Carrington Chun, David A. Guerra-Zubiaga, Garrett Bailey, Kathryn Bharadwaj
Advanced industrial assembly lines often utilize large-scale robotic arms such as the Fanuc S 420-F. Such arms, and their end-effectors, are typically constructed from high-strength steel, which gives the systems superior rigidity at the cost of being very heavy. A new cutting-edge composite material, carbon fiber, offers the strength of steel at a fraction of the weight. To improve energy efficiency, this research project analyzed the feasibility of replacing the steel structure in an end-effector with a carbon-fiber composite, in addition to equipping the end effector with revolutionary ‘Smart’ technologies. Simulations performed in Siemens’ Process Simulate Tecnomatix module helped to inform mechanical energy computations for an arbitrary pick and place task and energy cost estimations were analyzed with the end-effector constructed from both steel and carbon fiber. The projected change in energy consumption for performing the pick and place task was then compared to determine the potential benefit of the carbon fiber substitution. In addition to the advanced material use, this research project also investigated the possibility of implementing ‘Smart’ technologies in the custom end effector design to further improve energy efficiency. The proposed smart technology would utilize machine vision to actively direct vacuum pressure to only the necessary suction cups in a pneumatic gripper array. Possible energy savings associated with the smart end effector design were analyzed. Simulation results for a simple pick and place operation showed that the Smart Carbon Fiber End Effector required only 2.22 Kilojoules of energy, compared to the 3.92 Kilojoules of energy needed for a Passive Steel Framed End Effector. Through creation and simulation with Digital Design Tools, the feasibility of combining advanced new structural materials with integrated intelligence was explored to create a revolutionary new end effector design that could reduce the energy consumption for a pick and place task.
{"title":"High Efficiency Manufacturing With a Smart Carbon Fiber End Effector","authors":"Carrington Chun, David A. Guerra-Zubiaga, Garrett Bailey, Kathryn Bharadwaj","doi":"10.1115/imece2022-94207","DOIUrl":"https://doi.org/10.1115/imece2022-94207","url":null,"abstract":"\u0000 Advanced industrial assembly lines often utilize large-scale robotic arms such as the Fanuc S 420-F. Such arms, and their end-effectors, are typically constructed from high-strength steel, which gives the systems superior rigidity at the cost of being very heavy. A new cutting-edge composite material, carbon fiber, offers the strength of steel at a fraction of the weight. To improve energy efficiency, this research project analyzed the feasibility of replacing the steel structure in an end-effector with a carbon-fiber composite, in addition to equipping the end effector with revolutionary ‘Smart’ technologies. Simulations performed in Siemens’ Process Simulate Tecnomatix module helped to inform mechanical energy computations for an arbitrary pick and place task and energy cost estimations were analyzed with the end-effector constructed from both steel and carbon fiber. The projected change in energy consumption for performing the pick and place task was then compared to determine the potential benefit of the carbon fiber substitution. In addition to the advanced material use, this research project also investigated the possibility of implementing ‘Smart’ technologies in the custom end effector design to further improve energy efficiency. The proposed smart technology would utilize machine vision to actively direct vacuum pressure to only the necessary suction cups in a pneumatic gripper array. Possible energy savings associated with the smart end effector design were analyzed. Simulation results for a simple pick and place operation showed that the Smart Carbon Fiber End Effector required only 2.22 Kilojoules of energy, compared to the 3.92 Kilojoules of energy needed for a Passive Steel Framed End Effector. Through creation and simulation with Digital Design Tools, the feasibility of combining advanced new structural materials with integrated intelligence was explored to create a revolutionary new end effector design that could reduce the energy consumption for a pick and place task.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"95 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":"116716282","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}
Nelson Rodrigues, Inês Teixeira, V. Carvalho, Duarte Santos, J. Velosa, A. Ferreira, D. Soares, J. Teixeira, S. Teixeira
In the present work, a numerical model was developed to simulate the placement of a pin through-hole (PTH) component in an aperture containing solder paste by using Ansys Fluent® software. The work uses numerical tools to understand the physical process and the solder paste behaviour for future manufacturing improvements. The component movement was simulated with the dynamic mesh model through a user-defined function (UDF). Due to software limitations, the pin of the component is already contacting the solder paste at the start of the movement. additionally, the contact detection feature was activated to prevent the pin and PCB contact during the solder paste melting. Nevertheless, the component can still move in the domain if other forces act upon it, except in the direction where the contact would occur. The second point to address is the meniscus formation process. This begins with the evaporation of the solder paste flux, causing a volume reduction followed by the melting of the metallic beads. The simulation allowed us to see that the melted solder surrounded the pin through capillarity and reproduced the phenomena of this solder printing process. This work allowed to follow the behaviour of both the component and the solder paste.
