T. Sugihara, Takuma Nomura, T. Enomoto, A. Udupa, K. Viswanathan, J. Mann
In metal cutting processes, a chemical ambient environment in the cutting zone can be a useful variable for process control and process performance improvement. In this work, we study how mechanochemical effects influence the chip formation process, especially focusing on a specific chemical reaction between aluminum alloys and alcohols as a model system. Using high speed in-situ imaging and particle image velocimetry, we demonstrate that the mechanochemical effect in cutting of annealed Al with use of isopropyl alcohol (IPA) is manifest in two different ways: a lubricating effect at the tool-chip interface and an embrittlement effect at the workpiece free-surface, depending on the undeformed chip thickness and cutting speed. Consequently, the highly unsteady chip flow seen in dry cutting of annealed Al, which is typically seen in cutting of ductile “gummy” metals, transitions to a laminar-type (smooth) chip-flow mode or a segmented, fracture-controlled chip flow, due to the Al-IPA reaction. In both cases, the modified chip flow modes lead to significant reduction in cutting forces and improvement of finished surface quality. The specific manifestation of the mechanochemical effect is found to be principally determined by the penetration capability of the alcohols into the tool-chip interface and the time required for the chemical reaction between aluminum and the alcohols. Also, we discuss some implications for improving the performance of practical Al cutting operations using alcohols as a fluid medium.
{"title":"Exploring the Role of Mechanochemical Effects in Cutting of Aluminum Alloys With Alcohols","authors":"T. Sugihara, Takuma Nomura, T. Enomoto, A. Udupa, K. Viswanathan, J. Mann","doi":"10.1115/msec2022-85192","DOIUrl":"https://doi.org/10.1115/msec2022-85192","url":null,"abstract":"\u0000 In metal cutting processes, a chemical ambient environment in the cutting zone can be a useful variable for process control and process performance improvement. In this work, we study how mechanochemical effects influence the chip formation process, especially focusing on a specific chemical reaction between aluminum alloys and alcohols as a model system. Using high speed in-situ imaging and particle image velocimetry, we demonstrate that the mechanochemical effect in cutting of annealed Al with use of isopropyl alcohol (IPA) is manifest in two different ways: a lubricating effect at the tool-chip interface and an embrittlement effect at the workpiece free-surface, depending on the undeformed chip thickness and cutting speed. Consequently, the highly unsteady chip flow seen in dry cutting of annealed Al, which is typically seen in cutting of ductile “gummy” metals, transitions to a laminar-type (smooth) chip-flow mode or a segmented, fracture-controlled chip flow, due to the Al-IPA reaction. In both cases, the modified chip flow modes lead to significant reduction in cutting forces and improvement of finished surface quality. The specific manifestation of the mechanochemical effect is found to be principally determined by the penetration capability of the alcohols into the tool-chip interface and the time required for the chemical reaction between aluminum and the alcohols. Also, we discuss some implications for improving the performance of practical Al cutting operations using alcohols as a fluid medium.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87837146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In laser keyhole welding of dissimilar metals, metal mixing in the molten pool is critical to the microstructure and mechanical performance of the welds. In this study, metal mixing and its effects on the mechanical performance of the miscible Nickel-Copper welds are investigated. Experiments were carried out to fabricate samples with different welding parameters. Energy-dispersive X-ray spectroscopy is used to characterize the metal distribution in the fusion zone of the post welds. Mechanical strength testing and fractographic analysis are performed to characterize the strength of the welds and the fracture mode. Two regions of different concentrations can be found in the welds, and the concentrations of these two regions vary significantly with welding parameters. The weld strength is dependent on the interfacial region concentration, and the welds undergo a mixture of shear fracture and tensile fracture during the mechanical strength testing process. The interfacial concentration and the weld strength can be controlled by tuning the concentration of the two concentration regions in the fusion zone and the location of the boundary between the two regions. This study provides insights for industries regarding the design and optimization of the laser welding process to achieve welds with optimal mechanical performance.
