This document describes the application of artificial neural networks to model the mechanical properties of structural steels after heat treatment. Specifically:
1. Artificial neural networks were used to predict mechanical properties like strength, impact resistance, and hardness based on input parameters like chemical composition, heat treatment type and parameters, and element geometry.
2. The neural networks were trained on a data set of over 14,212 samples of different structural steel compositions and heat treatments.
3. The results showed that the neural networks were able to accurately predict the mechanical properties for steels within the ranges of the input parameters, with correlation coefficients over 90% and low standard deviation, indicating good ability to generalize to new data.
Angshuman Ghosh has extensive experience in mechanical engineering and metal forming processes. He received his PhD from North Carolina State University in Mechanical Engineering, with a focus on temperature prediction in ultrasonic microextrusion. He has worked as a research assistant at NCSU since 2007, conducting research in areas such as tube hydroforming loading path prediction, tribology testing for metal forming lubricants, and press forming simulation. Ghosh has published several papers in these areas and has skills in finite element analysis, experimental design, and programming languages relevant to mechanical engineering.
The document presents a study that aims to optimize welding variables and predict weld bead geometry in gas metal arc welding (GMAW) process. Experiments were conducted using a central composite design to study the relationship between input process parameters (current, speed, angle, distance, pinch) and output parameters (bead width, height, depth, dilution). Mathematical models were developed using regression analysis. A neural network was then used to predict welding outputs, and a simulated annealing algorithm was applied to optimize the process parameters to achieve optimum dilution.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Identification of material constitutive laws for machining 1macrolf
1. The document presents a new analytical model called the distributed primary zone deformation (DPZD) model to describe the distributions of effective stress, strain, strain rate, and temperature in the primary shear zone during orthogonal metal cutting.
2. Finite element simulations are used to characterize the plastic deformation fields in the primary shear zone and develop the DPZD model, which overcomes limitations of previous average value models.
3. The DPZD model is assessed by comparing its predictions to finite element simulation results for a variety of cutting conditions and materials.
This paper discuss of literature review of EDM and ANNOVA Technology used in on CNC. Today CNC technology has major contribution in i ndustries. CNC machines are main platform in the contribution of good quality products in industries. Basically CNC machines are automated operating machines which are based on code letters,numbers and special characters. The numerical data required for manufacturin g a part provided by machine is called CNC (Computer Numerical Controlled). To remain successful in today�s competitive market,the manufacturers should rely on their Engineer s and production professionals for quick and effective manufacturing setup and also to achie ve Quality products. The machining process on a CNC Milling is programmed by speed,feed rate,and cutting depth,which are frequently determined based on the job s hop experiences However,the machine performance and the product characteristics are not guaranteed to be acceptable.
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...IJAEMSJORNAL
The document describes an experiment to optimize cutting rate (CR) when machining EN 1010 low alloy steel using wire electric discharge machining (WEDM). Central composite design was used to plan experiments varying pulse on time, pulse off time, peak current, and servo voltage. 21 experiments were conducted and the average CR was measured for each. Response surface methodology was applied to develop a model relating the process parameters to CR. Analysis of variance showed the quadratic model adequately described the relationship with an R2 of 0.970641. An empirical equation was derived to predict optimal CR based on settings of the four process parameters.
Analysis of Machining Characteristics of Cryogenically Treated Die Steels Usi...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Angshuman Ghosh has extensive experience in mechanical engineering and metal forming processes. He received his PhD from North Carolina State University in Mechanical Engineering, with a focus on temperature prediction in ultrasonic microextrusion. He has worked as a research assistant at NCSU since 2007, conducting research in areas such as tube hydroforming loading path prediction, tribology testing for metal forming lubricants, and press forming simulation. Ghosh has published several papers in these areas and has skills in finite element analysis, experimental design, and programming languages relevant to mechanical engineering.
The document presents a study that aims to optimize welding variables and predict weld bead geometry in gas metal arc welding (GMAW) process. Experiments were conducted using a central composite design to study the relationship between input process parameters (current, speed, angle, distance, pinch) and output parameters (bead width, height, depth, dilution). Mathematical models were developed using regression analysis. A neural network was then used to predict welding outputs, and a simulated annealing algorithm was applied to optimize the process parameters to achieve optimum dilution.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Identification of material constitutive laws for machining 1macrolf
1. The document presents a new analytical model called the distributed primary zone deformation (DPZD) model to describe the distributions of effective stress, strain, strain rate, and temperature in the primary shear zone during orthogonal metal cutting.
2. Finite element simulations are used to characterize the plastic deformation fields in the primary shear zone and develop the DPZD model, which overcomes limitations of previous average value models.
3. The DPZD model is assessed by comparing its predictions to finite element simulation results for a variety of cutting conditions and materials.
This paper discuss of literature review of EDM and ANNOVA Technology used in on CNC. Today CNC technology has major contribution in i ndustries. CNC machines are main platform in the contribution of good quality products in industries. Basically CNC machines are automated operating machines which are based on code letters,numbers and special characters. The numerical data required for manufacturin g a part provided by machine is called CNC (Computer Numerical Controlled). To remain successful in today�s competitive market,the manufacturers should rely on their Engineer s and production professionals for quick and effective manufacturing setup and also to achie ve Quality products. The machining process on a CNC Milling is programmed by speed,feed rate,and cutting depth,which are frequently determined based on the job s hop experiences However,the machine performance and the product characteristics are not guaranteed to be acceptable.
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...IJAEMSJORNAL
The document describes an experiment to optimize cutting rate (CR) when machining EN 1010 low alloy steel using wire electric discharge machining (WEDM). Central composite design was used to plan experiments varying pulse on time, pulse off time, peak current, and servo voltage. 21 experiments were conducted and the average CR was measured for each. Response surface methodology was applied to develop a model relating the process parameters to CR. Analysis of variance showed the quadratic model adequately described the relationship with an R2 of 0.970641. An empirical equation was derived to predict optimal CR based on settings of the four process parameters.
Analysis of Machining Characteristics of Cryogenically Treated Die Steels Usi...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This document summarizes a study on using simulated annealing algorithm to predict weld bead geometry in gas metal arc welding. Key points:
1. The study develops mathematical models using multiple regression to predict four dimensions of weld bead geometry based on welding process parameters.
2. Experiments are conducted using a central composite design with five levels and factors including current, welding speed, gun angle, contact tip distance, and pinch.
3. The developed models are checked for adequacy and significance. Bead geometry is then predicted again using a simulated annealing algorithm for comparison.