{"title":"2D Simulation of the Placement of a Pin-Through-Hole Component and Solder Paste Melting","authors":"Nelson Rodrigues, Inês Teixeira, V. Carvalho, Duarte Santos, J. Velosa, A. Ferreira, D. Soares, J. Teixeira, S. Teixeira","doi":"10.1115/imece2022-95960","DOIUrl":"https://doi.org/10.1115/imece2022-95960","url":null,"abstract":"\u0000 In the present work, a numerical model was developed to simulate the placement of a pin through-hole (PTH) component in an aperture containing solder paste by using Ansys Fluent® software. The work uses numerical tools to understand the physical process and the solder paste behaviour for future manufacturing improvements. The component movement was simulated with the dynamic mesh model through a user-defined function (UDF). Due to software limitations, the pin of the component is already contacting the solder paste at the start of the movement. additionally, the contact detection feature was activated to prevent the pin and PCB contact during the solder paste melting. Nevertheless, the component can still move in the domain if other forces act upon it, except in the direction where the contact would occur.\u0000 The second point to address is the meniscus formation process. This begins with the evaporation of the solder paste flux, causing a volume reduction followed by the melting of the metallic beads. The simulation allowed us to see that the melted solder surrounded the pin through capillarity and reproduced the phenomena of this solder printing process. This work allowed to follow the behaviour of both the component and the solder paste.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"2 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":"128766840","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}
Rupali Srivastava, V. Kuts, Eber Lawrence Souza Gouveia, Niall Murray, D. Devine, Eoin O’Connell
Conceptualization of the Smart Factory started with introducing the Industry 4.0 paradigm and its nine pillars, which it stands. The paradigm itself is automation and robot-centric focused, which means less and less involvement of the humans on the manufacturing shop floor. However, even robots and simulation aspects of the factories are the most crucial aspects; Industry 4.0 still focuses on the Augmented and Virtual Reality (AR and VR input methods for the human operators, making the smooth transition to the Industry 5.0 concept a human-centric. Although VR/AR is still being enabled and widely used in the Human-Robot Interaction (HRI) research aspect, the heavy headset is limited in the observation field of view. The input methods, such as headsets, have voice and gesture recognition; however, those are mainly limited by factory noise and cameras pointing to the human hands. These headsets constrain the use of smart wearables to a given boundary inside the factory environment. A Shape Memory Alloy (SMA) based haptic glove with discrete data outputs from the kinaesthetic analysis of the hand bending can remove the need for gesture recognition. The paper proposes a modular framework using the SMA-based Haptic Gloves in the Smart Manufacturing environment. These gloves, without additional wearables, can enable interactions with heavy machinery, screens, and all other assets of the industrial area, even with holographic. In this paper, the authors aim to prose the context, design, and framework with the chosen use-cases mainly based on the robotic system applications in the Technological University of the Shannon: Midlands Midwest (TUS: MMW), Ireland, and Tallinn University of Technology (TalTech), Estonia.
