Purvee Bhatia, Yang Liu, Sohan Nagaraj, Varshita Achanta, Bharat Pulaparthi, N. Diaz-Elsayed
This paper proposes a multi-criteria decision-making analysis of the alternatives for smart and sustainable machining processes to provide visibility and clarity on the factors that can affect production performance. Identification of such parameters can aid in the adoption of smart manufacturing technologies. The framework developed for decision making utilizes fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to compare alternative machining scenarios. Machining with Tool Condition Monitoring (TCM) and machining with Computational Fluid Dynamics (CFD) for modeling ambient conditions are analyzed for their application and form use cases in the framework. Feasibility of TCM via vibration analysis when milling 17-4 Stainless Steel is investigated and a positive trend is observed between the surface roughness of the work piece and the cutting tool vibration at time steps where tool wear is predicted. Thus, a viable low-cost solution for TCM is available. The ambient conditions of the machining environment have been modelled with CFD to study temperature and airflow gradients. The CFD model can be used to reduce thermal errors for precision machining and enhance operator efficiency. The result from the decision-making framework shows a clear preference for smart machining alternatives as compared to the conventional machining. In all, machining with TCM and CFD is found to be the most preferred.
{"title":"Data-Driven Multi-Criteria Decision-Making for Smart and Sustainable Machining","authors":"Purvee Bhatia, Yang Liu, Sohan Nagaraj, Varshita Achanta, Bharat Pulaparthi, N. Diaz-Elsayed","doi":"10.1115/imece2021-73085","DOIUrl":"https://doi.org/10.1115/imece2021-73085","url":null,"abstract":"\u0000 This paper proposes a multi-criteria decision-making analysis of the alternatives for smart and sustainable machining processes to provide visibility and clarity on the factors that can affect production performance. Identification of such parameters can aid in the adoption of smart manufacturing technologies. The framework developed for decision making utilizes fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to compare alternative machining scenarios. Machining with Tool Condition Monitoring (TCM) and machining with Computational Fluid Dynamics (CFD) for modeling ambient conditions are analyzed for their application and form use cases in the framework. Feasibility of TCM via vibration analysis when milling 17-4 Stainless Steel is investigated and a positive trend is observed between the surface roughness of the work piece and the cutting tool vibration at time steps where tool wear is predicted. Thus, a viable low-cost solution for TCM is available. The ambient conditions of the machining environment have been modelled with CFD to study temperature and airflow gradients. The CFD model can be used to reduce thermal errors for precision machining and enhance operator efficiency. The result from the decision-making framework shows a clear preference for smart machining alternatives as compared to the conventional machining. In all, machining with TCM and CFD is found to be the most preferred.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100770","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}
Md Shahjahan Hossain, Ashley Pliego, Jinsun Lee, H. Taheri
The use of metal additive manufacturing (AM) becomes increasingly popular in many industries. AM can create functional parts with lower cost and lead time than the subtractive manufacturing processes. In AM technology, flaws or defects can be present due to variations in the manufacturing process or quality of raw materials, so AM technologies must still be developed to ensure acceptable and reliable quality of the product. Ensuring the high quality of the AM is crucial for safety in critical applications such as the aerospace industry. Various destructive and nondestructive techniques have been used for testing the AM components and their properties evaluation. The use of various nondestructive testing (NDT) techniques is becoming popular for defect identification and characterization of the parts, and still, more techniques need to be developed for better performance and higher optimization. In this study, wire-arc AM (WAAM) parts as-build and heat-treated components have been characterized for nanomechanical properties and finding possible defects created during the fabrication process. Nanoindentation, surface profilometry, and SEM (Scanning Electron Microscope) were used to characterize various wire-arc additive manufactured Titanium alloy (Ti-6Al-4V) samples. The samples were also being tested for material characteristics at different deposition locations and as-deposited versus heat-treated conditions.
