W. Hoover, David A. Guerra-Zubiaga, Jeremy Banta, Kevin Wandene, Kaleb Key, Germanico Gonzalez-Badillo
Current trends indicate that the manufacturing industry is moving toward implementing Industry 4.0 concepts in search of improved adaptability, efficiency, sustainability, and advanced technological implementation. Some of these new technologies include virtual process simulation, automation, machine learning technologies, and the use of IIoT to innovate solutions. Researchers are focusing on ways to improve the rate and economy of implementing Industry 4.0 concepts in current manufacturing processes. This paper focuses on the implementation of a combination of specific industry 4.0 concepts in a lab environment. There will also be a case study where this research will be applied, and the results discussed. Digital Twins is also a proposed component of the research case study that is implemented using Siemens PLM Tecnomatix tool. Future work is to improve the efficiency of the manufacturing, pick-and-place operation using Deep Reinforcement learning.
{"title":"Industry 4.0 Trends in Intelligent Manufacturing Automation Exploring Machine Learning","authors":"W. Hoover, David A. Guerra-Zubiaga, Jeremy Banta, Kevin Wandene, Kaleb Key, Germanico Gonzalez-Badillo","doi":"10.1115/imece2022-96092","DOIUrl":"https://doi.org/10.1115/imece2022-96092","url":null,"abstract":"\u0000 Current trends indicate that the manufacturing industry is moving toward implementing Industry 4.0 concepts in search of improved adaptability, efficiency, sustainability, and advanced technological implementation. Some of these new technologies include virtual process simulation, automation, machine learning technologies, and the use of IIoT to innovate solutions.\u0000 Researchers are focusing on ways to improve the rate and economy of implementing Industry 4.0 concepts in current manufacturing processes. This paper focuses on the implementation of a combination of specific industry 4.0 concepts in a lab environment. There will also be a case study where this research will be applied, and the results discussed. Digital Twins is also a proposed component of the research case study that is implemented using Siemens PLM Tecnomatix tool. Future work is to improve the efficiency of the manufacturing, pick-and-place operation using Deep Reinforcement learning.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123624017","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}
Saleh Alsaleh, A. Tepljakov, M. Tamre, V. Kuts, E. Petlenkov
Hybrid mobile robots are able to function in a number of different modes of locomotion, which increases their capacity to overcome challenges and makes them appropriate for a wide range of applications. To be able to develop navigation techniques that make use of these improved capabilities, one must first have a solid grasp of the constraints imposed by each of those different modalities of locomotion. In this paper, we present a data-driven approach for evaluating the robots’ locomotion modes. To do this, we formalize the problem as a reinforcement learning task that is applied to a digital twin simulation of the mobile robot. The proposed method is demonstrated through the use of a case study that examines the capabilities of hybrid wheel-on-leg robot locomotion modes in terms of speed, slope ascent, and step obstacle climbing. First, a comprehensive explanation of the process of creating the digital twin of the mobile robot through the use of the Unity gaming engine is presented. Second, a description of the construction of three test environments is provided so that the aforementioned capabilities of the robot can be evaluated. In the end, Reinforcement Learning is used to evaluate the two types of locomotion that the mobile robot can utilize in each of these different environments. Corresponding simulations are conducted in the virtual environment and the results are analyzed.