{"title":"SMA-Based Haptic Gloves Usage in the Smart Factory Concept: XR Use Case","authors":"Rupali Srivastava, V. Kuts, Eber Lawrence Souza Gouveia, Niall Murray, D. Devine, Eoin O’Connell","doi":"10.1115/imece2022-94305","DOIUrl":"https://doi.org/10.1115/imece2022-94305","url":null,"abstract":"\u0000 Conceptualization of the Smart Factory started with introducing the Industry 4.0 paradigm and its nine pillars, which it stands. The paradigm itself is automation and robot-centric focused, which means less and less involvement of the humans on the manufacturing shop floor. However, even robots and simulation aspects of the factories are the most crucial aspects; Industry 4.0 still focuses on the Augmented and Virtual Reality (AR and VR input methods for the human operators, making the smooth transition to the Industry 5.0 concept a human-centric. Although VR/AR is still being enabled and widely used in the Human-Robot Interaction (HRI) research aspect, the heavy headset is limited in the observation field of view. The input methods, such as headsets, have voice and gesture recognition; however, those are mainly limited by factory noise and cameras pointing to the human hands. These headsets constrain the use of smart wearables to a given boundary inside the factory environment. A Shape Memory Alloy (SMA) based haptic glove with discrete data outputs from the kinaesthetic analysis of the hand bending can remove the need for gesture recognition. The paper proposes a modular framework using the SMA-based Haptic Gloves in the Smart Manufacturing environment. These gloves, without additional wearables, can enable interactions with heavy machinery, screens, and all other assets of the industrial area, even with holographic. In this paper, the authors aim to prose the context, design, and framework with the chosen use-cases mainly based on the robotic system applications in the Technological University of the Shannon: Midlands Midwest (TUS: MMW), Ireland, and Tallinn University of Technology (TalTech), Estonia.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"101 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":"115734620","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}
Ankur Verma, Seog-Chan Oh, James W. Wells, J. Arinez, S. Kumara
The automotive industry has used in-line conveyor systems to move vehicles through the general assembly process since the early days of vehicle manufacturing in the 20th century. With products shifting to EVs (Electric Vehicle) and the emergence of AMRs (Autonomous Mobile Robot), there is an opportunity to transform the assembly process into a more flexible conveyor-less matrix-based system. One key development need is how to structure and optimize such an asynchronous system. This paper presents a new methodology for designing a conveyor-less matrix assembly layout to maximize labor productivity, workstation utilization, and footprint usage, while minimizing the system costs, and cycle times. Specifically, we develop an asynchronous assembly system for the automotive trim area. We aim to answer the question: How do we decide on an optimum number of workstations for the asynchronous assembly system, such that productivity and ROI are maximized? For this, we use cycle times, the number of operations per workstation, reference heights, and precedence graphs as input variables. Similarity matrices are used to quantify the similarity of tools, ergonomics, and human operations between workstations. Workstation utilization percentage and makespan are the metrics used to compare between alternative layouts. Finally, we perform a cost and makespan analysis to evaluate the ratio of trim area costs to total revenue, calculate makespan, and report the best layout found in the study. Quantifying subjective data, repeatability, less setup, and simulation time are the attributes that make this methodology valuable to any virtual commissioning software, an integral part of the smart manufacturing ecosystem.