{"title":"Effect of Metal Mixing on Mechanical Performance of Laser Keyhole Welding of Nickel and Copper","authors":"Wenkang Huang, W. Tan, W. Cai, Jennifer Bracey","doi":"10.1115/msec2022-85224","DOIUrl":"https://doi.org/10.1115/msec2022-85224","url":null,"abstract":"\u0000 In laser keyhole welding of dissimilar metals, metal mixing in the molten pool is critical to the microstructure and mechanical performance of the welds. In this study, metal mixing and its effects on the mechanical performance of the miscible Nickel-Copper welds are investigated. Experiments were carried out to fabricate samples with different welding parameters. Energy-dispersive X-ray spectroscopy is used to characterize the metal distribution in the fusion zone of the post welds. Mechanical strength testing and fractographic analysis are performed to characterize the strength of the welds and the fracture mode. Two regions of different concentrations can be found in the welds, and the concentrations of these two regions vary significantly with welding parameters. The weld strength is dependent on the interfacial region concentration, and the welds undergo a mixture of shear fracture and tensile fracture during the mechanical strength testing process. The interfacial concentration and the weld strength can be controlled by tuning the concentration of the two concentration regions in the fusion zone and the location of the boundary between the two regions. This study provides insights for industries regarding the design and optimization of the laser welding process to achieve welds with optimal mechanical performance.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82716158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The chip formation models developed to date give no exact representation of the physics phenomena occurring during the complex machine cutting process. Despite the large number of investigations and simulations, there is still limited clarity of the real chip formation process. The models try to solve the plastic flow through force or stress simulation, without proper regard to adequate process mechanics. Due to these circumstances, practical evidence is missing. Analyzing this situation very carefully, some scientists founded the Industry 4.0 initiative to create scientific space with new opportunities. Whereas second and third industrial revolutions have been focused on organization and automation — Industry 4.0 is focused on technology, data integration and artificial intelligence (AI). However, before teaching a computer AI, the adequate process mechanics should be systematically developed and understood. This paper presents the complex process mechanics of chip formation with non-linear conditions in the metal microstructure, with two different friction zones, with self-hardening or temperatures effects. These entire phenomena can’t be solved separately because they have an interdependent relationship. The developed mathematical equations for strain and stress lead to square grid deformation in the chip formation zone, and this grid deformation does not disappear after completing the process, so that the theoretical development can be compared with practical results. This will be presented for two different materials Nimonic 90 and Ti-6Al-4V. For Nimonic 90 a built-up-edge (BUE) will be identified, and this is based on the stream-line inflow-angle. Quite contrary is the chip formation process for Ti-6Al-4V. A diffusion process in the interface chip-tool take place resulting in a self-blockade with segmented chip. In addition, the developed temperatures during cutting could be estimated and will be presented for the two different creep-resistant alloys. Finally, a high agreement between the theoretical and experimental results could be documented.