IRJET- Advance Manufacturing Processes Review Part IV: Electrical Dischar...IRJET Journal
1) Electrical discharge machining (EDM) is a non-traditional machining process that uses electric sparks to erode conductive materials.
2) EDM can machine hard materials like steel and super alloys regardless of their hardness by melting and vaporizing small amounts of material with each electric spark.
3) The document reviews research on EDM process parameters and their effects on material removal rate, surface roughness, and tool wear. It also summarizes recent research trends in EDM like ultrasonic vibration-assisted EDM and EDM in water.
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...IRJET Journal
This document analyzes the optimization of process parameters for wire electrical discharge machining (WEDM) of stainless steel 316L. It conducts experiments using different controllable factors like discharge current, pulse on/off time, and arc gap. Taguchi design of experiments is used to optimize for maximum material removal rate. Experiments are conducted using a copper tool on 316L stainless steel workpieces. The results are analyzed to determine the optimum parameter levels for achieving higher material removal rate and better surface finish quality during WEDM of 316L stainless steel.
This document summarizes a study that investigated the effect of process parameters on machining EN31 steel using electrical discharge machining (EDM). Experiments were conducted using different tool materials (copper, aluminum, and EN24), supply currents, flushing pressures, and pulse-on times. The experiments analyzed their influence on surface roughness, material removal rate, tool wear rate, and taper. The results of this study can help optimize the EDM process for machining EN31 steel.
electric discharge machining of stainless steelMustufa Abidi
This document provides an overview of electric discharge machining (EDM) and its application to machining stainless steels. It discusses the principles and parameters of EDM, including electrical parameters like voltage, current, and pulse duration/interval. It reviews research on using EDM variants like wire EDM and micro-EDM to machine different stainless steel grades. The document examines models used to determine optimal EDM conditions and discusses trends and gaps in the research.
This document summarizes a study that used finite element analysis to simulate machining of Inconel 718, a nickel-based superalloy. Orthogonal cutting experiments were conducted with cutting speeds of 50 m/min and a feed rate of 0.1 mm/rev. The Johnson-Cook constitutive model was used to represent the material's flow behavior. The results showed that chip segmentation did not occur at low cutting speeds and residual stresses were highest near the machined surface and lowest near the uncut surface and deformed chip. Plastic strain was highest in the primary shear zone and lowest at the free end of the chip.
Realizing Potential of Graphite Powder in Enhancing Machining Rate in AEDM of...IDES Editor
Additive mixed electric discharge machining
(AEDM) is a recent innovation for enhancing the capabilities
of electrical discharge machining process. The objective of
present study is to realize the potential of graphite powder as
additive in enhancing machining capabilities of AEDM on
Inconel 718. Taguchi methodology has been adopted to plan
and analyze the experimental results. L36 Orthogonal Array
has been selected to conduct experiments. Peak current, Pulse
on time, duty cycle, gap voltage, retract distance and
concentration of fine graphite powder added into the dielectric
fluid were chosen as input process variables to study
performance in terms of material removal rate. The ANOVA
analysis identifies the most important parameters to maximize
material removal rate. The recommended best parametric
settings have been verified by conducting confirmation
experiments. From the present experimental study it is found
that addition of graphite powder enhances machining rate
appreciably. Machining rate is improved by 26.85% with 12g/
l of fine graphite powder at best parametric setting.
Study of effects of heat treatment on the hardness and microstructure of weld...Alexander Decker
This study investigated the effects of annealing heat treatment on the microstructure and mechanical properties of welded low-carbon steel pipes with 0.078% carbon content. Welding was performed on steel pipe samples, followed by heat treating one of the samples at 850°C for 2 hours. Hardness tests and microstructural analysis were conducted on the heat treated and untreated samples. The results showed that the heat treated sample had a lower hardness and a more predominant pearlite structure compared to the untreated sample. This resulted in improved ductility and toughness for the heat treated sample due to carbon diffusion and grain refinement during annealing. Therefore, annealing heat treatment is recommended after welding low-carbon steel to obtain higher ductility properties.
IRJET- Design, fabrication of Powder Compaction Die and Sintered behavior of ...IRJET Journal
The document describes the design, fabrication, and testing of a copper matrix hybrid composite material. Key points:
1. A powder compaction die was designed and fabricated from D2 tool steel using wire-cut EDM for compacting copper, TiC, graphite, and SiC powders.
2. Samples were prepared with 3, 6, and 9% SiC by weight and sintered at 850°C.
3. Hardness testing showed that hardness increased with higher SiC content. The 6% SiC composition exhibited the highest compressive strength.
4. The composite material could have applications in components requiring high conductivity, strength, and wear resistance such as bushes, bear
A review study of mechanical fatigue testing methods for small scale metal ma...Alexander Decker
This document reviews mechanical fatigue testing techniques for small-scale metal materials. It begins by discussing the increased focus on materials behavior at the micro and nano scales due to growing MEMS/NEMS applications. It then classifies fatigue testing techniques for small-scale materials, including uniaxial tension-tension, dynamic bending, and uniaxial tension-compression. Specific techniques are described in more detail, such as using piezoelectric actuators to enable load-controlled uniaxial cyclic loading of thin films. The document also examines fatigue properties of materials tested with these techniques, like studying crack growth rates in nickel alloy cantilever beams under dynamic bending.
A review study of mechanical fatigue testing methods for small scale metal ma...Alexander Decker
This document reviews mechanical fatigue testing techniques for small-scale metal materials. It begins by discussing the importance of characterizing the mechanical properties of materials used in micro-electromechanical systems. It then classifies and describes various fatigue testing techniques for small-scale materials, including uniaxial tension-tension testing, dynamic bending, and uniaxial tension-compression. Specific examples of how each technique has been used to test materials like copper thin films, carbon nanotube wires, and nickel alloy cantilever beams are provided. The document concludes by discussing factors to consider when developing testing methods for materials at the micro and nano scales.
IRJET- Experimental Investigation on Nickel Aluminium Alloy by Electric D...IRJET Journal
This document presents an experimental investigation on machining nickel aluminium alloy composites containing 4%, 6%, and 8% alumina by weight using electric discharge machining (EDM). The composites were characterized for hardness and tensile strength. EDM was used to machine the composites and statistical models were developed to predict material removal rate and surface roughness based on discharge current, pulse-on time, and duty cycle. It was found that material removal rate increased with discharge current and duty cycle but initially decreased and then increased with pulse-on time. Surface roughness increased with discharge current and pulse-on time. The study provides scope for future work investigating other composites and dielectric fluids using EDM.