{"title":"Conveyer-Less Matrix Assembly Layout Design to Maximize Labor Productivity and Footprint Usage","authors":"Ankur Verma, Seog-Chan Oh, James W. Wells, J. Arinez, S. Kumara","doi":"10.1115/imece2022-94628","DOIUrl":"https://doi.org/10.1115/imece2022-94628","url":null,"abstract":"\u0000 The automotive industry has used in-line conveyor systems to move vehicles through the general assembly process since the early days of vehicle manufacturing in the 20th century. With products shifting to EVs (Electric Vehicle) and the emergence of AMRs (Autonomous Mobile Robot), there is an opportunity to transform the assembly process into a more flexible conveyor-less matrix-based system. One key development need is how to structure and optimize such an asynchronous system. This paper presents a new methodology for designing a conveyor-less matrix assembly layout to maximize labor productivity, workstation utilization, and footprint usage, while minimizing the system costs, and cycle times. Specifically, we develop an asynchronous assembly system for the automotive trim area. We aim to answer the question: How do we decide on an optimum number of workstations for the asynchronous assembly system, such that productivity and ROI are maximized? For this, we use cycle times, the number of operations per workstation, reference heights, and precedence graphs as input variables. Similarity matrices are used to quantify the similarity of tools, ergonomics, and human operations between workstations. Workstation utilization percentage and makespan are the metrics used to compare between alternative layouts. Finally, we perform a cost and makespan analysis to evaluate the ratio of trim area costs to total revenue, calculate makespan, and report the best layout found in the study. Quantifying subjective data, repeatability, less setup, and simulation time are the attributes that make this methodology valuable to any virtual commissioning software, an integral part of the smart manufacturing ecosystem.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"28 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":"114977880","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}
Mizuki Ishiguro, S. Warisawa, Naoyasu Narita, Hironobu Miyoshi, R. Fukui
Recently, laser cutting is an essential technology for high-speed flexible sheet-metal processing. The problem of defective cutting has occurred even with appropriate cutting parameter settings. The authors have constructed a defect recognizer that uses data of light generated during thin plate cutting, and have achieved a high recognition rate. On the other hand, some misrecognition occurred, and all of the misrecognized data were found to be workpieces with rust on the surface. Therefore, as a method to reduce misrecognition, rust information should be acquired and used for defect recognition before cutting. This study aims to acquire rust information on the workpiece surface by sensing and to recognize the existence of rust in order to reduce the false recognition in thin plate cutting defect recognition. The proposed method consists of three steps. In the first step, the surface of the workpiece is irradiated by a low-power laser, and the light generated is measured using a spectrometer installed in the laser head. In the second step, the acquired spectral data is converted into a spectrogram, and the image is binarized using Otsu’s binarization method to obtain features. In the final step, a one-class support vector machine recognizes the existence of rust on a workpiece surface from the extracted features. Verification tests using a normal and two kinds of rusted surface plates data confirmed that the proposed method accurately detected the existence of rust. (Precision = 0.89, Recall = 1.0.) It was also confirmed that the low-power laser irradiation trace did not affect the spectral data of the cutting for defect recognition.
{"title":"Thin Steel Plate Surface Rust Recognition Using Processing Light Measurement for Reduction of Laser Cutting Defect False Recognition","authors":"Mizuki Ishiguro, S. Warisawa, Naoyasu Narita, Hironobu Miyoshi, R. Fukui","doi":"10.1115/imece2022-96166","DOIUrl":"https://doi.org/10.1115/imece2022-96166","url":null,"abstract":"\u0000 Recently, laser cutting is an essential technology for high-speed flexible sheet-metal processing. The problem of defective cutting has occurred even with appropriate cutting parameter settings. The authors have constructed a defect recognizer that uses data of light generated during thin plate cutting, and have achieved a high recognition rate. On the other hand, some misrecognition occurred, and all of the misrecognized data were found to be workpieces with rust on the surface. Therefore, as a method to reduce misrecognition, rust information should be acquired and used for defect recognition before cutting. This study aims to acquire rust information on the workpiece surface by sensing and to recognize the existence of rust in order to reduce the false recognition in thin plate cutting defect recognition. The proposed method consists of three steps. In the first step, the surface of the workpiece is irradiated by a low-power laser, and the light generated is measured using a spectrometer installed in the laser head. In the second step, the acquired spectral data is converted into a spectrogram, and the image is binarized using Otsu’s binarization method to obtain features. In the final step, a one-class support vector machine recognizes the existence of rust on a workpiece surface from the extracted features. Verification tests using a normal and two kinds of rusted surface plates data confirmed that the proposed method accurately detected the existence of rust. (Precision = 0.89, Recall = 1.0.) It was also confirmed that the low-power laser irradiation trace did not affect the spectral data of the cutting for defect recognition.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"39 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":"121250853","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}
Electron beam melting (EBM) is an additive manufacturing process that utilizes a metallic powder as the feedstock material. During the setup of an Arcam A2X machine, the operator is first required to load the feedstock into the hoppers and set the “zero” position of the start plate relative to the rake. This manual process relies on operator-experience and is hypothesized to have a detrimental impact on process repeatability by modifying the local layer thickness. The potential effect that this has on the as-built quality and performance of EBM-manufactured components is unknown. Therefore, this study presents a device used to measure the variability that exists during A2X machine operations. Consideration is given to both the build basin as well as the start plate during ten machine setups. Assuming a mean particle diameter and commensurate layer thickness of 50 μm for Ti-6Al-4V, data indicate that local thickness deviations greater than ten layers are achievable using the prescribed zeroing routine. These deviations are shown to manifest in the measured mass of specimens within a test build. While not directly considered in this study, a discussion regarding powder pile size during fetching operations and how this may affect bed coverage and build quality is also provided.
