5 or Creo Based) is an integrated solution for NX / CATIA V5 or Creo. Since it is an integrated solution, users can not only activate 3DCS workbenches from within the modelling solution, they can use many of its inbuilt functionality to support their modelling.
3DCS Variation Analyst provides three analysis methods:
Monte Carlo Analysis
High-Low-Mean (Sensitivity Analysis) and
Geofactor Analysis (Relationship)
Cutting Steelmaking Costs Without Sacrificing Quality. Machine Learning for M...Yandex Data Factory
For further information about Yandex Data Factory solutions,
please contact us at ydf-customer@yandex-team.com
Metallurgy companies must balance two competing demands: keeping production costs to a minimum while still ensuring that the resulting steel composition complies with all requirements. Given how difficult this balance is to strike, you might find it hard to believe that metallurgy companies can actually achieve 5% cost optimisation with no investments in expensive equipment and software. But that is exactly what Magnitogorsk Iron and Steel Works managed to do with the help of Yandex Data Factory’s machine learning technology.
You’ll learn how implementing Yandex Data Factory’s ferroalloy optimisation service has resulted in projected savings of more than $4m a year for Magnitogorsk Iron and Steel Works. We also discuss other advantages these new technologies bring to metallurgy and give practical advice on how to get started with your first machine learning and big data analytics project so that your company can also cut costs while maintaining the same high quality of resultant steel.
Design Optimization for Additive Manufacturing - Webinar - June 28th
With the widespread use of Additive Manufacturing, a new generation of design concepts are now possible to economically produce that weren’t feasible with traditional manufacturing methods. Design Engineers can now quickly optimize designs for 3D Printing, using the new Generative Shape Designer role in the 3DEXPERIENCE platform. This new role allows designers to create optimized designs in a fraction of the time possible with current tools, while using the powerful Abaqus™ solver. See how this exciting new technology can transform your design process!
3DCS Dimensional Variation Analysis Integrated in Siemens NX CADBenjamin Reese
3DCS for NX gives outputs based on part and process variation. These can be input in a variety of ways, from CAD based PMI to selecting from applicable feature or point based options in 3DCS. The final results are shown as statistical (Monte Carlo) and mathematics (GeoFactor) based outputs with toggle-able metrics like Cpk, Ppk, ranges, percent out of specification and a variety more.
What helps 3DCS for NX stand out is its connection to Siemen's Teamcenter PLM system. Not only is 3DCS for NX integrated into NX CAD, but it in turn is integrated with Teamcenter. The 3DCS analysis data is stored in the NX CAD model, meaning that any place the model is stored or managed takes the 3DCS data along with it. This makes it easy to store your model and 3DCS data in Teamcenter, handling both version control and data security.
Learn more at https://www.3dcs.com/tolerance-analysis-software-and-spc-systems/3dcs-software/siemens-nx-integrated
SSE Practices Overview covering systems engineering, embedded software development, DO178B/C, ISO 26262, IEC62304, and including some short exercises on practice customization
IBM ALM for aviation safety compliance aerospaceImran Hashmi
Check out more info at https://hashmi.ca
Challenges in Aviation Engineering
IBM Engineering platform for Aerospace/Defense
Engineering Lifecycle Management Solution for A&D capabilities
Deeper Dive: Accelerating Industry Compliance for Aerospace:
ARP4754 and DO178C
Summary and additional resources
CATIA Integrated Tolerance Analysis - 3DCS for CATIA V5Benjamin Reese
3DCS Variation Analyst CAA V5 Based Software is used by manufacturers across the globe for Tolerance Analysis to reduce scrap, rework and warranty claims.
3DCS Variation Analyst CAA V5 Based (3DCS for V5) is an integrated software solution in CATIA V5 that simulates product assembly and part tolerance 3D stack-ups through Monte Carlo Analysis and High-Low-Mean (Sensitivity) Analysis.
Model Part and Process Variation - How does it work?
Use CATIA FTA - Embedded GD&T3DCS for V5 uses three methods of simulation; Monte Carlo Simulation, High-Low-Mean (Sensitivity analysis) and GeoFactor Analysis. These together highlight the sources of variation as well as potential build issues in the product.
By accurately modeling the build process, users can determine how their process will affect the assembly in addition to their part tolerance stack-up. This together essentially creates a virtual prototype that can be used to make decisions about design changes and tooling while reducing scrap and rework.
Learn more at: http://www.3dcs.com/tolerance-analysis-software-and-spc-systems/3dcs-software/catia-v5-integrated
Bringing 15+ years of experience in 3D modeling in the life sciences and semiconductor industry and 25+ years working in high tech engineering. Background includes extensive experience in documentation, mechanical design engineering and drafting, quality control and internal and external audits as required by the FDA, ISO and RoHS product safety initiative.