The document discusses the selection of engineering materials. It covers:
(i) Factors that must be considered when selecting materials, including mechanical properties, manufacturing requirements, cost, and availability.
(ii) The complex process of materials selection that involves design, selecting production methods, testing, and evaluation.
(iii) Classifications of common engineering materials like metals, ceramics, polymers, composites, and advanced materials. It provides examples and properties of each class.
The document discusses the selection of engineering materials. It covers:
(i) Factors that must be considered when selecting materials, including mechanical properties, manufacturing requirements, cost, and availability.
(ii) The complex process of materials selection that involves design, selecting production methods, and evaluating performance.
(iii) Classifications of common engineering materials like metals, ceramics, polymers, composites, and advanced materials. It provides examples and properties of each class.
Metal Additive Manufacturing: Processes, Applications in Aerospace, and Antic...IRJET Journal
This document provides a comprehensive review of metal additive manufacturing processes, applications in aerospace, and anticipated challenges. It begins with an introduction to common metal additive manufacturing techniques like laser beam melting, electron beam melting, and laser metal deposition. It then discusses applications of these processes in the aerospace industry for manufacturing engine components, structural parts, and tools. Finally, it concludes by examining current limitations in areas like materials properties, cost, and standardization and the future challenges of improving process efficiency, scalability, and reliability. The review provides valuable insights for researchers, practitioners and policymakers interested in metal additive manufacturing opportunities in aerospace and other industries.
Optimization of Process Parameters in Wire Electrical Discharge Machining of ...IJERA Editor
Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This practical technology of the WEDM process is based on the conventional EDM sparking phenomenon utilizing the widely accepted non-contact technique of material removal. Since the introduction of the process, WEDM has evolved from a simple means of making tools and dies to the best alternative of producing micro-scale parts with the highest degree of dimensional accuracy and surface finish quality. Metal matrix composites are advanced materials having high specific strength, good wear resistance, and high thermal expansion coefficient. To achieve this task, machining parameters such as pulse on time, pulse off time, peak current, servo voltage, wire feed, wire tension etc. of this process should be selected such that optimal value of their performance measures like Material Removal Rate (MRR), Surface Roughness (SR), Gap current, Dimensional deviation, etc. can be obtained or improved. In past decades, intensive research work had been carried out by different researchers for improvement and optimization of WEDM performance measures using various optimization techniques like Taguchi, Response Surface Methodology (RSM), Artificial Neural Network (ANN), Genetic Algorithm (GA), etc. This paper also highlights the feasibility of the different control strategies of obtaining the optimal machining conditions. This literature review helps to identify the suitable process parameters and their ranges in machining of metal matrix composites.
IRJET - Dissimilar Welding of Alloy Steel and Stainless Steel by using Va...IRJET Journal
The document discusses dissimilar welding of alloy steel and stainless steel using cryogenic treatment. It first provides background on dissimilar welding and challenges associated with joining different metals. It then describes the materials used (alloy steel SS335 G12 and stainless steel SS 316), cryogenic equipment, and testing processes for hardness, impact resistance, and tensile strength. Results showed that cryogenic treatment improved mechanical properties like hardness, yield strength, and tensile strength compared to untreated specimens. The conclusions state that cryogenic treatment can further change metal structures and properties, improve mechanics, and reduce production costs.
Determination of Mechanical and Corrosion Properties of Cobalt Reinforced Al-...IRJET Journal
This document discusses a study that determined the mechanical and corrosion properties of cobalt-reinforced aluminum 6061 metal matrix composites produced using stir casting. Samples with 1%, 3%, 5%, and 7% cobalt reinforcements were tested. Results showed that tensile strength increased with higher cobalt content compared to the base aluminum alloy. Microstructure and composition analysis was performed using scanning electron microscopy and electrochemical impedance testing to evaluate corrosion properties. The study aimed to understand how different cobalt reinforcement compositions affected the mechanical and corrosion behavior of aluminum 6061 composites for potential engineering applications.
IRJET- An Experimental Investigation of Material Removal Rate in Electric Dis...IRJET Journal
The document presents an experimental investigation into the effect of electric discharge machining (EDM) parameters on material removal rate (MRR) when machining heat-treated SK2MCr4 carbon tool steel. Experiments were conducted using a die-sinking EDM machine with a copper electrode. Current, pulse on time, and servo voltage were varied as parameters, while pulse off time was held constant. MRR was found to be most influenced by current, which contributed 64.29% to MRR. The optimal parameter combination for maximizing MRR was determined to be a current of 12 A, pulse on time of 60 μs, and servo voltage of 3 V.
This document provides a review of research on wire electric discharge machining (WEDM). It begins with an abstract that describes WEDM as a process that uses a continuously traveling wire electrode to produce complex shapes in electrically conductive materials by generating sparks between the wire and workpiece. The document then reviews research on WEDM, including studies optimizing process parameters to improve machining performance and productivity and reduce wire breakage. It also discusses the basic principles and cutting process of WEDM, noting that the exact sparking phenomena remains disputed, and lists common wire materials and their applications.
This document discusses analyzing process parameters in wire electrical discharge machining (WEDM) of Inconel 718, a nickel-based super alloy. The objectives are to investigate significant WEDM parameters that affect material removal rate, electrode wear ratio, and hardness; study the effect of parameters on dimensional output like circularity and cylindricity; establish optimal WEDM parameters for Inconel; and develop an empirical model using Taguchi's design of experiments. The document reviews literature on modeling and optimizing WEDM performance and discusses the mechanism and influencing parameters of the WEDM process.
This document summarizes a study on using simulated annealing algorithm to predict weld bead geometry in gas metal arc welding. Key points:
1. The study develops mathematical models using multiple regression to predict four dimensions of weld bead geometry based on welding process parameters.
2. Experiments are conducted using a central composite design with five levels and factors including current, welding speed, gun angle, contact tip distance, and pinch.
3. The developed models are checked for adequacy and significance. Bead geometry is then predicted again using a simulated annealing algorithm for comparison.
IRJET- Advance Manufacturing Processes Review Part IV: Electrical Dischar...IRJET Journal
1) Electrical discharge machining (EDM) is a non-traditional machining process that uses electric sparks to erode conductive materials.
2) EDM can machine hard materials like steel and super alloys regardless of their hardness by melting and vaporizing small amounts of material with each electric spark.
3) The document reviews research on EDM process parameters and their effects on material removal rate, surface roughness, and tool wear. It also summarizes recent research trends in EDM like ultrasonic vibration-assisted EDM and EDM in water.
