PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY
In recent years, it has become possible to explore the design space for multiple objectives consi... more In recent years, it has become possible to explore the design space for multiple objectives considering multidisciplinary requirements with the advent of generative design for additive manufacturing (G-DFAM). Generative design is a part of the family of DFAM techniques that compare and consider a multitude of design outcomes in several technical fields. G-DFAM provides the most desirable outcome, light-weighting with the increase in the performance and functionality of the parts by ensuring the correct design into the right manufacturing technology. The G-DFAM has resulted as an effective way to predict, analyze, and design additive manufacturing (AM) processes. The generative design offers new modes of aesthetic experience based on the system dynamics into the production of artifacts and experiences. This study describes the generative approach for obtaining alternative solutions dealing with the structural optimization problems for an aircraft landing gear door bracket and a jet engine bracket. The main design objective is to minimize weight while fitting in the target envelope and meeting the technical requirements. Finally, the generative design solutions are simulated for process simulation of metal AM to check for various stress and distortions in the final part before manufacturing it.
Advances in Materials and Processing Technologies, 2022
Hidden layers perform a vital role in the performance of artificial neural networks (ANNs), espec... more Hidden layers perform a vital role in the performance of artificial neural networks (ANNs), especially for serpentine problems where the curtailment of accuracy and time complexity is observed. A higher number of hidden layers and neurons enlarge the order of weights and influence the ANN accuracy. In the present work, an ANN model is developed to predict the surface roughness of fused deposition modelling (FDM) parts considering the effect of the number of neurons, hidden layers, layer height, infill density, nozzle temperature, and print speed. A feedforward back-propagation machine learning algorithm is used to model an ANN. This study finds better prediction accuracy with the ANN architecture having two hidden layers with 150 neurons on each layer (R-squared: 0.875) followed by one hidden layer with 250 neurons (R-squared: 0.83). This study finds a decrease in the surface roughness of FDM parts with the increase in infill density. However, the surface roughness was observed to increase with the layer height, print speed, and nozzle temperature. This study finds a decrease in the surface roughness of FDM parts with the increase in infill density and an increase in the surface roughness with the layer height, print speed, and nozzle temperature.
Journal of Materials Engineering and Performance, 2022
Additive manufacturing (AM) has emerged as a promising technology to cater to the increasing dema... more Additive manufacturing (AM) has emerged as a promising technology to cater to the increasing demand for the fabrication of multi-functional, multi-material, and complex parts. AM is revolutionizing production and product development in the aerospace, automotive, and medical fields. However, mismatch in material properties, pervasive imperfections in the printed part, and lack of build consistency are crucial concerns. Higher accuracy in AM processes primarily depends on controlling various aspects of the process. In the last few years, machine learning, data analytics, and design for additive manufacturing have been the most extensively used techniques to address the vital concerns of additive manufacturing. Despite well-known techniques, very few studies reported applications of these techniques for aerospace. Specifically, this study comprehensively reviews recent advancements in the design for additive manufacturing (DfAM) and applications of machine learning and big data analytics to address the prime concerns of AM. The DfAM emphasizes issues and opportunities for topology optimization and methods for generative design for weight reduction and manufacturing of products with high resolution. Simulation and modeling techniques that are being used to improve geometric quality and process analysis are discussed to enable its potential for different applications. Further, automation of AM process using the Internet of things and knowledge-based systematic process planning is discussed to address key issues in process planning of multiple parts. Finally, the current challenges and scope for algorithmically driven AM processes are summarized with the trends of automation in AM to ensure greater efficiency and a better lifecycle of AM products in the era of industry 4.0.
IOP Conference Series: Materials Science and Engineering, 2021
Hard turning with CBN and ceramic inserts is commonly regarded as a cost-effective alternative to... more Hard turning with CBN and ceramic inserts is commonly regarded as a cost-effective alternative to grinding. However, there have been few studies comparing hard turning with low-cost carbide tools to high-cost CBN and ceramic cutting tools. However, when it comes to the usage of cutting coolant during severe turning, there are mixed outcomes. In this study, a PVD-coated TiSiN-TiAlN carbide tool was used to hard turn AISI 52100 steel in a dry and MQL environment. Through multi-objective optimization, a comparative assessment in terms of surface roughness, cutting force, and tool life under various cutting settings is provided. In terms of three components of cutting force, surface roughness, and tool life, mathematical models were constructed to forecast and improve machining performance. Under both dry and MQL conditions, the study discovered an optimal cutting speed of 108 m/min, a feed value of 0.09 mm/rev, and a depth of cut of 0.16 mm. Under MQL, hard turning produced optimal sur...
PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY
In recent years, it has become possible to explore the design space for multiple objectives consi... more In recent years, it has become possible to explore the design space for multiple objectives considering multidisciplinary requirements with the advent of generative design for additive manufacturing (G-DFAM). Generative design is a part of the family of DFAM techniques that compare and consider a multitude of design outcomes in several technical fields. G-DFAM provides the most desirable outcome, light-weighting with the increase in the performance and functionality of the parts by ensuring the correct design into the right manufacturing technology. The G-DFAM has resulted as an effective way to predict, analyze, and design additive manufacturing (AM) processes. The generative design offers new modes of aesthetic experience based on the system dynamics into the production of artifacts and experiences. This study describes the generative approach for obtaining alternative solutions dealing with the structural optimization problems for an aircraft landing gear door bracket and a jet engine bracket. The main design objective is to minimize weight while fitting in the target envelope and meeting the technical requirements. Finally, the generative design solutions are simulated for process simulation of metal AM to check for various stress and distortions in the final part before manufacturing it.
Advances in Materials and Processing Technologies, 2022
Hidden layers perform a vital role in the performance of artificial neural networks (ANNs), espec... more Hidden layers perform a vital role in the performance of artificial neural networks (ANNs), especially for serpentine problems where the curtailment of accuracy and time complexity is observed. A higher number of hidden layers and neurons enlarge the order of weights and influence the ANN accuracy. In the present work, an ANN model is developed to predict the surface roughness of fused deposition modelling (FDM) parts considering the effect of the number of neurons, hidden layers, layer height, infill density, nozzle temperature, and print speed. A feedforward back-propagation machine learning algorithm is used to model an ANN. This study finds better prediction accuracy with the ANN architecture having two hidden layers with 150 neurons on each layer (R-squared: 0.875) followed by one hidden layer with 250 neurons (R-squared: 0.83). This study finds a decrease in the surface roughness of FDM parts with the increase in infill density. However, the surface roughness was observed to increase with the layer height, print speed, and nozzle temperature. This study finds a decrease in the surface roughness of FDM parts with the increase in infill density and an increase in the surface roughness with the layer height, print speed, and nozzle temperature.
Journal of Materials Engineering and Performance, 2022
Additive manufacturing (AM) has emerged as a promising technology to cater to the increasing dema... more Additive manufacturing (AM) has emerged as a promising technology to cater to the increasing demand for the fabrication of multi-functional, multi-material, and complex parts. AM is revolutionizing production and product development in the aerospace, automotive, and medical fields. However, mismatch in material properties, pervasive imperfections in the printed part, and lack of build consistency are crucial concerns. Higher accuracy in AM processes primarily depends on controlling various aspects of the process. In the last few years, machine learning, data analytics, and design for additive manufacturing have been the most extensively used techniques to address the vital concerns of additive manufacturing. Despite well-known techniques, very few studies reported applications of these techniques for aerospace. Specifically, this study comprehensively reviews recent advancements in the design for additive manufacturing (DfAM) and applications of machine learning and big data analytics to address the prime concerns of AM. The DfAM emphasizes issues and opportunities for topology optimization and methods for generative design for weight reduction and manufacturing of products with high resolution. Simulation and modeling techniques that are being used to improve geometric quality and process analysis are discussed to enable its potential for different applications. Further, automation of AM process using the Internet of things and knowledge-based systematic process planning is discussed to address key issues in process planning of multiple parts. Finally, the current challenges and scope for algorithmically driven AM processes are summarized with the trends of automation in AM to ensure greater efficiency and a better lifecycle of AM products in the era of industry 4.0.
IOP Conference Series: Materials Science and Engineering, 2021
Hard turning with CBN and ceramic inserts is commonly regarded as a cost-effective alternative to... more Hard turning with CBN and ceramic inserts is commonly regarded as a cost-effective alternative to grinding. However, there have been few studies comparing hard turning with low-cost carbide tools to high-cost CBN and ceramic cutting tools. However, when it comes to the usage of cutting coolant during severe turning, there are mixed outcomes. In this study, a PVD-coated TiSiN-TiAlN carbide tool was used to hard turn AISI 52100 steel in a dry and MQL environment. Through multi-objective optimization, a comparative assessment in terms of surface roughness, cutting force, and tool life under various cutting settings is provided. In terms of three components of cutting force, surface roughness, and tool life, mathematical models were constructed to forecast and improve machining performance. Under both dry and MQL conditions, the study discovered an optimal cutting speed of 108 m/min, a feed value of 0.09 mm/rev, and a depth of cut of 0.16 mm. Under MQL, hard turning produced optimal sur...
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