{"title":"Build Chamber and Start Plate Variability During Electron Beam Melting Machine Setup","authors":"Garrett M. Kelley, Ramulu Mamidala","doi":"10.1115/imece2022-96980","DOIUrl":"https://doi.org/10.1115/imece2022-96980","url":null,"abstract":"\u0000 Electron beam melting (EBM) is an additive manufacturing process that utilizes a metallic powder as the feedstock material. During the setup of an Arcam A2X machine, the operator is first required to load the feedstock into the hoppers and set the “zero” position of the start plate relative to the rake. This manual process relies on operator-experience and is hypothesized to have a detrimental impact on process repeatability by modifying the local layer thickness. The potential effect that this has on the as-built quality and performance of EBM-manufactured components is unknown. Therefore, this study presents a device used to measure the variability that exists during A2X machine operations. Consideration is given to both the build basin as well as the start plate during ten machine setups. Assuming a mean particle diameter and commensurate layer thickness of 50 μm for Ti-6Al-4V, data indicate that local thickness deviations greater than ten layers are achievable using the prescribed zeroing routine. These deviations are shown to manifest in the measured mass of specimens within a test build. While not directly considered in this study, a discussion regarding powder pile size during fetching operations and how this may affect bed coverage and build quality is also provided.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"203 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":"131612880","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}
Milling is one of the old and common cutting processes that utilize rotating tools to take materials off the main component with a combination of tools and workpiece movements. The texture of a machined surface is a key factor in defining how an essential component interacts with its environment. Trial-and-error machining to produce high-quality surfaces has been a time-consuming method that yields lower production and poor revenue. In this paper, the performances of an Adaptive Network-based Fuzzy Inference System (ANFIS) model has been employed for the prediction of the surface roughness (SR) of a block of Aluminum alloy AI6061 machined on an end-mill CNC machine by varying four input settings namely: The spindle speed of rotation, the tool cutting rate, the radial depth, and the axial depth. The approach consisted of a parametric analysis carried out within each system to obtain the finest models for the prediction. The hybrids ANFIS-PSO and ANFIS-GA have been employed to find out which one, either PSO or GA, optimizes better ANFIS for the prediction of Al6061 SR. Their performances produced better results than the stand-alone ANFIS, with ANFIS-GA yielding the best results of the most negligible RMSE value of 0.01097 and the regression values of 0.9939 for training and 0.8102 for testing.