{"title":"Process Mechanics – a Guide for Industry 4.0: Modelling Cutting of Nimonic 90 and Ti-6Al-4V","authors":"W. Lortz, Radu Pavel","doi":"10.1115/msec2022-85443","DOIUrl":"https://doi.org/10.1115/msec2022-85443","url":null,"abstract":"\u0000 The chip formation models developed to date give no exact representation of the physics phenomena occurring during the complex machine cutting process. Despite the large number of investigations and simulations, there is still limited clarity of the real chip formation process. The models try to solve the plastic flow through force or stress simulation, without proper regard to adequate process mechanics. Due to these circumstances, practical evidence is missing. Analyzing this situation very carefully, some scientists founded the Industry 4.0 initiative to create scientific space with new opportunities. Whereas second and third industrial revolutions have been focused on organization and automation — Industry 4.0 is focused on technology, data integration and artificial intelligence (AI). However, before teaching a computer AI, the adequate process mechanics should be systematically developed and understood. This paper presents the complex process mechanics of chip formation with non-linear conditions in the metal microstructure, with two different friction zones, with self-hardening or temperatures effects. These entire phenomena can’t be solved separately because they have an interdependent relationship. The developed mathematical equations for strain and stress lead to square grid deformation in the chip formation zone, and this grid deformation does not disappear after completing the process, so that the theoretical development can be compared with practical results. This will be presented for two different materials Nimonic 90 and Ti-6Al-4V. For Nimonic 90 a built-up-edge (BUE) will be identified, and this is based on the stream-line inflow-angle. Quite contrary is the chip formation process for Ti-6Al-4V. A diffusion process in the interface chip-tool take place resulting in a self-blockade with segmented chip. In addition, the developed temperatures during cutting could be estimated and will be presented for the two different creep-resistant alloys. Finally, a high agreement between the theoretical and experimental results could be documented.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79555832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Uenohara, M. Yasuda, Kosei Yamamoto, Y. Mizutani, Y. Takaya
Three-dimensional microstructures in the sub-micrometer scale exhibit unique properties. A flexible machining method to fabricate such structures is desired. Photonic nanojet (PNJ) is high intensity laser beam with sub-micrometer scale beam diameter and micrometer scale depth of focus. PNJs have a longer depth of focus than tightly focused laser beams with a high numerical aperture. In this study, we investigate the angular control of PNJs by controlling the propagation direction of incident light in order to realize flexible laser micro machining using PNJs. By controlling the position of the microsphere in the focused laser beam with a large defocus, the propagation direction of the laser beam incident on the microsphere is changed, and the angle of the PNJ can be controlled. Laser machining experiments on a silicon substrate showed that the PNJ angle can be controlled by incident laser angle. Furthermore, sub-micrometer scale laser machining was achieved even when using an oblique PNJ. The simulation results and experimental results are in good agreement. In conclusion, the angle control of the photonic nanojet can be applied to flexible multi-axis laser micro machining.
{"title":"Laser Micro Machining Using an Oblique Photonic Nanojet With Focused Laser Beam Irradiation","authors":"T. Uenohara, M. Yasuda, Kosei Yamamoto, Y. Mizutani, Y. Takaya","doi":"10.1115/msec2022-82356","DOIUrl":"https://doi.org/10.1115/msec2022-82356","url":null,"abstract":"\u0000 Three-dimensional microstructures in the sub-micrometer scale exhibit unique properties. A flexible machining method to fabricate such structures is desired. Photonic nanojet (PNJ) is high intensity laser beam with sub-micrometer scale beam diameter and micrometer scale depth of focus. PNJs have a longer depth of focus than tightly focused laser beams with a high numerical aperture. In this study, we investigate the angular control of PNJs by controlling the propagation direction of incident light in order to realize flexible laser micro machining using PNJs. By controlling the position of the microsphere in the focused laser beam with a large defocus, the propagation direction of the laser beam incident on the microsphere is changed, and the angle of the PNJ can be controlled. Laser machining experiments on a silicon substrate showed that the PNJ angle can be controlled by incident laser angle. Furthermore, sub-micrometer scale laser machining was achieved even when using an oblique PNJ. The simulation results and experimental results are in good agreement. In conclusion, the angle control of the photonic nanojet can be applied to flexible multi-axis laser micro machining.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75443018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The coronavirus pandemic has caused unprecedented supply chain disruptions globally, resulting in a heightened need for supply chain resilience. Particularly in the case of semiconductor chips, a commodity already in high demand, the existing challenges in supply chains have been aggravated by the pandemic. This global shortage is resulting in manufacturing disruptions across multiple sectors from automobiles to electronics. The global automobile industry alone is said to suffer a $210 billion loss in revenue from chip shortages. This highlights the cruciality of scientifically analyzing and building solutions that addresses the issue of resiliency of global semiconductor supply chains. While several news articles and white papers have reported this issue, there has been a lack of scientific literature on this topic. The objective of this paper is to identify the factors causing semiconductor shortage, analyze, and quantify their impact on the supply chain. This paper identifies 20 factors under 4 major categories from pre- and post-pandemic era, in the period ranging from 2018 to 2021, that have contributed to this disruption. The categories are: geopolitical tensions, natural disasters, logistics challenges and COVID-19 pandemic. The factors are ranked using the Analytical Hierarchy Process (AHP) methodology. The scientific value of this study lies in its contribution of quantifying and ranking the impact of the individual factors leading to the recent disruption in semiconductor supply chains. The results of this study will provide supply chain managers with the analytical information necessary for enabling resilient semiconductor supply chains as they navigate through these current challenges.