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...IRJET Journal
This document analyzes the optimization of process parameters for wire electrical discharge machining (WEDM) of stainless steel 316L. It conducts experiments using different controllable factors like discharge current, pulse on/off time, and arc gap. Taguchi design of experiments is used to optimize for maximum material removal rate. Experiments are conducted using a copper tool on 316L stainless steel workpieces. The results are analyzed to determine the optimum parameter levels for achieving higher material removal rate and better surface finish quality during WEDM of 316L stainless steel.
This document summarizes a study that investigated the effect of process parameters on machining EN31 steel using electrical discharge machining (EDM). Experiments were conducted using different tool materials (copper, aluminum, and EN24), supply currents, flushing pressures, and pulse-on times. The experiments analyzed their influence on surface roughness, material removal rate, tool wear rate, and taper. The results of this study can help optimize the EDM process for machining EN31 steel.
electric discharge machining of stainless steelMustufa Abidi
This document provides an overview of electric discharge machining (EDM) and its application to machining stainless steels. It discusses the principles and parameters of EDM, including electrical parameters like voltage, current, and pulse duration/interval. It reviews research on using EDM variants like wire EDM and micro-EDM to machine different stainless steel grades. The document examines models used to determine optimal EDM conditions and discusses trends and gaps in the research.
This document summarizes a study that used finite element analysis to simulate machining of Inconel 718, a nickel-based superalloy. Orthogonal cutting experiments were conducted with cutting speeds of 50 m/min and a feed rate of 0.1 mm/rev. The Johnson-Cook constitutive model was used to represent the material's flow behavior. The results showed that chip segmentation did not occur at low cutting speeds and residual stresses were highest near the machined surface and lowest near the uncut surface and deformed chip. Plastic strain was highest in the primary shear zone and lowest at the free end of the chip.
Realizing Potential of Graphite Powder in Enhancing Machining Rate in AEDM of...IDES Editor
Additive mixed electric discharge machining
(AEDM) is a recent innovation for enhancing the capabilities
of electrical discharge machining process. The objective of
present study is to realize the potential of graphite powder as
additive in enhancing machining capabilities of AEDM on
Inconel 718. Taguchi methodology has been adopted to plan
and analyze the experimental results. L36 Orthogonal Array
has been selected to conduct experiments. Peak current, Pulse
on time, duty cycle, gap voltage, retract distance and
concentration of fine graphite powder added into the dielectric
fluid were chosen as input process variables to study
performance in terms of material removal rate. The ANOVA
analysis identifies the most important parameters to maximize
material removal rate. The recommended best parametric
settings have been verified by conducting confirmation
experiments. From the present experimental study it is found
that addition of graphite powder enhances machining rate
appreciably. Machining rate is improved by 26.85% with 12g/
l of fine graphite powder at best parametric setting.
Study of effects of heat treatment on the hardness and microstructure of weld...Alexander Decker
This study investigated the effects of annealing heat treatment on the microstructure and mechanical properties of welded low-carbon steel pipes with 0.078% carbon content. Welding was performed on steel pipe samples, followed by heat treating one of the samples at 850°C for 2 hours. Hardness tests and microstructural analysis were conducted on the heat treated and untreated samples. The results showed that the heat treated sample had a lower hardness and a more predominant pearlite structure compared to the untreated sample. This resulted in improved ductility and toughness for the heat treated sample due to carbon diffusion and grain refinement during annealing. Therefore, annealing heat treatment is recommended after welding low-carbon steel to obtain higher ductility properties.
IRJET- Design, fabrication of Powder Compaction Die and Sintered behavior of ...IRJET Journal
The document describes the design, fabrication, and testing of a copper matrix hybrid composite material. Key points:
1. A powder compaction die was designed and fabricated from D2 tool steel using wire-cut EDM for compacting copper, TiC, graphite, and SiC powders.
2. Samples were prepared with 3, 6, and 9% SiC by weight and sintered at 850°C.
3. Hardness testing showed that hardness increased with higher SiC content. The 6% SiC composition exhibited the highest compressive strength.
4. The composite material could have applications in components requiring high conductivity, strength, and wear resistance such as bushes, bear
A review study of mechanical fatigue testing methods for small scale metal ma...Alexander Decker
This document reviews mechanical fatigue testing techniques for small-scale metal materials. It begins by discussing the increased focus on materials behavior at the micro and nano scales due to growing MEMS/NEMS applications. It then classifies fatigue testing techniques for small-scale materials, including uniaxial tension-tension, dynamic bending, and uniaxial tension-compression. Specific techniques are described in more detail, such as using piezoelectric actuators to enable load-controlled uniaxial cyclic loading of thin films. The document also examines fatigue properties of materials tested with these techniques, like studying crack growth rates in nickel alloy cantilever beams under dynamic bending.
A review study of mechanical fatigue testing methods for small scale metal ma...Alexander Decker
This document reviews mechanical fatigue testing techniques for small-scale metal materials. It begins by discussing the importance of characterizing the mechanical properties of materials used in micro-electromechanical systems. It then classifies and describes various fatigue testing techniques for small-scale materials, including uniaxial tension-tension testing, dynamic bending, and uniaxial tension-compression. Specific examples of how each technique has been used to test materials like copper thin films, carbon nanotube wires, and nickel alloy cantilever beams are provided. The document concludes by discussing factors to consider when developing testing methods for materials at the micro and nano scales.
IRJET- Experimental Investigation on Nickel Aluminium Alloy by Electric D...IRJET Journal
This document presents an experimental investigation on machining nickel aluminium alloy composites containing 4%, 6%, and 8% alumina by weight using electric discharge machining (EDM). The composites were characterized for hardness and tensile strength. EDM was used to machine the composites and statistical models were developed to predict material removal rate and surface roughness based on discharge current, pulse-on time, and duty cycle. It was found that material removal rate increased with discharge current and duty cycle but initially decreased and then increased with pulse-on time. Surface roughness increased with discharge current and pulse-on time. The study provides scope for future work investigating other composites and dielectric fluids using EDM.
The document discusses the selection of engineering materials. It covers:
(i) Factors that must be considered when selecting materials, including mechanical properties, manufacturing requirements, cost, and availability.
(ii) The complex process of materials selection that involves design, selecting production methods, testing, and evaluation.
(iii) Classifications of common engineering materials like metals, ceramics, polymers, composites, and advanced materials. It provides examples and properties of each class.
The document discusses the selection of engineering materials. It covers:
(i) Factors that must be considered when selecting materials, including mechanical properties, manufacturing requirements, cost, and availability.
(ii) The complex process of materials selection that involves design, selecting production methods, and evaluating performance.