{"title":"Digital Twin Simulations Based Reinforcement Learning for Navigation and Control of a Wheel-on-Leg Mobile Robot","authors":"Saleh Alsaleh, A. Tepljakov, M. Tamre, V. Kuts, E. Petlenkov","doi":"10.1115/imece2022-95411","DOIUrl":"https://doi.org/10.1115/imece2022-95411","url":null,"abstract":"\u0000 Hybrid mobile robots are able to function in a number of different modes of locomotion, which increases their capacity to overcome challenges and makes them appropriate for a wide range of applications. To be able to develop navigation techniques that make use of these improved capabilities, one must first have a solid grasp of the constraints imposed by each of those different modalities of locomotion. In this paper, we present a data-driven approach for evaluating the robots’ locomotion modes. To do this, we formalize the problem as a reinforcement learning task that is applied to a digital twin simulation of the mobile robot. The proposed method is demonstrated through the use of a case study that examines the capabilities of hybrid wheel-on-leg robot locomotion modes in terms of speed, slope ascent, and step obstacle climbing. First, a comprehensive explanation of the process of creating the digital twin of the mobile robot through the use of the Unity gaming engine is presented. Second, a description of the construction of three test environments is provided so that the aforementioned capabilities of the robot can be evaluated. In the end, Reinforcement Learning is used to evaluate the two types of locomotion that the mobile robot can utilize in each of these different environments. Corresponding simulations are conducted in the virtual environment and the results are analyzed.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124641309","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}
Sk. Yasin Habib Abir, S. Rahi, M. Hasan, T. C. Paul
Embedded cracks of different type of samples of mild steel gas pipe API 1104 were identified and sized using Non-destructive evaluation. A procedure to create these embedded cracks inside the sample was developed. The UH-F1000knX hydraulic universal testing machine was used for bending and arc welding was done for bidding. 21 samples of different thickness and crack size were physically made. They were machined in shaper and grinding machine. A special reference slit of known deformity size was made to calibrate the ultrasonic testing machine’s sensitivity. EPOCH-650 ultrasonic testing machine was used to identify and measure these defects. Both normal and shear waves were used. Contact probe was used. Using the fabricated reference slit 4 different curves of deformity size (mm) vs Eco amplitude (%) were developed. Each of them provided a 3rd-degree polynomial equation. Amplitude Comparison Technique (ACT) and Amplitude Distance Differential Technique (ADDT) was used to size the deformities. For each shear wave an eco-amplitude (%) vs constant (c) curve was developed. Validation was done by destructive testing. For identifying deformities, around 95% accuracy was obtained and for sizing the deformities almost 60% accuracy was obtained. 35° and 45° shear waves gave 72% and 43% accurate data respectively.
{"title":"Non-Destructive Evaluation of Embedded Cracks in Metal by Ultrasound: Experimental Investigation","authors":"Sk. Yasin Habib Abir, S. Rahi, M. Hasan, T. C. Paul","doi":"10.1115/imece2022-94929","DOIUrl":"https://doi.org/10.1115/imece2022-94929","url":null,"abstract":"\u0000 Embedded cracks of different type of samples of mild steel gas pipe API 1104 were identified and sized using Non-destructive evaluation. A procedure to create these embedded cracks inside the sample was developed. The UH-F1000knX hydraulic universal testing machine was used for bending and arc welding was done for bidding. 21 samples of different thickness and crack size were physically made. They were machined in shaper and grinding machine. A special reference slit of known deformity size was made to calibrate the ultrasonic testing machine’s sensitivity. EPOCH-650 ultrasonic testing machine was used to identify and measure these defects. Both normal and shear waves were used. Contact probe was used. Using the fabricated reference slit 4 different curves of deformity size (mm) vs Eco amplitude (%) were developed. Each of them provided a 3rd-degree polynomial equation. Amplitude Comparison Technique (ACT) and Amplitude Distance Differential Technique (ADDT) was used to size the deformities. For each shear wave an eco-amplitude (%) vs constant (c) curve was developed. Validation was done by destructive testing. For identifying deformities, around 95% accuracy was obtained and for sizing the deformities almost 60% accuracy was obtained. 35° and 45° shear waves gave 72% and 43% accurate data respectively.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117133314","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}
H. Abaza, Alan L. Clark, Aaron Schwartz, Henry J. Durce, David A. Guerra-Zubiaga
This research investigates using AI robotics in automating wooden residential construction. Residential building construction still depends on manual labor. Automation is used to construct certain buildings such as wood trusses, cabinets, doors, windows, and mechanical systems. However, there is a great need for automating the assembly of the building construction. This research builds a framework on how robotics can automate the construction of significant building components and assemble them in the field. In this approach, walls, roofs, and floors assemble in the area. Several attempts have been made to use robots for wood framing. However, these automation attempts focused only on the structural parts of the wooden construction. The scope of this research includes using multiple robots with conveying belts to incorporate the assembly of the main building components, including the wooden framing, exterior sheathing and siding, electric wiring and trim, plumbing pipes, thermal insulation, vapor barrier, waterproofing, trim, windows, and doors, and first coat painting. Building assemblies will be transported to the field and fit together. The system will integrate mass customization strategies which includes Product Family Architecture and Personalization Design. This approach will improve quality assurance, reduce labor costs, reduce construction time, and reduce material waste. As a proof of concept, the research simulates the use of a robot to assemble walls components. The results showed that using AI Robotics to automate wooden residential construction is possible and practical. The advancement in AI robots overcame the inconsistencies in building materials and achieved the goals of this project.
