Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.01.036
Hanbing Xia , Jiahong Li , Qian (Jan) Li , Jelena Milisavljevic-Syed , Konstantinos Salonitis
The reverse supply chain (RSC), vital for circular economies, faces considerable challenges, including transparency deficits, trust issues, and inefficient information sharing. Blockchain can enhance transparency and traceability in circular economy practices, but it has limitations in providing detailed, product-specific lifecycle data. In contrast, the digital product passport (DPP) is designed to provide this detailed information, offering comprehensive, high-quality data across a product’s entire lifecycle. By integrating DPP, the inherent data limitations of blockchain can be overcame, making it a more useful tool in promoting sustainable practices. The aim of this research is to explore integrating blockchain technology and the DPP to manage RSC for circular economy practices. To achieve this, a novel conceptual framework is presented that merge blockchain with DPP, potentially enhancing information efficiency throughout the entire lifecycle of end-of-life products and reducing uncertainties in RSC processes.
{"title":"Integrating blockchain with digital product passports for managing reverse supply chain","authors":"Hanbing Xia , Jiahong Li , Qian (Jan) Li , Jelena Milisavljevic-Syed , Konstantinos Salonitis","doi":"10.1016/j.procir.2025.01.036","DOIUrl":"10.1016/j.procir.2025.01.036","url":null,"abstract":"<div><div>The reverse supply chain (RSC), vital for circular economies, faces considerable challenges, including transparency deficits, trust issues, and inefficient information sharing. Blockchain can enhance transparency and traceability in circular economy practices, but it has limitations in providing detailed, product-specific lifecycle data. In contrast, the digital product passport (DPP) is designed to provide this detailed information, offering comprehensive, high-quality data across a product’s entire lifecycle. By integrating DPP, the inherent data limitations of blockchain can be overcame, making it a more useful tool in promoting sustainable practices. The aim of this research is to explore integrating blockchain technology and the DPP to manage RSC for circular economy practices. To achieve this, a novel conceptual framework is presented that merge blockchain with DPP, potentially enhancing information efficiency throughout the entire lifecycle of end-of-life products and reducing uncertainties in RSC processes.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"132 ","pages":"Pages 215-220"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.01.021
Alexios Papacharalampopoulos , Olga Maria Karagianni , Panagiotis Stavropoulos , Unai Ziarsolo , Peter Totterdill , Rosemary Exton , Steven Dhondt , Peter Oeij , Matteo Fedeli , Massimo Ippolito , Fabrizio Timo , Arturas Gumuliauskas , Dovilė Eitmantytė , Unai Elorza
Manufacturing has been undergoing many changes, with the latest one being the paradigm shift to Industry 5.0. In this long procedure, training is required at any level, from operators to managers. Thus, interventions must be made so that Teaching and Learning Factories are upgraded towards integrating Industry 5.0. To this end, an evaluation system has to be made, assessing the feasibility of the three pillars’ integration. This procedure can concern a qualitative assessment (or a quantitative one) of the feasibility and the other implicated concepts, such as upskilling. At the same time, multilevel metrics are relevant, such as Key Performance Indicators (KPIs) related to company practices, manufacturing itself, jobs and trainees. Herein, a summative differential evaluation scheme, based on heuristic aspects, is explored, under the framework of the aforementioned TLF interventions. Examples of companies’ ex-ante characterization are given. Then, potential extensions are being discussed towards achieving formative evaluation and potentially towards KPIs.