(iii) Classifications of common engineering materials like metals, ceramics, polymers, composites, and advanced materials. It provides examples and properties of each class.
Metal Additive Manufacturing: Processes, Applications in Aerospace, and Antic...IRJET Journal
This document provides a comprehensive review of metal additive manufacturing processes, applications in aerospace, and anticipated challenges. It begins with an introduction to common metal additive manufacturing techniques like laser beam melting, electron beam melting, and laser metal deposition. It then discusses applications of these processes in the aerospace industry for manufacturing engine components, structural parts, and tools. Finally, it concludes by examining current limitations in areas like materials properties, cost, and standardization and the future challenges of improving process efficiency, scalability, and reliability. The review provides valuable insights for researchers, practitioners and policymakers interested in metal additive manufacturing opportunities in aerospace and other industries.
Optimization of Process Parameters in Wire Electrical Discharge Machining of ...IJERA Editor
Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This practical technology of the WEDM process is based on the conventional EDM sparking phenomenon utilizing the widely accepted non-contact technique of material removal. Since the introduction of the process, WEDM has evolved from a simple means of making tools and dies to the best alternative of producing micro-scale parts with the highest degree of dimensional accuracy and surface finish quality. Metal matrix composites are advanced materials having high specific strength, good wear resistance, and high thermal expansion coefficient. To achieve this task, machining parameters such as pulse on time, pulse off time, peak current, servo voltage, wire feed, wire tension etc. of this process should be selected such that optimal value of their performance measures like Material Removal Rate (MRR), Surface Roughness (SR), Gap current, Dimensional deviation, etc. can be obtained or improved. In past decades, intensive research work had been carried out by different researchers for improvement and optimization of WEDM performance measures using various optimization techniques like Taguchi, Response Surface Methodology (RSM), Artificial Neural Network (ANN), Genetic Algorithm (GA), etc. This paper also highlights the feasibility of the different control strategies of obtaining the optimal machining conditions. This literature review helps to identify the suitable process parameters and their ranges in machining of metal matrix composites.
IRJET - Dissimilar Welding of Alloy Steel and Stainless Steel by using Va...IRJET Journal
The document discusses dissimilar welding of alloy steel and stainless steel using cryogenic treatment. It first provides background on dissimilar welding and challenges associated with joining different metals. It then describes the materials used (alloy steel SS335 G12 and stainless steel SS 316), cryogenic equipment, and testing processes for hardness, impact resistance, and tensile strength. Results showed that cryogenic treatment improved mechanical properties like hardness, yield strength, and tensile strength compared to untreated specimens. The conclusions state that cryogenic treatment can further change metal structures and properties, improve mechanics, and reduce production costs.
Determination of Mechanical and Corrosion Properties of Cobalt Reinforced Al-...IRJET Journal
This document discusses a study that determined the mechanical and corrosion properties of cobalt-reinforced aluminum 6061 metal matrix composites produced using stir casting. Samples with 1%, 3%, 5%, and 7% cobalt reinforcements were tested. Results showed that tensile strength increased with higher cobalt content compared to the base aluminum alloy. Microstructure and composition analysis was performed using scanning electron microscopy and electrochemical impedance testing to evaluate corrosion properties. The study aimed to understand how different cobalt reinforcement compositions affected the mechanical and corrosion behavior of aluminum 6061 composites for potential engineering applications.
IRJET- An Experimental Investigation of Material Removal Rate in Electric Dis...IRJET Journal
The document presents an experimental investigation into the effect of electric discharge machining (EDM) parameters on material removal rate (MRR) when machining heat-treated SK2MCr4 carbon tool steel. Experiments were conducted using a die-sinking EDM machine with a copper electrode. Current, pulse on time, and servo voltage were varied as parameters, while pulse off time was held constant. MRR was found to be most influenced by current, which contributed 64.29% to MRR. The optimal parameter combination for maximizing MRR was determined to be a current of 12 A, pulse on time of 60 μs, and servo voltage of 3 V.
This document provides a review of research on wire electric discharge machining (WEDM). It begins with an abstract that describes WEDM as a process that uses a continuously traveling wire electrode to produce complex shapes in electrically conductive materials by generating sparks between the wire and workpiece. The document then reviews research on WEDM, including studies optimizing process parameters to improve machining performance and productivity and reduce wire breakage. It also discusses the basic principles and cutting process of WEDM, noting that the exact sparking phenomena remains disputed, and lists common wire materials and their applications.
This document discusses analyzing process parameters in wire electrical discharge machining (WEDM) of Inconel 718, a nickel-based super alloy. The objectives are to investigate significant WEDM parameters that affect material removal rate, electrode wear ratio, and hardness; study the effect of parameters on dimensional output like circularity and cylindricity; establish optimal WEDM parameters for Inconel; and develop an empirical model using Taguchi's design of experiments. The document reviews literature on modeling and optimizing WEDM performance and discusses the mechanism and influencing parameters of the WEDM process.
Neural network modeling of agglomeration firing process for polymetallic oresIJECEIAES
While processing polymetallic ores at the non-ferrous metallurgy problems arises connecting with the excellence of production and the efficient applying the technological devices-firing furnace and crusher machine. In earlier time, similar questions were solved due to the big practice experiences and using a mathematical modeling method. The mathematical model for optimizing those operating mode is a very complex and hard to calculation. Performing computations is needed too much time and many resources. Because the control of the agglomeration furnaces and other machines are including complex multiparameter processes. The method of the math modeling for optimization the operating mode to the firing furnace is replaced with modeling based on the neural network that is here a new method. The results obtained have shown that proposed methods based on the neural network modeling of metallurgical processes allow determining more accurate and adequate results of calculations than mathematical modeling by the traditional program. The use of new approaches for modeling the technological processes at the non-ferrous metallurgy gives opportunity to enhance an effectiveness of production excellence and to enhance an efficient applying the heat-energy equipment while a firing the sulfide polymetallic ores of non-ferrous metallurgy.