{"title":"Characterization of Wire-Arc Additively Manufactured (WAAM) of Titanium Alloy (Ti-6Al-4V) for Nanomechanical Properties","authors":"Md Shahjahan Hossain, Ashley Pliego, Jinsun Lee, H. Taheri","doi":"10.1115/imece2021-69673","DOIUrl":"https://doi.org/10.1115/imece2021-69673","url":null,"abstract":"\u0000 The use of metal additive manufacturing (AM) becomes increasingly popular in many industries. AM can create functional parts with lower cost and lead time than the subtractive manufacturing processes. In AM technology, flaws or defects can be present due to variations in the manufacturing process or quality of raw materials, so AM technologies must still be developed to ensure acceptable and reliable quality of the product. Ensuring the high quality of the AM is crucial for safety in critical applications such as the aerospace industry. Various destructive and nondestructive techniques have been used for testing the AM components and their properties evaluation. The use of various nondestructive testing (NDT) techniques is becoming popular for defect identification and characterization of the parts, and still, more techniques need to be developed for better performance and higher optimization. In this study, wire-arc AM (WAAM) parts as-build and heat-treated components have been characterized for nanomechanical properties and finding possible defects created during the fabrication process. Nanoindentation, surface profilometry, and SEM (Scanning Electron Microscope) were used to characterize various wire-arc additive manufactured Titanium alloy (Ti-6Al-4V) samples. The samples were also being tested for material characteristics at different deposition locations and as-deposited versus heat-treated conditions.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115176731","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}
Jose O. Savazzi, S. Shiki, G. Barbosa, David A. Guerra-Zubiaga
The development of materials and methods used in the aircraft manufacturing industry has been advancing in order to provide a reliable and light aircraft. The use of composite materials becomes indispensable, meanwhile, the processing of this kind of material must be studied to obtain the higher manufacturing efficiency and the best quality of the final product. Industry 4.0 concepts as internet of things, cloud computing and others can be used to fulfil these demands. In this sense, this study aims to create a remaining useful life prediction model for the tools used on the machining of composite materials with robotic manipulators. This task is performed by monitoring and analyzing the mechanical vibrations of the motor assembly and the cutting tool, then reducing the consumption of this material and ensuring the quality and surface integrity of the finished parts. The self-awareness of the process is improved by combining signal processing algorithms and statistical techniques to assist the constant monitoring of the tool wear. In this sense, a digital model is constantly updated aiming the optimization of the cutting process. In the conclusions of the paper, the advantages and drawbacks of the proposed methodology are presented.
{"title":"Tool Remaining Useful Life Prediction in Robotic Machining of Composite Materials Based on Mechanical Vibrations","authors":"Jose O. Savazzi, S. Shiki, G. Barbosa, David A. Guerra-Zubiaga","doi":"10.1115/imece2021-70682","DOIUrl":"https://doi.org/10.1115/imece2021-70682","url":null,"abstract":"\u0000 The development of materials and methods used in the aircraft manufacturing industry has been advancing in order to provide a reliable and light aircraft. The use of composite materials becomes indispensable, meanwhile, the processing of this kind of material must be studied to obtain the higher manufacturing efficiency and the best quality of the final product. Industry 4.0 concepts as internet of things, cloud computing and others can be used to fulfil these demands. In this sense, this study aims to create a remaining useful life prediction model for the tools used on the machining of composite materials with robotic manipulators. This task is performed by monitoring and analyzing the mechanical vibrations of the motor assembly and the cutting tool, then reducing the consumption of this material and ensuring the quality and surface integrity of the finished parts. The self-awareness of the process is improved by combining signal processing algorithms and statistical techniques to assist the constant monitoring of the tool wear. In this sense, a digital model is constantly updated aiming the optimization of the cutting process. In the conclusions of the paper, the advantages and drawbacks of the proposed methodology are presented.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115158619","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}
V. Kuts, Yevhen Bondarenko, Marietta Gavriljuk, Andriy Paryshev, S. Jegorov, Simone Pizzagall, T. Otto
Industry 4.0 concept enables connecting a multitude of equipment to computer simulations through IoT and virtual commissioning, but using conventional interfaces for each separate piece of equipment for control and maintenance of Digital Twins is not always an optimal solution. Industrial Digital Twins software toolkits usually consist of simulation or offline programming tools. It can even connect real machines and controllers and sensors to feed a simulation with actual production data and later analyze it. Moreover, Virtual Reality (VR) and Augmented Reality (AR) are used in different ways for monitoring and design purposes. However, there are many software tools for the simulation and re-programming of robots on the market already, but those are a limited number of software that combine all these features, and all of those send data only in one way, not allowing to re-program machines from the simulations. The related research aims to build a modular framework for designing and deploying Digital Twins of industrial equipment (i.e., robots, manufacturing lines), focusing on online connectivity for monitoring and control. A developed use-case solution enables one to operate the equipment in VR/AR/Personal Computer (PC) and mobile interfaces from any point globally while receiving real-time feedback and state information of the machinery equipment. Gamified multi-platform interfaces allow for more intuitive interactions with Digital Twins, providing a real-scale model of the real device, augmented by spatial UIs, actuated physical elements, and gesture tracking. The introduced solution can control and simulate any aspect of the production line without limitation of brand or type of the machine and being managed and self-learning independently by exploiting Machine Learning algorithms. Moreover, various interfaces such as PC, mobile, VR, and AR give an unlimited number of options for interactions with your manufacturing shop floor both offline and online. Furthermore, when it comes to manufacturing floor data monitoring, all gathered data is being used for statistical analysis, and in a later phase, predictive maintenance functions are enabled based on it. However, the research scope is broader; this particular research paper introduces a use-case interface on a mobile platform, monitoring and controlling the production unit of three various industrial- and three various mobile robots, partially supported by data monitoring sensors. The solution is developed using the game engine Unity3D, Robot Operation System (ROS), and MQTT for connectivity. Thus, developed is a universal modular Digital Twin all-in-one software platform for users and operators, enabling full control over the manufacturing system unit.
{"title":"Digital Twin: Universal User Interface for Online Management of the Manufacturing System","authors":"V. Kuts, Yevhen Bondarenko, Marietta Gavriljuk, Andriy Paryshev, S. Jegorov, Simone Pizzagall, T. Otto","doi":"10.1115/imece2021-69092","DOIUrl":"https://doi.org/10.1115/imece2021-69092","url":null,"abstract":"\u0000 Industry 4.0 concept enables connecting a multitude of equipment to computer simulations through IoT and virtual commissioning, but using conventional interfaces for each separate piece of equipment for control and maintenance of Digital Twins is not always an optimal solution. Industrial Digital Twins software toolkits usually consist of simulation or offline programming tools. It can even connect real machines and controllers and sensors to feed a simulation with actual production data and later analyze it. Moreover, Virtual Reality (VR) and Augmented Reality (AR) are used in different ways for monitoring and design purposes. However, there are many software tools for the simulation and re-programming of robots on the market already, but those are a limited number of software that combine all these features, and all of those send data only in one way, not allowing to re-program machines from the simulations. The related research aims to build a modular framework for designing and deploying Digital Twins of industrial equipment (i.e., robots, manufacturing lines), focusing on online connectivity for monitoring and control. A developed use-case solution enables one to operate the equipment in VR/AR/Personal Computer (PC) and mobile interfaces from any point globally while receiving real-time feedback and state information of the machinery equipment. Gamified multi-platform interfaces allow for more intuitive interactions with Digital Twins, providing a real-scale model of the real device, augmented by spatial UIs, actuated physical elements, and gesture tracking.\u0000 The introduced solution can control and simulate any aspect of the production line without limitation of brand or type of the machine and being managed and self-learning independently by exploiting Machine Learning algorithms. Moreover, various interfaces such as PC, mobile, VR, and AR give an unlimited number of options for interactions with your manufacturing shop floor both offline and online. Furthermore, when it comes to manufacturing floor data monitoring, all gathered data is being used for statistical analysis, and in a later phase, predictive maintenance functions are enabled based on it.\u0000 However, the research scope is broader; this particular research paper introduces a use-case interface on a mobile platform, monitoring and controlling the production unit of three various industrial- and three various mobile robots, partially supported by data monitoring sensors. The solution is developed using the game engine Unity3D, Robot Operation System (ROS), and MQTT for connectivity. Thus, developed is a universal modular Digital Twin all-in-one software platform for users and operators, enabling full control over the manufacturing system unit.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129539637","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}
David A. Guerra-Zubiaga, Grayson McMichael, D. Segura-Velandia, Maria Aslam, Seung-Woo Yim, Zack Anderson, Y. Goh
Industry 4.0 is the next phase in the industrial revolution, and it is considered a key factor for advanced process control. This paper is focused on Industry 4.0 aspects to support better process control through a Manufacturing Execution System (MES). Some intelligent manufacturing decision systems require complex infrastructures that make advanced feedback control possible. The motivation of this paper is exploring the paradigms such as Industrial Internet of Things (IIoT), Big Data collection, Cloud Manufacturing (CM), and Machine Learning (ML) to provide better manufacturing support decisions in process control. This paper proposes a new approach at MES providing more intelligent process control through the integration of IIoT, CM, and ML. This research effort created a Process Control Training Bench (PCTB) as experimental infrastructure to implement a process control system incorporating Industry 4.0 trends and applying ML to analyze and predict anomalies.