{"title":"On a heuristic evaluation system for Industry 5.0 with respect to interventions: the case of training in businesses","authors":"Alexios Papacharalampopoulos , Olga Maria Karagianni , Panagiotis Stavropoulos , Unai Ziarsolo , Peter Totterdill , Rosemary Exton , Steven Dhondt , Peter Oeij , Matteo Fedeli , Massimo Ippolito , Fabrizio Timo , Arturas Gumuliauskas , Dovilė Eitmantytė , Unai Elorza","doi":"10.1016/j.procir.2025.01.021","DOIUrl":"10.1016/j.procir.2025.01.021","url":null,"abstract":"<div><div>Manufacturing has been undergoing many changes, with the latest one being the paradigm shift to Industry 5.0. In this long procedure, training is required at any level, from operators to managers. Thus, interventions must be made so that Teaching and Learning Factories are upgraded towards integrating Industry 5.0. To this end, an evaluation system has to be made, assessing the feasibility of the three pillars’ integration. This procedure can concern a qualitative assessment (or a quantitative one) of the feasibility and the other implicated concepts, such as upskilling. At the same time, multilevel metrics are relevant, such as Key Performance Indicators (KPIs) related to company practices, manufacturing itself, jobs and trainees. Herein, a summative differential evaluation scheme, based on heuristic aspects, is explored, under the framework of the aforementioned TLF interventions. Examples of companies’ ex-ante characterization are given. Then, potential extensions are being discussed towards achieving formative evaluation and potentially towards KPIs.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"132 ","pages":"Pages 122-128"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.01.022
Pedro Antonio Boareto , Eduardo de Freitas Rocha Loures , Eduardo Alves Portela Santos , Fernando Deschamps
One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and the need for workforce requalification. Generative Artificial Intelligence (GAI) emerges as a promising solution, offering capabilities to accelerate development processes and reduce costs. This study aims to leverage GAI to enhance the development of DT and support decision-making in industrial environments by proposing a Generative Assistant for Digital Twin Simulations (GADTS). This proposal generates operational models quickly, offers greater customization, and facilitates the creation of efficient scenario simulations in natural language. The proposal was tested with artificial data. As a result, the development of highly personalized DT simulations with Key Performance Indicators (KPIs) was entirely abstracted into natural language requests.
{"title":"Generative assistant for digital twin simulations","authors":"Pedro Antonio Boareto , Eduardo de Freitas Rocha Loures , Eduardo Alves Portela Santos , Fernando Deschamps","doi":"10.1016/j.procir.2025.01.022","DOIUrl":"10.1016/j.procir.2025.01.022","url":null,"abstract":"<div><div>One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and the need for workforce requalification. Generative Artificial Intelligence (GAI) emerges as a promising solution, offering capabilities to accelerate development processes and reduce costs. This study aims to leverage GAI to enhance the development of DT and support decision-making in industrial environments by proposing a Generative Assistant for Digital Twin Simulations (GADTS). This proposal generates operational models quickly, offers greater customization, and facilitates the creation of efficient scenario simulations in natural language. The proposal was tested with artificial data. As a result, the development of highly personalized DT simulations with Key Performance Indicators (KPIs) was entirely abstracted into natural language requests.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"132 ","pages":"Pages 129-134"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.077
Mikel Etxebeste , Gorka Ortiz-de-Zarate , Iñaki M. Arrieta , Pedro J. Arrazola
Long milling tools are often limited in productivity due to chatter vibrations. Embedded Tuned Mass Dampers (TMDs) in these tools have proven to be an effective solution for reducing chatter and increasing productivity. The performance of TMDs is highly dependent on the correct dimensioning and selection of the most suitable damping materials, which cannot be determined through trial and error, making modeling essential. This study presents a new TMD design for milling tools, optimized through Finite Element Method (FEM) modeling. The FEM analysis allows for maximizing damping efficiency through the precise selection of optimal dimensional parameters tailored to the specific tool geometry. A prototype of the optimized TMD tool was manufactured and experimentally tested, validating the FEM model through tap testing and showing significantly improved performance in machining tests, with reduced chatter compared to the original undamped tool.
{"title":"Finite Element Modeling to Design Optimized TMD for Milling Tools","authors":"Mikel Etxebeste , Gorka Ortiz-de-Zarate , Iñaki M. Arrieta , Pedro J. Arrazola","doi":"10.1016/j.procir.2025.02.077","DOIUrl":"10.1016/j.procir.2025.02.077","url":null,"abstract":"<div><div>Long milling tools are often limited in productivity due to chatter vibrations. Embedded Tuned Mass Dampers (TMDs) in these tools have proven to be an effective solution for reducing chatter and increasing productivity. The performance of TMDs is highly dependent on the correct dimensioning and selection of the most suitable damping materials, which cannot be determined through trial and error, making modeling essential. This study presents a new TMD design for milling tools, optimized through Finite Element Method (FEM) modeling. The FEM analysis allows for maximizing damping efficiency through the precise selection of optimal dimensional parameters tailored to the specific tool geometry. A prototype of the optimized TMD tool was manufactured and experimentally tested, validating the FEM model through tap testing and showing significantly improved performance in machining tests, with reduced chatter compared to the original undamped tool.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 448-453"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759171","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}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.019
Sangil Han , Emilie Viéville , Mehmet Cici , Thierry André , Frédéric Valiorgue , Joël Rech
This study presents process analysis and tool wear monitoring using spindle motor power and current signals in longitudinal and face turning. To achieve these tasks, longitudinal and face turnings with different levels of flank wear (new, VB150, VB300) were performed. In longitudinal turning, two workpieces with different diameters (Ø80 and Ø45 mm) were used. In face turning, two feed directions (uphill and downhill) were attempted. The spindle motor power, current and speed, as well as cutting forces were recorded in real time during turning. In longitudinal turning, the spindle motor power was found to be sensitive to the level of flank wear, which can be used for tool wear monitoring. Reliable cutting force prediction from spindle motor power (air cutting power + net cutting power) is also shown. In face turning, the tool exit time in the spindle motor power signal was found to be sensitive to tool geometry changes due to wear, suggesting that it can be used for tool wear monitoring. The spindle motor current and speed signals for all turning cases are also analyzed. The influence of the corresponding three-phase spindle motor connections (star and delta) on tool wear monitoring is discussed.