{"title":"Industrializing Residential Construction Using Artificial Intelligent (AI) Robotics","authors":"H. Abaza, Alan L. Clark, Aaron Schwartz, Henry J. Durce, David A. Guerra-Zubiaga","doi":"10.1115/imece2022-96675","DOIUrl":"https://doi.org/10.1115/imece2022-96675","url":null,"abstract":"\u0000 This research investigates using AI robotics in automating wooden residential construction. Residential building construction still depends on manual labor. Automation is used to construct certain buildings such as wood trusses, cabinets, doors, windows, and mechanical systems. However, there is a great need for automating the assembly of the building construction. This research builds a framework on how robotics can automate the construction of significant building components and assemble them in the field. In this approach, walls, roofs, and floors assemble in the area. Several attempts have been made to use robots for wood framing. However, these automation attempts focused only on the structural parts of the wooden construction. The scope of this research includes using multiple robots with conveying belts to incorporate the assembly of the main building components, including the wooden framing, exterior sheathing and siding, electric wiring and trim, plumbing pipes, thermal insulation, vapor barrier, waterproofing, trim, windows, and doors, and first coat painting. Building assemblies will be transported to the field and fit together. The system will integrate mass customization strategies which includes Product Family Architecture and Personalization Design. This approach will improve quality assurance, reduce labor costs, reduce construction time, and reduce material waste. As a proof of concept, the research simulates the use of a robot to assemble walls components. The results showed that using AI Robotics to automate wooden residential construction is possible and practical. The advancement in AI robots overcame the inconsistencies in building materials and achieved the goals of this project.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131307170","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 Cyber-Manufacturing systems (CMS) is an integration of informational and operational entities that are synchronized with manufacturing processes to increase productivity. However, this integration enlarges the scope for cyber attackers to intrude manufacturing processes, which are called cyber-manufacturing attacks. They can have significant impacts on physical operations within a CMS, such as shutting down plants, production interruption, premature failure of products, and fatal accidents. Although research activities in this emerging problem have been increased recently, existing research has been limited to detection and prevention solutions. However, these strategies cannot ensure a continuous function of an attacked CMS. To ensure continuous functioning of a CMS, a robust recovery strategy must be developed and employed. Current research in recovery has been limited to feedback controllers with an assumption of a complete knowledge of a system model. To overcome this limitation, a recovery agent augmented by reinforcement learning was developed. This is to utilize the ability of reinforcement learning to handle sequential decisions and to proceed even without a complete knowledge of a system model. A virtual environment for recovery agents has been developed to assist efforts needed to obtain sample data, experiment various scenarios, and explore with reinforcement learning. Two cyber-manufacturing attack scenarios have been developed: (i) spoofing a stepper motor controlling additive manufacturing processes, (ii) disrupting the sequence of the pick and place robot. The recovery agent takes random actions by exploring its environment and receives rewards from the actions. After many iterations, it learns proper actions to take.