Similar to Simulation of transient temperature and stress field in the polymer extrusion additive manufacturing processes (20)
Process of Integration the Laser Scan Data into FEA Model and Level 3 Fitness-for-Service Assessment of Critical Assets in Refinery & Process Industries
From customer value engagements to hands-on production support, our Services span across every stage of our customers digital transformation journey, to help ensure that every customer is successful in their adoption of our solutions.
• Implementation, Upgrade, Migration, and Maintenance Services
• On-Premises and On-Cloud
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The permeation on thin walls is a mass diffusion problem in a long-term time domain.
The application of mass diffusion analysis is limited to continuum modeling, which is not feasible to be used for thin walls, where shell elements are needed.
However, the analogy with the heat transfer governing equations allows the FE analyst to simulate the physics of the permeation problem to overcome this limitation with the proper mathematical modeling.
The objective is to develop a methodology using finite element analysis (FEA) for the shelf life of a beverage based on permeation of CO2 through a Polyethylene Terephthalate (PET) bottle wall.
To find out how we can help you with your ROI using modeling and simulation, please contact us and out consulting division can arrange a 30 minute complimentary call.
Lithium-ion batteries are currently used in EVs due to their fast-charging rate enabled by high energy density and cell voltage.
The high energy stored in EV battery packs translates to a higher probability of fire in the battery compartment due to an automotive crash. Crash test of the battery pack is typically performed to verify the safety performance of the battery under the specified loading conditions.
The work presents a virtual crash test approach to comply with regulations while designing a battery pack.
Contact us for more details on how we can help with our consulting based approach.
Digital twins represent the shape of physical objects in 3D.
A virtual twin experience starts with designing a 3D model that represents the shape, dimensions and properties of a physical product or system. Simulations are run on that virtual model to explore how the product will behave when assembled, operated or subjected to a range of events.
The presentation elaborates through practical examples and software solutions how modeling and simulation can aid in having better products to market faster in the era of digitalization.
If you have further interest contact us to know more and see how we can help with a consulting based approach.
The aim of this study is to create a FEA model to make a relative comparison between two implant tray materials (Co-Cr-Mo and Ti-Al) at the tibia-implant interface under the constant loading condition with the help of patient-specific bone microstructure using a representative volume element (RVE).
Using the study,
Was able to accurately evaluate designs under different conditions leading to more tailored, patient-specific implants.
Using numerical modeling, it was possible to improve product performance by comparing various design options.
Able to reduce the number of material testing and lead time reduction.
The presentation describes how to integrate Laser Scan Data into FEA Model and Perform Level 3 Fitness-for-Service Assessment of Critical Assets in Refinery & Process Industries. It also, talks about an engineer friendly plugin that helps in the data import with insights from the asset owners and FEA consultants.
Finite Element Analysis is used to simulate crushing of an automotive battery pack as per ISO 12405-3:2014 standard and crashworthiness performance of the battery pack was performed to optimize the structure.
VIAS conducted a recent study involving the design qualification an Earthquake-Resistant Ductile Iron Pipe (ERDIP) joint against seismic events using simulation. Validation of the FEA model was performed using physical test data for a small diameter pipe joint. Further analysis was carried out wherein the pipe-soil interactions and the pipe-joint behaviors were represented by sets of non-linear springs. A stretch of the pipeline consisting of many pipe joints was undertaken to ascertain integrity under seismic fault movement conditions. The pipeline was acted upon by a fault rupture at specified azimuth angles and fault length. The simulation predicted that the pipe joints would meet the design criteria if the fault azimuth angle was within a certain threshold.
The following presentation describes the use of numerical simulation to optimize the shape, elasticity and volume of the compensation chamber in an axial pump while minimizing pressure peaks.
Presentation shows through a numerical example of a BOP model how to optimize a critical subsea component using the SIMULIA Power of Portfolio components for fatigue (fe-safe) and reliability (Isight).
Abaqus Knee Simulator (AKS) is an automated modeling tool for building advanced knee implant simulations based on a validated framework.The current work describes the workflows with various aspects of knee implant design evaluation.
More from Arindam Chakraborty, Ph.D., P.E. (CA, TX) (20)
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Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Simulation of transient temperature and stress field in the polymer extrusion additive manufacturing processes
1. Simulation of Transient Temperature and
Stress Field in The Polymer Extrusion Additive
Manufacturing Processes
Ellie Ai Vineyard, PhD, Arindam Chakraborty, PhD, PE
Virtual Integrated Analytics Solutions (VIAS)
www.viascorp.com
Jan 18, 2018