{"title":"Process analysis and tool wear monitoring with spindle motor power and current signals in longitudinal and face turning","authors":"Sangil Han , Emilie Viéville , Mehmet Cici , Thierry André , Frédéric Valiorgue , Joël Rech","doi":"10.1016/j.procir.2025.02.019","DOIUrl":"10.1016/j.procir.2025.02.019","url":null,"abstract":"<div><div>This study presents process analysis and tool wear monitoring using spindle motor power and current signals in longitudinal and face turning. To achieve these tasks, longitudinal and face turnings with different levels of flank wear (new, VB150, VB300) were performed. In longitudinal turning, two workpieces with different diameters (Ø80 and Ø45 mm) were used. In face turning, two feed directions (uphill and downhill) were attempted. The spindle motor power, current and speed, as well as cutting forces were recorded in real time during turning. In longitudinal turning, the spindle motor power was found to be sensitive to the level of flank wear, which can be used for tool wear monitoring. Reliable cutting force prediction from spindle motor power (air cutting power + net cutting power) is also shown. In face turning, the tool exit time in the spindle motor power signal was found to be sensitive to tool geometry changes due to wear, suggesting that it can be used for tool wear monitoring. The spindle motor current and speed signals for all turning cases are also analyzed. The influence of the corresponding three-phase spindle motor connections (star and delta) on tool wear monitoring is discussed.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 102-107"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759550","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}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.02.012
Gaetano Massimo Pittalà
Nickel-based alloys like Inconel718 are crucial in aerospace for their strength and resistance to thermal fatigue and corrosion, but they are difficult to machine due to rapid tool wear and high cutting forces. This study introduces a novel coolant design with holes connecting adjacent flutes. The new design helps to reduce flank wear thanks to better fluid flow around the cutting edge, as verified by CFD simulation. Experimental results showed that the new coolant system outperforms conventional methods, particularly when the tool wear is thermal driven. These demonstrate the potential of advanced coolant system to enhance the machinability of Inconel718.
{"title":"A new coolant supply for solid end mills in HRSA alloy machining","authors":"Gaetano Massimo Pittalà","doi":"10.1016/j.procir.2025.02.012","DOIUrl":"10.1016/j.procir.2025.02.012","url":null,"abstract":"<div><div>Nickel-based alloys like Inconel718 are crucial in aerospace for their strength and resistance to thermal fatigue and corrosion, but they are difficult to machine due to rapid tool wear and high cutting forces. This study introduces a novel coolant design with holes connecting adjacent flutes. The new design helps to reduce flank wear thanks to better fluid flow around the cutting edge, as verified by CFD simulation. Experimental results showed that the new coolant system outperforms conventional methods, particularly when the tool wear is thermal driven. These demonstrate the potential of advanced coolant system to enhance the machinability of Inconel718.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"133 ","pages":"Pages 61-65"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759646","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}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.06.006
Jun-Cheng Lu , Qing-Hai Liu , Ji-Chao Liu , Xue-Cheng Xi , Wan-Sheng Zhao , Qiang Wu , Jun-Liang Xu
Wire Electrical Discharge Machining (WEDM) is extensively applied in the mold and die industry, as well as in aerospace sectors, for the processing of components with complex surface geometries. The unit arc length incremental interpolation method (UALI) is a suitable approach for achieving high-precision four-axis simultaneous direct interpolation of wire cutting trajectories. To address the shortcomings and deficiencies of current UALI when considering tool radius compensation or wire electrode wear compensation, particularly in the handling of interpolation points at corners where multiple trajectories intersect, this paper proposes a tool compensation trajectory generation algorithm based on UALI. Simultaneously, taking taper components as an example, a corner processing strategy that considers the intersection of straight lines and arcs is introduced. To evaluate the superiority and limitations of the proposed algorithm, this paper includes an analysis of interpolation results and machining experiments conducted on a taper component. The results demonstrate that the proposed algorithm exhibits better adaptability and, compared to conventional algorithms, significantly improves geometric accuracy.