{"title":"Recovering From Cyber-Manufacturing Attacks by Reinforcement Learning","authors":"Romesh Prasad, Matthew K. Swanson, Y. Moon","doi":"10.1115/imece2022-93982","DOIUrl":"https://doi.org/10.1115/imece2022-93982","url":null,"abstract":"\u0000 A Cyber-Manufacturing systems (CMS) is an integration of informational and operational entities that are synchronized with manufacturing processes to increase productivity. However, this integration enlarges the scope for cyber attackers to intrude manufacturing processes, which are called cyber-manufacturing attacks. They can have significant impacts on physical operations within a CMS, such as shutting down plants, production interruption, premature failure of products, and fatal accidents. Although research activities in this emerging problem have been increased recently, existing research has been limited to detection and prevention solutions. However, these strategies cannot ensure a continuous function of an attacked CMS. To ensure continuous functioning of a CMS, a robust recovery strategy must be developed and employed. Current research in recovery has been limited to feedback controllers with an assumption of a complete knowledge of a system model. To overcome this limitation, a recovery agent augmented by reinforcement learning was developed. This is to utilize the ability of reinforcement learning to handle sequential decisions and to proceed even without a complete knowledge of a system model. A virtual environment for recovery agents has been developed to assist efforts needed to obtain sample data, experiment various scenarios, and explore with reinforcement learning. Two cyber-manufacturing attack scenarios have been developed: (i) spoofing a stepper motor controlling additive manufacturing processes, (ii) disrupting the sequence of the pick and place robot. The recovery agent takes random actions by exploring its environment and receives rewards from the actions. After many iterations, it learns proper actions to take.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125628890","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}
Increasingly named as the number one non-traditional risk cyber-attacks against Cyber-manufacturing Systems (CMS) can cause a wide variety of losses. As the 4th industrial revolution is taking place CMS have become more resilient with the implementation of prevention, detection, redundancy, withstanding, and recovery mechanisms against cyber-attacks. However, the ever-evolving nature of these threats require systems to still be prepared for their eventual occurrence as it’s been demonstrated in the increasingly more common advent of successful cyber-attacks. While multiple generic threat models have been proposed by academics and government organizations for assessing the impact of cyber-attacks against Cyber-Physical Systems there is a research gap when it comes to manufacturing specific applications and how to assess their severity. In order to evaluate the impact of a cyber-attack against CMS this taxonomy proposes a classification of threats severity comprising three general themes: i) Operational Impact: Effective production time loss that incur in inability to yield the expected output, ii) Economic Impacts: Direct financial cost of the attack, mitigation, and recovery, and iii) Intangible Losses: Integrity breaches against original patents, models, or intangible actives.
{"title":"Taxonomy of Severity of Cyber-Attacks in Cyber-Manufacturing Systems","authors":"Carlos Espinoza-Zelaya, Y. Moon","doi":"10.1115/imece2022-94492","DOIUrl":"https://doi.org/10.1115/imece2022-94492","url":null,"abstract":"\u0000 Increasingly named as the number one non-traditional risk cyber-attacks against Cyber-manufacturing Systems (CMS) can cause a wide variety of losses. As the 4th industrial revolution is taking place CMS have become more resilient with the implementation of prevention, detection, redundancy, withstanding, and recovery mechanisms against cyber-attacks. However, the ever-evolving nature of these threats require systems to still be prepared for their eventual occurrence as it’s been demonstrated in the increasingly more common advent of successful cyber-attacks. While multiple generic threat models have been proposed by academics and government organizations for assessing the impact of cyber-attacks against Cyber-Physical Systems there is a research gap when it comes to manufacturing specific applications and how to assess their severity. In order to evaluate the impact of a cyber-attack against CMS this taxonomy proposes a classification of threats severity comprising three general themes: i) Operational Impact: Effective production time loss that incur in inability to yield the expected output, ii) Economic Impacts: Direct financial cost of the attack, mitigation, and recovery, and iii) Intangible Losses: Integrity breaches against original patents, models, or intangible actives.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132777676","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 this study, we propose a method to optimize a domain segmentation of multiple anisotropic materials having varying orientation angles (OAs). The feature of this method is that anisotropic materials having different OAs are considered as different materials for each angle and the domain segmentation is optimized. First, the formulation of a multi-material topology optimization problem is described in which anisotropic materials with different OAs are considered as different materials. Then, linear elasticity topological derivatives are calculated when an anisotropic material is replaced with a different anisotropic material. Subsequently, we outline a topology optimization method based on the extended level set method, which is used to solve the multi-material topology optimization problem. Finally, we apply the proposed method to a stiffness maximization problem and demonstrate its effectiveness using multiple numerical examples.