Optimization of EDM Process Parameters using Response Surface Methodology for...ijtsrd
The present work demonstrates the optimization process of material removal rate MRR of electrical discharge machining EDM by RSM Response Surface Methodology . The work piece material was EN31 tool steel. The pulse on time, pulse off time, pulse current and voltage were the control parameters of EDM. RSM method was used to design the experiment using rotatable central composite design as this is the most widely used experimental design for modeling a second–order response surface. The process has been successfully modeled using response surface methodology RSM and model adequacy checking is also carried out using Minitab software. The second order response models have been validated with analysis of variance. Finally, an attempt has been made to estimate the optimum machining conditions to produce the best possible responses within the experimental constraints. Dr. N. Mahesh Kumar | Mr. P. Chinna Rao ""Optimization of EDM Process Parameters using Response Surface Methodology for AISI D3 Steel"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23535.pdf
Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/23535/optimization-of-edm-process-parameters-using-response-surface-methodology-for-aisi-d3-steel/dr-n-mahesh-kumar
Experimental and Analytical Investigations of Friction Stir Welding of Variou...IRJET Journal
The document discusses experimental and analytical investigations of friction stir welding (FSW) of aluminum alloys. Finite element analysis will be performed to analyze FSW of aluminum 6061 and 7475 at different speeds and tool pin profiles using ANSYS. A 3D model of the welding process will be created in Pro/Engineer. Static structural analysis will determine stress, strain, and deformation, while thermal analysis will determine temperature distribution and heat flux during FSW. The effects of tool pin profile and rotational speed on weld strength will be examined.
The document discusses the effect of different specimen preparation methods for tension testing on the design of cold-formed steel members. It examines common specimen cutting methods like computer numerical control (CNC) laser cutting, plasma cutting, and wire electrical discharge machine (EDM) cutting. Each method has advantages but can also affect the specimen in different ways, such as through heat effects or surface roughness variations. The study aims to determine the most favorable cutting method for preparing tension test specimens to inform the design of cold-formed steel structures, by investigating how the preparation method influences tension test results and measured material properties.
Study of brass wire and cryogenic treated brass wire on titanium alloy using ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to Application of artificial neural networks (20)
2. Journal of Achievements in Materials and Manufacturing Engineering Volume 32 Issue 1 January 2009
methodology. Such models should enable the qualification of independent from input parameters) or the function is strong
mechanical properties such as strength, impact resistance or deformed by the noise, presented in descriptive vectors [9-12].
hardness for numerous engineer materials (with various chemical
composition and after heat and plastic treatment conducted with
various parameters). Suitable tool, which allows the easy 2. nvestigated material
I
2. Investigated material
modelling of such properties will make possible the better
selection both chemical composition and the parameters of the Structural non-alloy and alloy steels were chosen for
material processing. It makes possible at the same time the investigations. They are used in manufacturing of steel
obtainment of steels, which are qualitatively better, cheaper and constructions and devices and machines elements of the typical
more optimised under customers needs [3-7]. destination.
The prediction of steels mechanical parameters after Structural steels are the most often species produced in polish
normalisation process is not an easy process at all. The metallurgy. They are delivered to the customer as semi-
determination of the chemical composition influence is manufactured articles or ready articles in the form of long, round
particularly difficult, especially in the case of rolled sheet metal or squared bars, or (rarely) as sections, sheet metals and pipes.
plates [8]. Because of the fact that there is no physical models Structural steels delivered as semi-manufactured articles are
allowing to connect the influence of the chemical compositions manufactured as normalised, they reach customers after the
and the parameters of the mechanical and heat treatment on normalising rolling or without any thermal processing (directly
properties of manufactured steels, existing models are mainly after the hot rolling).
based on the statistical analysis and have limited range of use. As ready products steels are delivered after heat treatment,
They are the most often prepared to describe one single steel manufactured according to conditions required by a customer or
species manufactured in equal conditions [7,16-18]. polish standards.
Application of artificial neural networks is considerably Mechanical properties of over 125 various structural non-alloy
simplifies the modelling methodology. There is no need to posses and alloy steel species were examined. Examples of those species
the function of input and output parameters in evident form. If are showed in table 1.
only such a function exists it will be established through the Examined material was delivered in a form of round and
network during the training process and it will be written down as square rods. Steels were manufactured as normalised with various
weights individual neurons. However it is important that this processing parameters. Ranges of chemical elements, temperatures,
function exists and has the regular and unique character. duration times, kinds of cooling mediums for normalisation
In most cases the failure of neural network creation is caused treatment and geometrical parameters of manufactured elements
by the lack of the assignment function (output parameters are are presented in table 2.
Table. 1.
Examples of steels selected for examination.
Non-alloy steels Alloy steels
Steel for general Steels for Steels for pressure Steels for Spring Steels for Steels for
purposes [22] toughening [23] devices [24] toughening [25] steels [26] nitrogenising [27] carburetting [28]
E295 C22 P235T2 17CrNiMo6 52CrMoV4 31CrMo12 16MnCr5
E335 C30 P255G1 24CrMo4 45SiCrV6-2 31CrMoV9 16CrMo4-4
E360 C45R P265GH 30CrNiMo8 51CrV4 33CrAlMo7-10 17Cr3
S235J2G3 C60E P355N 36CrNiMo4 54SiCr6 34CrAlNi7 18NiCr5-4
S275JR 20Mn5 P355NL1 41Cr4 55Cr3 40NiCr6 20MnCr5
S355K2G2 28Mn6 P360N 50CrMo4 60Cr3 41CrAlMo7 20NiCrMo2-2
Table. 2.
Ranges of chemical elements, temperatures, times, kinds of cooling mediums for heat treatment and geometrical parameters of examined steels.
Mechanical
range size shape Chemical composition [%]
treatment
[mm] C Mn Si P S Cr Ni Mo W V Ti Cu Al
min 30 - round 0.09 0.25 0.16 0 0 0 0 0 0 0 0 0 0 - rolling
max 220 - square 0.60 1.57 1.20 0.3 0.28 2.20 2.08 1.10 0.12 0.26 0.15 0.35 1.02 - forging
Normalising
range Temperature Time Cooling
[°C] [min] medium
min 180 30
-air
max 980 480
38 Research paper L.A. Dobrzański, R. Honysz
3. Analysis and modelling
3. rtificial neural networks
A
3. Artificial neural networks A creation of a model with utilisation of artificial neural
networks is indicated where the precise physical and
mathematical description of the considered phenomenon is not
The name “artificial neural networks” describes the
known and where input and output values in descriptive vectors
hardware or software simulators, which are realizing
are well determined, however its entries and exit are well
semiparallel data processing. They are built from many mutually
qualified. An artificial neural network is able to learn how to
joined neurons and they imitate the work of biological brain
recognise the analysed problem and quickly give the answer on
structures [15].
the changing input parameters of the given problem [18, 19].
McCulloch and Pitts worked out the scheme of the neuron in
1943 and it was created as a building imitation of the biological
nervous cell [11,12].