{"title":"Global Disruption of Semiconductor Supply Chains During COVID-19: An Evaluation of Leading Causal Factors","authors":"Aamirah Mohammed, Sardar Asif Khan","doi":"10.1115/msec2022-85306","DOIUrl":"https://doi.org/10.1115/msec2022-85306","url":null,"abstract":"\u0000 The coronavirus pandemic has caused unprecedented supply chain disruptions globally, resulting in a heightened need for supply chain resilience. Particularly in the case of semiconductor chips, a commodity already in high demand, the existing challenges in supply chains have been aggravated by the pandemic. This global shortage is resulting in manufacturing disruptions across multiple sectors from automobiles to electronics. The global automobile industry alone is said to suffer a $210 billion loss in revenue from chip shortages. This highlights the cruciality of scientifically analyzing and building solutions that addresses the issue of resiliency of global semiconductor supply chains. While several news articles and white papers have reported this issue, there has been a lack of scientific literature on this topic. The objective of this paper is to identify the factors causing semiconductor shortage, analyze, and quantify their impact on the supply chain. This paper identifies 20 factors under 4 major categories from pre- and post-pandemic era, in the period ranging from 2018 to 2021, that have contributed to this disruption. The categories are: geopolitical tensions, natural disasters, logistics challenges and COVID-19 pandemic. The factors are ranked using the Analytical Hierarchy Process (AHP) methodology. The scientific value of this study lies in its contribution of quantifying and ranking the impact of the individual factors leading to the recent disruption in semiconductor supply chains. The results of this study will provide supply chain managers with the analytical information necessary for enabling resilient semiconductor supply chains as they navigate through these current challenges.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76208121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays Al-steel joints are increasingly used in the lightweight automobile structure to meet the requirement of energy saving and CO2 emission reduction. Among various joining technologies proposed to join dissimilar material, resistance spot welding (RSW) stands out since the operation speed is fast, no extra material is needed and it is easy to be automated in mass production. Determining the joint load bearing capacity is one important task in the further application of this technology. Traditionally, it is obtained by performing some fractured tests, such as lap-shear test, coach peel test, cross tension test, by extracting and then analyzing the mechanical performance parameters including maximum load and failure energy from the force-and-displacement (F-D) curves. However, this method is time consuming and finite element simulation provides a much more efficient solution. Therefore, this work aimed at developing an experimentally validated performance model of Al-steel RSW joint. An aluminum alloy (AA6022) and a hot-dip galvanized high strength low alloy steel (HDG HSLA340), both of which are widely used in automotive industry, were joined by a unique RSW process proposed by General Motors in lap-shear configuration. To predict the joint fracture, a modified Gurson-Tvergaard-Needleman (GTN) model was applied. Finally, this performance model was validated experimentally and proved to be capable of predicting the maximum load and failure energy accurately.