{"title":"Domain Segmentation Optimization of Multiple Anisotropic Materials With Varying Orientation Angles Using a Topology Optimization Based on the Extended Level Set Method","authors":"M. Noda, K. Matsushima, Y. Noguchi, T. Yamada","doi":"10.1115/imece2022-94041","DOIUrl":"https://doi.org/10.1115/imece2022-94041","url":null,"abstract":"\u0000 In this study, we propose a method to optimize a domain segmentation of multiple anisotropic materials having varying orientation angles (OAs). The feature of this method is that anisotropic materials having different OAs are considered as different materials for each angle and the domain segmentation is optimized. First, the formulation of a multi-material topology optimization problem is described in which anisotropic materials with different OAs are considered as different materials. Then, linear elasticity topological derivatives are calculated when an anisotropic material is replaced with a different anisotropic material. Subsequently, we outline a topology optimization method based on the extended level set method, which is used to solve the multi-material topology optimization problem. Finally, we apply the proposed method to a stiffness maximization problem and demonstrate its effectiveness using multiple numerical examples.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133213774","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, Maulshree Singh, S. Alsamhi, D. Devine, Niall Murray
The Digital Twin (DT) in the manufacturing domain is already the everyday tool for visualizing the various industrial systems, equipment, and produced products. When designing a new manufacturing unit or enlarging an existing factory, it is important to do so without affecting the manufacturing process flow itself. There are opportunities through simulation and digital manufacturing to plan and optimize this design process. Within usage of the actual physical machinery data gathered from the Industrial Internet of Things (IIoT) sensors and feeding to the DT, optimizing the layout can be done more precisely and effectively. However, there is no way to test the potential equipment simultaneously with the physical one in real-time. This paper aims to propose a Mixed Reality (MR) based system framework and toolkit, which will enable physical industrial robots to interact with virtual equipment and other virtual robots. This way, via Virtual Reality (VR), it will be possible to design a system layout. Furthermore, via the Augmented Reality (AR) view, it will be possible to simulate the interaction between multiple robots by enhancing the possibilities of the physical environment and using the new precise scale real-time design method.
{"title":"Physical and Virtual Robotic Cells in Industry 4.0 Towards Industry 5.0: An XR-Based Conceptual Framework","authors":"V. Kuts, Maulshree Singh, S. Alsamhi, D. Devine, Niall Murray","doi":"10.1115/imece2022-95021","DOIUrl":"https://doi.org/10.1115/imece2022-95021","url":null,"abstract":"\u0000 The Digital Twin (DT) in the manufacturing domain is already the everyday tool for visualizing the various industrial systems, equipment, and produced products. When designing a new manufacturing unit or enlarging an existing factory, it is important to do so without affecting the manufacturing process flow itself. There are opportunities through simulation and digital manufacturing to plan and optimize this design process. Within usage of the actual physical machinery data gathered from the Industrial Internet of Things (IIoT) sensors and feeding to the DT, optimizing the layout can be done more precisely and effectively. However, there is no way to test the potential equipment simultaneously with the physical one in real-time. This paper aims to propose a Mixed Reality (MR) based system framework and toolkit, which will enable physical industrial robots to interact with virtual equipment and other virtual robots. This way, via Virtual Reality (VR), it will be possible to design a system layout. Furthermore, via the Augmented Reality (AR) view, it will be possible to simulate the interaction between multiple robots by enhancing the possibilities of the physical environment and using the new precise scale real-time design method.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130450418","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 this paper, we studied a flap valve micro-fluidic pump that relies on an electromagnetic actuation mechanism. The upper wall pump chamber is made of a smart material called magnetorheological elastomer (MRE). Under a magnetic field, the upper wall contracts, and the amount of contraction depends on the intensity of the applied magnetic field, which can be controlled via electromagnets. Moreover, flap valves mounted inside this micropump can convey fluids unidirectionally. A Finite Element Analysis (FEA)/Computational Fluid Dynamics (CFD)-based approach was embraced for the design of the device due to the coupled electromagnetic-fluid-structural interactions in the device. Simulations were carried out in COMSOL Multiphysics software. The performance characteristics of the pump were presented and discussed. In addition, a parametric study was conducted to see the effects of important design parameters on the net pumped volume, results of which were also presented and discussed. After the simulation studies, a working prototype pump with a 10.22 × 7.67 × 51.11 mm (W × H × L) was 3D printed. The experimental plan for the working prototype was discussed for further studies. The presented study lays the foundation for future studies where the pump size will be reduced to under 1 mm. The proposed micropump could potentially be used in a broad range of applications, such as an insulin dosing system for Type 1 Diabetic patients, artificial organs to transport blood, organ-on-chip applications, and so on.