Input signals xi coming from external receptors (for the input 4. odelling methodology
M
4. Modelling methodology
layer) or from the previous neurons layer (for hidden layers and
exit layer) are attached to the network inputs. Every signal is For property simulation of structural steels, the data set,
multiplicated by numerical value wi, which is interpreted as consisting of over 14212 vectors was used. This data describes
weight of the given neuron, ascribed to the neuron. This value has structural steels produced in the „Batory” foundry in Chorzów,
the influence in creation of the output value. The value of the Poland [20] after casting, mechanical and heat treatment. The
weight can stimulate the neuron to operate, when its sign is intelligent processing of data was applied with the use of artificial
positive, or can also hold on the neuron inactive , when the sign is neural networks for prediction of mechanical properties of steel
negative. The sum of entrance signals multiplied through materials. For every studied mechanical property the separate
appropriate weights is the argument of the neuron’s activation neural network was created.
function . The value of this function is the output signal of the Predicted mechanical parameters were: [1-3,7,13,14,16]
neuron y and is propagated to the neurons of the next layer or on yield stress Re,
the output of the network (for the neurons of the output layer). tensile strength Rm ,
The diagram representing the structure of single neuron is relative elongation A5,
presented in Figure 1. relative area reduction Z,
impact strength KV,
Brinell hardness HB.
Input values, which are used for parameter prediction are:
chemical composition,
type of mechanical treatment,
normalisation parameters (temperature, time and cooling
medium),
element shape and size.
The ranges of chemical elements, temperatures, times, kinds
Fig. 1. The model of artificial neuron by [11,12] of cooling mediums for normalisation processes and geometrical
parameters are presented in Table 2.
Equation 1 describes the mathematical model, where m is a The set of all descriptive vectors was divided into three
number of input signals of a single neuron subsets in the relation 2-1-1. The first set contains the half of all
vectors and was used for the modification of the neuron weights
m (training set). One fourth of the vectors were used for valuation of
y wi xi (1)
prediction errors by training process (validation set). Remaining
vectors were used for the independent determination of prediction
i 1 correctness, when the training process is finished (testing set).
Networks were trained with use of the back propagation and
The fundamental difference between artificial neural networks conjugate gradient methods. [10,12,15]
and other analytical algorithms, which are realizing the data processing For the verification of networks usability for the aims of
is the ability to generalise the knowledge for new given data, parameters prediction the following parameters of the quality
which were unknown earlier and which were not presented during
valuation were used:
the training process.
In distinction from expert systems, which require the average absolute error – difference between measured and
permanent access to whole assembly of knowledge on the subject predicted output values of the output variable,
about which they will decide, artificial neural networks requiring standard deviation ratio – a measure of the dispersion of the
only single access to this data set in the process of training. predicted values from their expected (mean) value. It is the
Neural networks reveal the tolerance on discontinuities, most common measure of statistical dispersion, measuring
accidental disorders or lacks of data in the descriptive vectors. how widely the values in a data set are spread,
This allows to use the networks for problems, that cannot be Pearson correlation – the standard Pearson-R correlation
solved by any other algorithm or their implementation will not coefficient between measured and predicted output values of
give any satisfactory results [10,12,15]. the output variable.
Application of artificial neural networks in modelling of normalised structural steels mechanical properties 39
4. Journal of Achievements in Materials and Manufacturing Engineering Volume 32 Issue 1 January 2009
The kind of the problem was determined as the standard, in properties among with the numbers of used neurons and the
which every vector is independent from another vector. The parameters of the quality valuation for all three sets for normalised
assignment of vectors to training, validation or testing set was and forged steels are introduced in the table 3. The parameters
random. The search for the optimal network was restricted to for steels after normalisation and rolling processes are in table 4.
architectures such as [6,9,10,18]: For all trained networks the Pearson correlation coefficient
linear networks has reached the value above 90% and comparatively low values of
radial basis function network (RBF) the standard deviation ratio. This is the very good representation of
generalized regression neural network (GRNN) estimated properties. On special attention deserving two networks
multi-layer perceptron (MLP) for yield stress (Re) and strength stress (Rm) prediction.
All computations were made by the use of Statistica Neural Correlation coefficient values over 98% and standard deviation
Network by Statsoft, the most technologically advanced and best ratio lower then 0,2 indicates very good regression performance.
performing neural networks application on the market. It offers For a graphical representation of networks quality
numerous selections of network types and training algorithms and comparative graphs among values predicted and measured of
is useful not only for neural network experts. [21]. testing set are shown on figures 2 and 3. For every estimated
parameter the vectors distribution is comparable for all three
subsets. It speaks for correctness of the prediction process.
5. odelling results
M
5. Modelling results Significant differences in vectors distribution among groups
would mark the possibility of excessive matching to training
vectors, and the bad quality of the network.
At the beginning only one multi-output network was trained
Trained neural networks made possible the analysis execution
for estimation of all parameters simultaneously, but the prediction
of the influence of the input parameters on predicted mechanical
quality was not satisfactory.
In order to improve results, all vectors were divided on two properties. In peculiarity the influence of alloying elements
sets: the first one containing all vectors, which describes steels change on mechanical properties with no change of heat
after normalisation and forging processes and the second - treatment. Then, the influence of heat treatment parameters
containing vectors coming from normalised and rolled steels. change on mechanical properties with no modification to steel
Then, another two multi-output networks were trained. Results chemical composition was computed.
were better, but still not satisfactory. To introduce the influence of chemical composition and
That is why it was decided to create separate network for processing parameters on estimated parameter surfaces graphs
every parameter, whose value has to be predicted. The best results were prepared. It allows to show the analysis results in graphical
were obtained for the multi-layer perceptron architecture with one style. Several of processed graphs are introduced as examples
and two hidden layers. The types of the net for individual (Figs. 4-9).
Table. 3.
Parameters of computed neural networks for steels after normalisation and forging processes
Training set Validation set Testing set
Variable Network Average Standard Pearson Average Standard Pearson Average Standard Pearson
architecture absolute deviation correla- absolute deviation correla- absolute deviation correla-
error ratio tion error ratio tion error ratio tion
Re MLP 18:18-5-1:1 15.439 0.1911 0.9817 16.623 0.1918 0.9814 18.146 0.1889 0.9820
Rm MLP 18:18-4-1:1 17.077 0.1953 0.9807 15.139 0.1983 0.9801 16.022 0.1940 0.9811
A5 MLP 14:14-6-1:1 1.397 0.3451 0.9388 1.257 0.3822 0.9242 1.324 0.3648 0.9313
Z MLP 16:16-10-1:1 1.984 0.2909 0.9567 1.926 0.3064 0.9511 1.894 0.3021 0.9533
KV MLP 14:14-9-1:1 14.594 0.2884 0.9581 14.161 0.3100 0.9553 14.624 0.3096 0.9513
HB MLP 11:11-5-1:1 5.243 0.3209 0.9482 5.595 0.3367 0.9423 4.743 0.2965 0.9563
Table. 4.