{"title":"Parametric Analysis of ANFIS, ANFIS-PSO, and ANFIS-GA Models for the Prediction of Aluminum Surface Roughness in End-Milling Operation","authors":"S. Balonji, I. Okokpujie, L. Tartibu","doi":"10.1115/imece2022-95418","DOIUrl":"https://doi.org/10.1115/imece2022-95418","url":null,"abstract":"\u0000 Milling is one of the old and common cutting processes that utilize rotating tools to take materials off the main component with a combination of tools and workpiece movements. The texture of a machined surface is a key factor in defining how an essential component interacts with its environment. Trial-and-error machining to produce high-quality surfaces has been a time-consuming method that yields lower production and poor revenue. In this paper, the performances of an Adaptive Network-based Fuzzy Inference System (ANFIS) model has been employed for the prediction of the surface roughness (SR) of a block of Aluminum alloy AI6061 machined on an end-mill CNC machine by varying four input settings namely: The spindle speed of rotation, the tool cutting rate, the radial depth, and the axial depth. The approach consisted of a parametric analysis carried out within each system to obtain the finest models for the prediction. The hybrids ANFIS-PSO and ANFIS-GA have been employed to find out which one, either PSO or GA, optimizes better ANFIS for the prediction of Al6061 SR. Their performances produced better results than the stand-alone ANFIS, with ANFIS-GA yielding the best results of the most negligible RMSE value of 0.01097 and the regression values of 0.9939 for training and 0.8102 for testing.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"98 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":"133265771","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. Bertolino, Andrea De Martin, Stefano Mauro, M. Sorli
No exact formulations are present in literature to calculate the principal curvatures of the ball screw grooves, while approximate equations are commonly adopted due to their simplicity. However, they derive from the ball bearing theory and do not take into account ball screw’s characteristic main geometric parameters, such as the helix angle. This leads to approximation relative errors up to 50% for commercial off-the-shelf ball screws. In this paper a new exact explicit exact solution is proposed by means of a rigorous mathematical approach. The helix angle and a generic gothic arch shape of the raceway profile are taken into account. The results of the exact and literature formulations are presented and compared. The principal curvature is seen to be strongly dependent on the helix angle and, therefore, the proposed exact solution should be used in the design phase and, in general, when high accuracy is required, such as in high-fidelity models for prognostic activities.
{"title":"Derivation of the Exact Curvature Formulation for Gothic Arch Ball Screw Grooves","authors":"A. Bertolino, Andrea De Martin, Stefano Mauro, M. Sorli","doi":"10.1115/imece2022-95746","DOIUrl":"https://doi.org/10.1115/imece2022-95746","url":null,"abstract":"\u0000 No exact formulations are present in literature to calculate the principal curvatures of the ball screw grooves, while approximate equations are commonly adopted due to their simplicity. However, they derive from the ball bearing theory and do not take into account ball screw’s characteristic main geometric parameters, such as the helix angle. This leads to approximation relative errors up to 50% for commercial off-the-shelf ball screws. In this paper a new exact explicit exact solution is proposed by means of a rigorous mathematical approach. The helix angle and a generic gothic arch shape of the raceway profile are taken into account. The results of the exact and literature formulations are presented and compared. The principal curvature is seen to be strongly dependent on the helix angle and, therefore, the proposed exact solution should be used in the design phase and, in general, when high accuracy is required, such as in high-fidelity models for prognostic activities.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"46 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":"131868149","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}
Additive manufacturing (AM) or 3D printing is gaining momentum in the market compared to conventional subtractive technologies due to its ability to speedily produce complex and customized geometries with less waste of material. Some 3D printing parameters are influential or crucial as they affect the final part’s mechanical properties based on the technology used. These are the printing height, the printing rate, the nozzle diameter, the nozzle movement rate, the layer thickness, the temperature, the ventilator speed, the print precision, the layer thickness, the type of infill, and the extrusion. This paper proposes the development of a neural networks model (ANN) and a hybrid neural network trained by particle swarm optimization (ANN-PSO) to get an insight into the selection of 3D printing parameters and adjust them. To ensure the quality of the 3D printing, a parametric analysis has been performed to identify the best configuration of the models. Readily available data has been used to demonstrate the potential of the proposed approach. These data have been used to train and test the algorithms and build robust models able to predict performance. The ANN and the ANN-PSO models have exhibited good overall performance that demonstrates the potential for modelling and prediction of 3D printing performance using machine learning techniques.