{"title":"Performance Prediction of Resistance Spot Welding Joints Using a Modified GTN Model","authors":"Weiling Wen, M. Banu","doi":"10.1115/msec2022-83697","DOIUrl":"https://doi.org/10.1115/msec2022-83697","url":null,"abstract":"\u0000 Nowadays Al-steel joints are increasingly used in the lightweight automobile structure to meet the requirement of energy saving and CO2 emission reduction. Among various joining technologies proposed to join dissimilar material, resistance spot welding (RSW) stands out since the operation speed is fast, no extra material is needed and it is easy to be automated in mass production. Determining the joint load bearing capacity is one important task in the further application of this technology. Traditionally, it is obtained by performing some fractured tests, such as lap-shear test, coach peel test, cross tension test, by extracting and then analyzing the mechanical performance parameters including maximum load and failure energy from the force-and-displacement (F-D) curves. However, this method is time consuming and finite element simulation provides a much more efficient solution. Therefore, this work aimed at developing an experimentally validated performance model of Al-steel RSW joint. An aluminum alloy (AA6022) and a hot-dip galvanized high strength low alloy steel (HDG HSLA340), both of which are widely used in automotive industry, were joined by a unique RSW process proposed by General Motors in lap-shear configuration. To predict the joint fracture, a modified Gurson-Tvergaard-Needleman (GTN) model was applied. Finally, this performance model was validated experimentally and proved to be capable of predicting the maximum load and failure energy accurately.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"128 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75590752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ziwei Feng, Xueyan Zhang, Jianhui Su, Yifan Liu, Hongyun Zhao, Bo Chen, Xiaoguo Song, C. Tan
Achieving strong joining of aluminum alloy to carbon fiber thermoplastic composites (CFRTP) using laser heating was an essential method to reduce the weight of structure and save energy. In current study, a silane coupling agent was adopted on the aluminum alloy surface to improve the bonding strength of the laser joining of aluminum alloy to CFRTP. Substrate characteristics of the aluminum alloy after treatment including surface morphology, surface roughness and wettability as well as chemical composition were then studied. For comparison, identical experiments were also performed for the polished and sanded aluminum alloy. Compared to the aluminum alloy polished, the surface roughness of aluminum alloy sanded and silane treated was increased, whereas the wettability was both decreased. The rougher surface led to a worse wettability of aluminum alloy surface for these two specimens. In addition, the detected organo-functional group for the latter was also responsible for the increase of water contact angle on the aluminum alloy surface. Compared to the polished specimens, the tensile shear force of joints for sanded and silane-treated substrates averaged 1106.9N and 2959.4N respectively, which represents a respective increase of 1.2 times and 3.3 times than the polished state. Correspondingly, the tensile shear strength of joints was 6.3MPa and 16.8MPa, which was increased by 1.1 times and 2.9 times, respectively. The improved mechanical interlocking induced by increasing the surface roughness in both sanded and silane treated cases was considered to strengthen the bonding strength at the interface. Moreover, for the joints fabricated by aluminum alloy silane treated and CFRTP, the remarkable enhancement of joint strength was also related to the formation of silane coupling film on the aluminum alloy surface. The tensile shear properties of joints were also found to have a positive influence in increasing the adhesion amount of CFRTP on the fractured surface of aluminum alloy. Finally, the silane treatment was expected to play a significant role in joining aluminum alloy to CFRTP.