Parameters of computed neural networks for steels after normalisation and rolling processes
Training set Validation set Testing set
Variable Network Average Standard Pearson Average Standard Pearson Average Standard Pearson
architecture absolute deviation correla- absolute deviation correla- absolute deviation correla-
error ratio tion error ratio tion error ratio tion
Re MLP 17:17-9-3-1:1 6.433 0.1880 0.9826 6.932 0.1978 0.9803 7.132 0.2005 0.9800
Rm MLP 17:17-12-6:1 12.412 0.1871 0.9823 13.148 0.1938 0.9811 13.155 0.1855 0.9827
A5 MLP 13:13-4-1:1 1.130 0.3064 0.9519 1.108 0.2993 0.9542 1.191 0.3116 0.9503
Z MLP 15:15-9-1:1 1.343 0.2921 0.9563 1.385 0.3089 0.9519 1.474 0.3396 0.9410
KV MLP 16:16-11-1:1 11.505 0.3344 0.9424 11.646 0.3664 0.9307 12.164 0.3849 0.9234
HB MLP 8:8-5-1:1 4.884 0.2790 0.9604 4.730 0.2878 0.9579 5.841 0.3320 0.9441
40 Research paper L.A. Dobrzański, R. Honysz
5. Analysis and modelling
Fig. 2. C omparative graph of a) yield stress R e , b) tensile strength Rm, c) relative elongation A5, d) relative area reduction Z, e) impact
strength KV, f) Brinell hardness HB, calculated with use of the neural network s (testing set) and determined experimentally for steels
after normalisation and forging processes
Application of artificial neural networks in modelling of normalised structural steels mechanical properties 41
6. Journal of Achievements in Materials and Manufacturing Engineering Volume 32 Issue 1 January 2009
Fig. 3... C omparative graph of a) yield stress R e , b) tensile strength Rm, c) relative elongation A5, d) relative area reduction Z, e) impact
strength KV, f) Brinell hardness HB, calculated with use of the neural network s (testing set) and determined experimentally for steels
after normalisation and rolling processes
42 Research paper L.A. Dobrzański, R. Honysz
7. Analysis and modelling
Fig. 4. Influence of normalisation temperature and time on Fig. 6. Influence of sulphur and phosphorus concentration on relative
yield stress. (shape: round, diameter: 135mm, 0.21%C, 0.74%Mn, elongation A5 ,(shape: square, size:220mm, normalisation parameters:
0.34%Si, 0.003%P, 0.002%S, 0.88%Cr, 0.34%Ni, 0.27%Mo, 980°C/180min/air, 0.13%C, 0.46%Mn, 0.22%Si, 0.34%Cr, 0.14%Ni,
0.12%Cu, 0,024%Al, rolling). 0.52Mo, 0.23%V, 0.14%Cu, forging).
Fig. 5. Influence of carbon and manganese concentration on Fig. 7. Influence of nickel and chromium concentration on Brinell
strength stress,(shape: round, diameter: 45mm, normalising hardness, (normalisation parameters: 760°C/240min/air, 0.21%C,
parameters: 550°C/240min/air, 0.32%Si, 0.016%P, 0.005%S, 0.56%Mn, 0.009%P, 0.021%S, 0.02%Mo, 0.03%Ti, 0.0035 Al,
1.17%Cr, 1.05%Ni, 0.02%Mo, 0.001%V, 0.05%Al, forging). rolling).
Application of artificial neural networks in modelling of normalised structural steels mechanical properties 43
8. Journal of Achievements in Materials and Manufacturing Engineering Volume 32 Issue 1 January 2009
An example of neural nets, which are used to predict yield
stress is presented on figure 10a. It is a four layer perceptron with
17 input values, 11 neurons in first hidden layer, 3 neurons in
second hidden layer and one output value. Figure 10b shows the
neural network used to predict impact resistance. It is a three-
layer perceptron with 16 input values, 11 neurons in one hidden
layer and one output value.
a) b)
Fig. 10. Architectures of neural net used to predict mechanical
Fig. 8. Influence of tungsten and vanadium concentration on parameters a) yield stress, four-layer perceptron 17:17-9-3-1:1,
relative area reduction Z, (shape:round. diameter:35mm, b) impact resistance , three-layer perceptron 16:16-11-1:1.
normalisation parameters: 860°C/120min/air, 0.33%C, 0.91%Mn,
0.33%Si, 0.017%P, 0.009%S, 0.1%Cr, 0.06%Ni, 0.02%Mo,
0%Ti, 0.12%Cu, 0.02%Al, rolling)
6. Conclusions
6. Conclusions
This paper introduces the property modelling methodology of
structural steels after normalisation process. Parameters, which
were determined through the use of artificial neural networks, are
yield stress, tensile strength, relative elongation, relative area reduction,
impact strength and Brinell hardness. The input values for prediction
process were chemical composition, type and parameters of heat and
plastic treatment and element shape and size.
Results obtained from the given ranges of input data show the
very good ability of artificial neural networks to predict mechanical
properties of normalised steels. The Pearson correlation
coefficient over 90% and low deviation ratio inform about the
correct execution of the training and obtained small differences in the
relation between computed and experimentally measured values. The
uniform distribution of vectors in every set indicates about the
good ability of the networks to results generalisation.
Peculiarity, on special attention deserves small differences among
training and testing sets. A large divergence among these sets in the
practice makes the network useless.
Received results also have confirmed the correctness of the artificial
neural networks usage as the simulating tool possible for the
application in the area of material engineering for the prediction of
mechanical properties. Applied with success for normalised
structural steels it gives the chance on the effective application for
different steel grades or even for the different types of engineer
materials.
Fig. 9. Influence of normalisation temperature and time on The virtual samples of normalised steels, created with the use
impact resistance KV, (shape:square, size:160mm, 0.15%C, 0.56%Mn, of described networks will be an immense aid in the Materials
0.21%Si, 0.45%Cr, 0.08Ni, 0.52%Mo, 0.001%Ti, 0.027%Al, Science Virtual Laboratory for constructors and also for students,
forging). whose will experience this group of engineers materials [4,5].
44 Research paper L.A. Dobrzański, R. Honysz
9. Analysis and modelling
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READING DIRECT: www.journalamme.org 45