{"title":"Analysis of 3D Printing Performance Using Machine Learning Techniques","authors":"K. Kabengele, L. Tartibu, I. Olayode","doi":"10.1115/imece2022-94000","DOIUrl":"https://doi.org/10.1115/imece2022-94000","url":null,"abstract":"\u0000 Additive manufacturing (AM) or 3D printing is gaining momentum in the market compared to conventional subtractive technologies due to its ability to speedily produce complex and customized geometries with less waste of material. Some 3D printing parameters are influential or crucial as they affect the final part’s mechanical properties based on the technology used. These are the printing height, the printing rate, the nozzle diameter, the nozzle movement rate, the layer thickness, the temperature, the ventilator speed, the print precision, the layer thickness, the type of infill, and the extrusion. This paper proposes the development of a neural networks model (ANN) and a hybrid neural network trained by particle swarm optimization (ANN-PSO) to get an insight into the selection of 3D printing parameters and adjust them. To ensure the quality of the 3D printing, a parametric analysis has been performed to identify the best configuration of the models. Readily available data has been used to demonstrate the potential of the proposed approach. These data have been used to train and test the algorithms and build robust models able to predict performance. The ANN and the ANN-PSO models have exhibited good overall performance that demonstrates the potential for modelling and prediction of 3D printing performance using machine learning techniques.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"71 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":"134077144","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}
P. Volke, G. Brock, S. Berger, J. Saelzer, J. Nickel, D. Biermann
Sustainability is an increasingly important trend, giving great potential in industrial production to save resources and energy. This paper presents fundamental research results on the reduction of cutting-tool consumption and saving on workpiece-material. For the basis of a new wear model, explicit knowledge of the thermo-mechanical loads and relative speeds between the tribological partners is of central importance. Therefore, a special open tribometer will be used to parameterize a friction model depending on relative speeds. Both, uncoated and coated tools are used. To reduce experimental effort, numerical simulations serve as an efficient representation of the cutting process. As a novelty in the project’s further course, both the experimentally determined tool temperatures and the friction conditions will be recorded in dependence on the tool wear, complemented by synchronized digitized wear images for validation purposes. The second work focuses on burnishing as a post-processing method for additively manufactured components. Compared to conventional machining, material and energy is saved and the workpieces’ surface integrity is improved, potentially increasing the components’ lifetime. Experimental tests are extended by finite element simulations, enabling investigations at different scales: At a workpiece model size of several millimeters, residual stresses are to be predicted; at a micrometer range, the surface smoothing is mapped.
{"title":"Sustainable Production of Rotationally Symmetrical Components: Approaches to Resource Saving on Tool and Workpiece","authors":"P. Volke, G. Brock, S. Berger, J. Saelzer, J. Nickel, D. Biermann","doi":"10.1115/imece2022-95378","DOIUrl":"https://doi.org/10.1115/imece2022-95378","url":null,"abstract":"\u0000 Sustainability is an increasingly important trend, giving great potential in industrial production to save resources and energy. This paper presents fundamental research results on the reduction of cutting-tool consumption and saving on workpiece-material.\u0000 For the basis of a new wear model, explicit knowledge of the thermo-mechanical loads and relative speeds between the tribological partners is of central importance. Therefore, a special open tribometer will be used to parameterize a friction model depending on relative speeds. Both, uncoated and coated tools are used. To reduce experimental effort, numerical simulations serve as an efficient representation of the cutting process. As a novelty in the project’s further course, both the experimentally determined tool temperatures and the friction conditions will be recorded in dependence on the tool wear, complemented by synchronized digitized wear images for validation purposes.\u0000 The second work focuses on burnishing as a post-processing method for additively manufactured components. Compared to conventional machining, material and energy is saved and the workpieces’ surface integrity is improved, potentially increasing the components’ lifetime. Experimental tests are extended by finite element simulations, enabling investigations at different scales: At a workpiece model size of several millimeters, residual stresses are to be predicted; at a micrometer range, the surface smoothing is mapped.","PeriodicalId":113474,"journal":{"name":"Volume 2B: Advanced Manufacturing","volume":"56 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":"116758890","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：