{"title":"Influence of Silane Treatment on the Joint Properties During Laser Joining of Aluminum Alloy to CFRTP","authors":"Ziwei Feng, Xueyan Zhang, Jianhui Su, Yifan Liu, Hongyun Zhao, Bo Chen, Xiaoguo Song, C. Tan","doi":"10.1115/msec2022-85298","DOIUrl":"https://doi.org/10.1115/msec2022-85298","url":null,"abstract":"Achieving strong joining of aluminum alloy to carbon fiber thermoplastic composites (CFRTP) using laser heating was an essential method to reduce the weight of structure and save energy. In current study, a silane coupling agent was adopted on the aluminum alloy surface to improve the bonding strength of the laser joining of aluminum alloy to CFRTP. Substrate characteristics of the aluminum alloy after treatment including surface morphology, surface roughness and wettability as well as chemical composition were then studied. For comparison, identical experiments were also performed for the polished and sanded aluminum alloy. Compared to the aluminum alloy polished, the surface roughness of aluminum alloy sanded and silane treated was increased, whereas the wettability was both decreased. The rougher surface led to a worse wettability of aluminum alloy surface for these two specimens. In addition, the detected organo-functional group for the latter was also responsible for the increase of water contact angle on the aluminum alloy surface. Compared to the polished specimens, the tensile shear force of joints for sanded and silane-treated substrates averaged 1106.9N and 2959.4N respectively, which represents a respective increase of 1.2 times and 3.3 times than the polished state. Correspondingly, the tensile shear strength of joints was 6.3MPa and 16.8MPa, which was increased by 1.1 times and 2.9 times, respectively. The improved mechanical interlocking induced by increasing the surface roughness in both sanded and silane treated cases was considered to strengthen the bonding strength at the interface. Moreover, for the joints fabricated by aluminum alloy silane treated and CFRTP, the remarkable enhancement of joint strength was also related to the formation of silane coupling film on the aluminum alloy surface. The tensile shear properties of joints were also found to have a positive influence in increasing the adhesion amount of CFRTP on the fractured surface of aluminum alloy. Finally, the silane treatment was expected to play a significant role in joining aluminum alloy to CFRTP.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73822926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shenghan Guo, Dali Wang, Jian Chen, Zhili Feng, W. Guo
With the advances of sensing technology, in-situ infrared thermal videos can be collected from Resistance Spot Welding (RSW) processes. Each video records the formulation process of a weld nugget. The nugget evolution creates a “temporal effect” across the frames, which can be leveraged for real-time, nondestructive evaluation (NDE) of the weld quality. Currently, quality prediction with imaging data mainly focuses on optical feature extraction with Convolutional Neural Network (CNN) but does not make the most of such temporal effect. In this study, pixels corresponding to critical locations on the weld nugget surface are extracted from a video to form multivariate time series (MTS). Multivariate Adaptive Regression Splines (MARS) is used in MTS processing to remove noisy signals related to uninformative frames. A Stacked Long Short-Term Memory (LSTM) model is developed to learn from the processed MTS and then predicts weld nugget size and thickness in real-time NDE. Results from a case study on RSW of Boron steel demonstrates the improvement in prediction accuracy and computational time with the proposed method, as compared to CNN-based weld quality prediction.
{"title":"Learning the Temporal Effect in Infrared Thermal Videos With Long Short-Term Memory for Quality Prediction in Resistance Spot Welding","authors":"Shenghan Guo, Dali Wang, Jian Chen, Zhili Feng, W. Guo","doi":"10.1115/msec2022-85422","DOIUrl":"https://doi.org/10.1115/msec2022-85422","url":null,"abstract":"\u0000 With the advances of sensing technology, in-situ infrared thermal videos can be collected from Resistance Spot Welding (RSW) processes. Each video records the formulation process of a weld nugget. The nugget evolution creates a “temporal effect” across the frames, which can be leveraged for real-time, nondestructive evaluation (NDE) of the weld quality. Currently, quality prediction with imaging data mainly focuses on optical feature extraction with Convolutional Neural Network (CNN) but does not make the most of such temporal effect. In this study, pixels corresponding to critical locations on the weld nugget surface are extracted from a video to form multivariate time series (MTS). Multivariate Adaptive Regression Splines (MARS) is used in MTS processing to remove noisy signals related to uninformative frames. A Stacked Long Short-Term Memory (LSTM) model is developed to learn from the processed MTS and then predicts weld nugget size and thickness in real-time NDE. Results from a case study on RSW of Boron steel demonstrates the improvement in prediction accuracy and computational time with the proposed method, as compared to CNN-based weld quality prediction.