{"title":"Intelligent Process Control Following Industry 4.0 Trends","authors":"David A. Guerra-Zubiaga, Grayson McMichael, D. Segura-Velandia, Maria Aslam, Seung-Woo Yim, Zack Anderson, Y. Goh","doi":"10.1115/imece2021-68686","DOIUrl":"https://doi.org/10.1115/imece2021-68686","url":null,"abstract":"\u0000 Industry 4.0 is the next phase in the industrial revolution, and it is considered a key factor for advanced process control. This paper is focused on Industry 4.0 aspects to support better process control through a Manufacturing Execution System (MES). Some intelligent manufacturing decision systems require complex infrastructures that make advanced feedback control possible. The motivation of this paper is exploring the paradigms such as Industrial Internet of Things (IIoT), Big Data collection, Cloud Manufacturing (CM), and Machine Learning (ML) to provide better manufacturing support decisions in process control. This paper proposes a new approach at MES providing more intelligent process control through the integration of IIoT, CM, and ML. This research effort created a Process Control Training Bench (PCTB) as experimental infrastructure to implement a process control system incorporating Industry 4.0 trends and applying ML to analyze and predict anomalies.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"75 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922961","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}
This study investigates the effect of machining parameters correlating to the quality and precision of machined parts. The main focus is given to coordinate measuring machine (CMM) features, dimensional accuracy and surface roughness. Samples of three hardness materials were milled according to a standard design while varying machining parameters. The varied parameters were the type of milling machine used, feed rate and spindle speed. A CMM of 0.0001mm precision was used to measure the dimensions of the machined parts and analysis of variance (ANOVA) was used as the analysis to study the impact of each machining parameter on the quality and accuracy of the machined specimen. The study revealed that spindle speed of the milling machine, hardness of the material and the machine automation type is the factor that has a significant effect on the surface roughness of the machined features and hardness, spindle speed and a combination of hardness plus spindle speed had a significant effect on the dimensional accuracy of the machined features. The significant difference in data collection using the three CMM touch probes available was also studied, which revealed that the effect of changing probe diameter does not have any significant effect on a standard design feature when collecting data.