{"title":"A Proof-of-Concept Study of a Magnetorheological Micropump","authors":"S. Cesmeci, Rubayet Hassan, Mark Thompson","doi":"10.1115/imece2022-96174","DOIUrl":"https://doi.org/10.1115/imece2022-96174","url":null,"abstract":"\u0000 In this paper, we studied a flap valve micro-fluidic pump that relies on an electromagnetic actuation mechanism. The upper wall pump chamber is made of a smart material called magnetorheological elastomer (MRE). Under a magnetic field, the upper wall contracts, and the amount of contraction depends on the intensity of the applied magnetic field, which can be controlled via electromagnets. Moreover, flap valves mounted inside this micropump can convey fluids unidirectionally. A Finite Element Analysis (FEA)/Computational Fluid Dynamics (CFD)-based approach was embraced for the design of the device due to the coupled electromagnetic-fluid-structural interactions in the device. Simulations were carried out in COMSOL Multiphysics software. The performance characteristics of the pump were presented and discussed. In addition, a parametric study was conducted to see the effects of important design parameters on the net pumped volume, results of which were also presented and discussed. After the simulation studies, a working prototype pump with a 10.22 × 7.67 × 51.11 mm (W × H × L) was 3D printed. The experimental plan for the working prototype was discussed for further studies. The presented study lays the foundation for future studies where the pump size will be reduced to under 1 mm. The proposed micropump could potentially be used in a broad range of applications, such as an insulin dosing system for Type 1 Diabetic patients, artificial organs to transport blood, organ-on-chip applications, and so on.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134376351","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}
Ahmed Almalki, Ali A. Rajhi, Hussam H Noor, A. Kundu, J. Coulter
The primary objective of this research was to experimentally investigate the robustness of a commercially available zirconium-based bulk metallic glass material (Zr-based BMG) for microinjection molding (μIM) tooling. The focused ion beam (FIB) direct milling process was utilized to fabricate microfeatures onto two BMG-based mold inserts. Uncoated and Ti-coated inserts were inspected through molding cycles utilizing SEM. Additionally, TPU molded samples were characterized to quantify the replication quality of the inserts through molding cycles. This is to understand the polymer melt effect of the tooling during molding conditions. The uncoated BMG insert was utilized for more than 1000 molding cycles regardless of the potential crystallization. No signs of any crack initiation were observed in any part of the BMG insert. Through molding process, the replication quality degraded due to the polymer adhesion to the microcavity base. In the case of the coated BMG insert, the coating could not withstand the high ejection force during demolding stage. The adhesion between the coating and the BMG surface was insufficient to survive molding conditions. This resulted in disintegrated coating that was bonded into molded samples.
{"title":"Experimental Investigation of the Robustness of Bulk Metallic Glass-Based Tooling for Microinjection Molding","authors":"Ahmed Almalki, Ali A. Rajhi, Hussam H Noor, A. Kundu, J. Coulter","doi":"10.1115/imece2022-94888","DOIUrl":"https://doi.org/10.1115/imece2022-94888","url":null,"abstract":"\u0000 The primary objective of this research was to experimentally investigate the robustness of a commercially available zirconium-based bulk metallic glass material (Zr-based BMG) for microinjection molding (μIM) tooling. The focused ion beam (FIB) direct milling process was utilized to fabricate microfeatures onto two BMG-based mold inserts.\u0000 Uncoated and Ti-coated inserts were inspected through molding cycles utilizing SEM. Additionally, TPU molded samples were characterized to quantify the replication quality of the inserts through molding cycles. This is to understand the polymer melt effect of the tooling during molding conditions.\u0000 The uncoated BMG insert was utilized for more than 1000 molding cycles regardless of the potential crystallization. No signs of any crack initiation were observed in any part of the BMG insert. Through molding process, the replication quality degraded due to the polymer adhesion to the microcavity base.\u0000 In the case of the coated BMG insert, the coating could not withstand the high ejection force during demolding stage. The adhesion between the coating and the BMG surface was insufficient to survive molding conditions. This resulted in disintegrated coating that was bonded into molded samples.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131683008","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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