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85248946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hao Yang, Chun Zhao, Nana Shen, Wenzheng Liu, Lin Zhang
By integrating the Internet of Things, artificial intelligence, 5G, and other new-generation electronic information technologies, the fourth industrial Revolution represented by intelligent manufacturing and industrial internet is promoted, which is the era of comprehensive intelligent industry 4.0. As a key technology of the industrial Internet, the Internet of Things (IoT) connects intelligent manufacturing complex systems and machines with built-in sensors to the network for real-time data collection, transmission, processing, and feedback, to optimize device management and production efficiency. With the increasing number and variety of IoT devices, improving the scalability and maintainability of IoT systems is a challenging demand and requires continuous efforts. This paper proposes an architecture of IoT platform based on Model-Based Systems Engineering (MBSE). In this architecture, a modeling method based on Integrated Modeling language and a model-driven method for cloud-edge collaboration platform is further proposed. The standardization, readability, and reusability of the model are used to drive the device expansion and management. The characteristics of interaction behaviours between cloud and edges are extracted, and models of Holonomic System are built by an integrated modeling language, called X language. Block Definition Diagram (BDD) of X language is used to build the static models of IoT devices and drive the platform to manage the devices. State Machine Diagram (SMD) of X language is used to build the dynamic models of process between the edges and cloud, and drive the processes of the platform. Through experiments and analysis, the feasibility and effectiveness of the X-Language-driven IoT platform are verified.
通过物联网、人工智能、5G等新一代电子信息技术的融合，推动以智能制造、工业互联网为代表的第四次工业革命，即全面智能工业4.0时代。物联网(Internet of Things, IoT)是工业互联网的一项关键技术，它将智能制造复杂系统和内置传感器的机器连接到网络中，实现数据的实时采集、传输、处理和反馈，优化设备管理和生产效率。随着物联网设备数量和种类的不断增加，提高物联网系统的可扩展性和可维护性是一项具有挑战性的需求，需要不断努力。本文提出了一种基于模型系统工程(MBSE)的物联网平台架构。在该体系结构中，进一步提出了基于集成建模语言的建模方法和面向云边缘协作平台的模型驱动方法。该模型的标准化、易读性和可重用性是设备扩容和管理的基础。提取云与边缘交互行为特征，采用集成建模语言X语言构建完整系统模型。使用X语言的块定义图(Block Definition Diagram, BDD)构建物联网设备的静态模型，驱动平台对设备进行管理。利用X语言的状态机图(SMD)建立边缘与云之间的过程动态模型，驱动平台的过程。通过实验和分析，验证了x语言驱动物联网平台的可行性和有效性。
{"title":"Modeling and Model-Driven of Holonomic System Based on MBSE: a Case of Internet of Things Platform","authors":"Hao Yang, Chun Zhao, Nana Shen, Wenzheng Liu, Lin Zhang","doi":"10.1115/msec2022-85135","DOIUrl":"https://doi.org/10.1115/msec2022-85135","url":null,"abstract":"\u0000 By integrating the Internet of Things, artificial intelligence, 5G, and other new-generation electronic information technologies, the fourth industrial Revolution represented by intelligent manufacturing and industrial internet is promoted, which is the era of comprehensive intelligent industry 4.0. As a key technology of the industrial Internet, the Internet of Things (IoT) connects intelligent manufacturing complex systems and machines with built-in sensors to the network for real-time data collection, transmission, processing, and feedback, to optimize device management and production efficiency. With the increasing number and variety of IoT devices, improving the scalability and maintainability of IoT systems is a challenging demand and requires continuous efforts. This paper proposes an architecture of IoT platform based on Model-Based Systems Engineering (MBSE). In this architecture, a modeling method based on Integrated Modeling language and a model-driven method for cloud-edge collaboration platform is further proposed. The standardization, readability, and reusability of the model are used to drive the device expansion and management. The characteristics of interaction behaviours between cloud and edges are extracted, and models of Holonomic System are built by an integrated modeling language, called X language. Block Definition Diagram (BDD) of X language is used to build the static models of IoT devices and drive the platform to manage the devices. State Machine Diagram (SMD) of X language is used to build the dynamic models of process between the edges and cloud, and drive the processes of the platform. Through experiments and analysis, the feasibility and effectiveness of the X-Language-driven IoT platform are verified.