{"title":"Influence of Cutting Conditions on Dimensional Integrity","authors":"Sumesh Narayan, Abhishek Kaushal Kumar, Aruf Ali, Kabir Mamun","doi":"10.1115/imece2021-66625","DOIUrl":"https://doi.org/10.1115/imece2021-66625","url":null,"abstract":"\u0000 This study investigates the effect of machining parameters correlating to the quality and precision of machined parts. The main focus is given to coordinate measuring machine (CMM) features, dimensional accuracy and surface roughness. Samples of three hardness materials were milled according to a standard design while varying machining parameters. The varied parameters were the type of milling machine used, feed rate and spindle speed. A CMM of 0.0001mm precision was used to measure the dimensions of the machined parts and analysis of variance (ANOVA) was used as the analysis to study the impact of each machining parameter on the quality and accuracy of the machined specimen. The study revealed that spindle speed of the milling machine, hardness of the material and the machine automation type is the factor that has a significant effect on the surface roughness of the machined features and hardness, spindle speed and a combination of hardness plus spindle speed had a significant effect on the dimensional accuracy of the machined features. The significant difference in data collection using the three CMM touch probes available was also studied, which revealed that the effect of changing probe diameter does not have any significant effect on a standard design feature when collecting data.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116395075","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}
A novel fiber production system established using principles of the dip drawing process is outlined in this paper, known as track spinning (TS). This system can produce micro- and nanofibers from polymer solutions for use in a variety of applications including filtration and biomedical devices. The system features automated tracks operated by a programmable motion controller in combination with stepper motors to consistently produce nanofibers. Utilizing a lignin-based solution, nanofibers of approximately 700–800 nanometers diameter have been successfully achieved and replicated with this device. TS offers wider compatibility with various solutions, in addition to scalability to fit the needs of the application or product. The TS device could open the doors to wide scale, automated nanofiber production.
{"title":"Scalable Fiber Dip Drawing Method Using Automated Tracks","authors":"Abigail Heinz, Dave Jao, V. Beachley","doi":"10.1115/imece2021-69153","DOIUrl":"https://doi.org/10.1115/imece2021-69153","url":null,"abstract":"\u0000 A novel fiber production system established using principles of the dip drawing process is outlined in this paper, known as track spinning (TS). This system can produce micro- and nanofibers from polymer solutions for use in a variety of applications including filtration and biomedical devices. The system features automated tracks operated by a programmable motion controller in combination with stepper motors to consistently produce nanofibers. Utilizing a lignin-based solution, nanofibers of approximately 700–800 nanometers diameter have been successfully achieved and replicated with this device. TS offers wider compatibility with various solutions, in addition to scalability to fit the needs of the application or product. The TS device could open the doors to wide scale, automated nanofiber production.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"482 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116526185","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 front matter for this proceedings is available by clicking on the PDF icon.
通过点击PDF图标可获得本次会议的主题。
{"title":"IMECE2021 Front Matter","authors":"","doi":"10.1115/imece2021-fm2b","DOIUrl":"https://doi.org/10.1115/imece2021-fm2b","url":null,"abstract":"\u0000 The front matter for this proceedings is available by clicking on the PDF icon.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"100 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128179356","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}
David A. Guerra-Zubiaga, Corey Morton, Derrick Stacey, Virginia Peach, C. Ham, Diego Escobar Escobar, Noah Hitchcock
Intelligent automation could be applied to continuous processes and discrete manufacturing. This research is presenting a new approach exploring intelligent automation on discrete manufacturing. A degree of smart integrated manufacturing is presented according to industry 4.0 trends. In this research a digital twin is explored incorporating some new manufacturing paradigms such as Industrial Internet of Things (IIoT), Cloud Manufacturing (CM) and Machine Learning (ML) in the creation of new intelligent manufacturing systems. A virtual simulation of B&R’s SuperTrak with magnetic shuttle technology is presented as a digital twin concept and specific aspects of IIoT, CM and ML are connected to extend smart manufacturing aspects providing the intelligent automation on discrete manufacturing. The aim of this research is to present a new approach to develop an intelligent manufacturing system using virtual tools.
{"title":"A New Approach to Develop an Intelligent Manufacturing System Using Virtual Tools","authors":"David A. Guerra-Zubiaga, Corey Morton, Derrick Stacey, Virginia Peach, C. Ham, Diego Escobar Escobar, Noah Hitchcock","doi":"10.1115/imece2021-71546","DOIUrl":"https://doi.org/10.1115/imece2021-71546","url":null,"abstract":"\u0000 Intelligent automation could be applied to continuous processes and discrete manufacturing. This research is presenting a new approach exploring intelligent automation on discrete manufacturing. A degree of smart integrated manufacturing is presented according to industry 4.0 trends. In this research a digital twin is explored incorporating some new manufacturing paradigms such as Industrial Internet of Things (IIoT), Cloud Manufacturing (CM) and Machine Learning (ML) in the creation of new intelligent manufacturing systems. A virtual simulation of B&R’s SuperTrak with magnetic shuttle technology is presented as a digital twin concept and specific aspects of IIoT, CM and ML are connected to extend smart manufacturing aspects providing the intelligent automation on discrete manufacturing. The aim of this research is to present a new approach to develop an intelligent manufacturing system using virtual tools.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133395312","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}
Tim Brix Nerenst, Martin Ebro, Morten Nielsen, K. Bhadani, Gauti Asbjörnsson, T. Eifler, Kim Lau Nielsen
A new medical device can take years to develop from early concept to product launch. The long development process can be attributed to the severe consequences for the patient if the device malfunctions. Three approaches are often combined to mitigate risks: rigorous simulation and modeling, physical test programs, and Failure Mode Effect Analysis (FMEA) — all of which are time-consuming. Physical test programs are often carried out on prototype components from the same batch and, therefore, limited in revealing the actual distribution of performance. The risk probabilities are subsequently based on educated guesses. Furthermore, simulation and modeling are usually performed on nominal geometry — not accounting for variation — and only provide a safety factor against failure. The traditional use of safety factors in structural analysis versus the probabilistic approach to risk management presents an obvious misfit. Therefore, these three approaches are not ideal for addressing the two key questions that the design engineer has: 1) How often will the design fail, and 2) How should the design be changed to improve robustness and failure rates. The present work builds upon the existing Robust and Reliability-Based Design Optimization (R2BDO) and adjusts it to address the key questions above using finite element analysis. The key feature of the new framework is the focus on minimal use of computational resources while being able to screen feasible design concepts early in the embodiment phase and subsequently optimize their probabilistic performance. A case study in collaboration with a medical design and manufacturing company demonstrates the new framework. The case study includes FEA contact modeling between two plastic molded components with twelve geometrical variables. The optimization focuses on minimizing the failure rate (and improving design robustness) concerning three constraint functions (contact pressure, strain, torque). The study finds that the new framework achieves significant improvements to the component’s performance function (failure rate) with minimal computational resources.
{"title":"Probabilistic Performance Evaluation and Optimization of Medical Plastic Moulded Components Subject to Large Scale Production","authors":"Tim Brix Nerenst, Martin Ebro, Morten Nielsen, K. Bhadani, Gauti Asbjörnsson, T. Eifler, Kim Lau Nielsen","doi":"10.1115/imece2021-68918","DOIUrl":"https://doi.org/10.1115/imece2021-68918","url":null,"abstract":"\u0000 A new medical device can take years to develop from early concept to product launch. The long development process can be attributed to the severe consequences for the patient if the device malfunctions. Three approaches are often combined to mitigate risks: rigorous simulation and modeling, physical test programs, and Failure Mode Effect Analysis (FMEA) — all of which are time-consuming. Physical test programs are often carried out on prototype components from the same batch and, therefore, limited in revealing the actual distribution of performance. The risk probabilities are subsequently based on educated guesses. Furthermore, simulation and modeling are usually performed on nominal geometry — not accounting for variation — and only provide a safety factor against failure. The traditional use of safety factors in structural analysis versus the probabilistic approach to risk management presents an obvious misfit. Therefore, these three approaches are not ideal for addressing the two key questions that the design engineer has: 1) How often will the design fail, and 2) How should the design be changed to improve robustness and failure rates. The present work builds upon the existing Robust and Reliability-Based Design Optimization (R2BDO) and adjusts it to address the key questions above using finite element analysis. The key feature of the new framework is the focus on minimal use of computational resources while being able to screen feasible design concepts early in the embodiment phase and subsequently optimize their probabilistic performance. A case study in collaboration with a medical design and manufacturing company demonstrates the new framework. The case study includes FEA contact modeling between two plastic molded components with twelve geometrical variables. The optimization focuses on minimizing the failure rate (and improving design robustness) concerning three constraint functions (contact pressure, strain, torque). The study finds that the new framework achieves significant improvements to the component’s performance function (failure rate) with minimal computational resources.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128585957","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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