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80426515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tensile testing is the most prevalent method for characterizing the mechanical properties of additively manufactured (AM) materials. During qualification of metallic AM properties, near-net AM parts are often machined prior to mechanical testing. The aim of this study is to understand the influence of net-shaped tensile coupons without post-AM machining on the accuracy of bulk material properties. The motivation for this study lies in: (1) reducing the qualification time and costs by (2) formulating and validating a correction factor to estimate bulk AM properties from mechanical testing of as-AM coupons. This research focused on the tensile testing of Laser Powder Bed Fusion (LPBF) produced Inconel 718 to isolate the effects of as-AM surface roughness. Six different surface conditions were produced by varying two different laser processing conditions, with and without contour laser scans. Specimens (n = 5 per condition) were tested in both net-shape and post-AM machined conditions. Surface roughness was analyzed using both stylus contact profilometry and micro-computed tomography (micro-CT) non-contact analysis. ANOVA analysis was performed to derive inference on processing conditions and resulting mechanical properties. It was observed that the measurement error in gauge diameter primarily accounts for variability in mechanical properties between machined and net-shape coupons, specifically Ultimate Tensile Strength (UTS). This study presents a methodology to determine corrected gauge diameter based on depth of surface roughness. Findings from this study will enable net-shape tensile data to be compared against machined data for accurately predicting the strength of parts with as-AM surfaces. By accounting for surface roughness depth, tensile strength of net-shape AM coupons was within 1% accuracy to that of machined AM coupons.
{"title":"Net-Shape Tensile Specimens as Representatives of Material Properties of Metal Additive Manufacturing: Evaluation and Correction Factor","authors":"Nicholas Bass, S. Jalui, G. Manogharan","doi":"10.1115/msec2022-85310","DOIUrl":"https://doi.org/10.1115/msec2022-85310","url":null,"abstract":"\u0000 Tensile testing is the most prevalent method for characterizing the mechanical properties of additively manufactured (AM) materials. During qualification of metallic AM properties, near-net AM parts are often machined prior to mechanical testing. The aim of this study is to understand the influence of net-shaped tensile coupons without post-AM machining on the accuracy of bulk material properties. The motivation for this study lies in: (1) reducing the qualification time and costs by (2) formulating and validating a correction factor to estimate bulk AM properties from mechanical testing of as-AM coupons. This research focused on the tensile testing of Laser Powder Bed Fusion (LPBF) produced Inconel 718 to isolate the effects of as-AM surface roughness. Six different surface conditions were produced by varying two different laser processing conditions, with and without contour laser scans. Specimens (n = 5 per condition) were tested in both net-shape and post-AM machined conditions. Surface roughness was analyzed using both stylus contact profilometry and micro-computed tomography (micro-CT) non-contact analysis. ANOVA analysis was performed to derive inference on processing conditions and resulting mechanical properties. It was observed that the measurement error in gauge diameter primarily accounts for variability in mechanical properties between machined and net-shape coupons, specifically Ultimate Tensile Strength (UTS). This study presents a methodology to determine corrected gauge diameter based on depth of surface roughness. Findings from this study will enable net-shape tensile data to be compared against machined data for accurately predicting the strength of parts with as-AM surfaces. By accounting for surface roughness depth, tensile strength of net-shape AM coupons was within 1% accuracy to that of machined AM coupons.","PeriodicalId":23676,"journal":{"name":"Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82995121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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