{"title":"Interpolation Algorithm for Taper Trajectory with Tool Compensation in WEDM Based on Unit Arc Length Incremental Interpolation Method","authors":"Jun-Cheng Lu , Qing-Hai Liu , Ji-Chao Liu , Xue-Cheng Xi , Wan-Sheng Zhao , Qiang Wu , Jun-Liang Xu","doi":"10.1016/j.procir.2025.06.006","DOIUrl":"10.1016/j.procir.2025.06.006","url":null,"abstract":"<div><div>Wire Electrical Discharge Machining (WEDM) is extensively applied in the mold and die industry, as well as in aerospace sectors, for the processing of components with complex surface geometries. The unit arc length incremental interpolation method (UALI) is a suitable approach for achieving high-precision four-axis simultaneous direct interpolation of wire cutting trajectories. To address the shortcomings and deficiencies of current UALI when considering tool radius compensation or wire electrode wear compensation, particularly in the handling of interpolation points at corners where multiple trajectories intersect, this paper proposes a tool compensation trajectory generation algorithm based on UALI. Simultaneously, taking taper components as an example, a corner processing strategy that considers the intersection of straight lines and arcs is introduced. To evaluate the superiority and limitations of the proposed algorithm, this paper includes an analysis of interpolation results and machining experiments conducted on a taper component. The results demonstrate that the proposed algorithm exhibits better adaptability and, compared to conventional algorithms, significantly improves geometric accuracy.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"137 ","pages":"Pages 164-169"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911612","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}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.06.002
Yuxin Yang , Chaojiang Li , Shenggui Liu , Wang Jiang , Dongyi Zou
Metal additive manufacturing (MAM) processes inevitably result in high surface roughness due to factors such as the step and balling effects, especially on the side surfaces parallel to the build direction. This severely limits their applicability in precision applications. Therefore, surface post-processing of MAM components is critical. Solid dielectric electrochemical polishing (SDECP) is an eco-friendly method that effectively addresses the high initial surface roughness of MAM components. This study investigates the effect of different processing energy distributions on polishing performance by adjusting the electric pulse duty cycle during SDECP at a constant current. Energy distributions, including 50%, 70%, and 90% duty cycle pulses, as well as direct current (DC), are experimentally tested, yielding surface roughness reductions of 70.3%, 74.1%, 85.2%, and 85.0%, respectively. These experimental results, along with SEM images, reveal that larger duty cycles lead to better polishing performance, while DC pulses cause pitting corrosion. This study offers valuable insights into the efficient surface post-processing of MAM components.
{"title":"Optimization of energy distribution over time for solid dielectric electrochemical polishing for additively manufactured parts","authors":"Yuxin Yang , Chaojiang Li , Shenggui Liu , Wang Jiang , Dongyi Zou","doi":"10.1016/j.procir.2025.06.002","DOIUrl":"10.1016/j.procir.2025.06.002","url":null,"abstract":"<div><div>Metal additive manufacturing (MAM) processes inevitably result in high surface roughness due to factors such as the step and balling effects, especially on the side surfaces parallel to the build direction. This severely limits their applicability in precision applications. Therefore, surface post-processing of MAM components is critical. Solid dielectric electrochemical polishing (SDECP) is an eco-friendly method that effectively addresses the high initial surface roughness of MAM components. This study investigates the effect of different processing energy distributions on polishing performance by adjusting the electric pulse duty cycle during SDECP at a constant current. Energy distributions, including 50%, 70%, and 90% duty cycle pulses, as well as direct current (DC), are experimentally tested, yielding surface roughness reductions of 70.3%, 74.1%, 85.2%, and 85.0%, respectively. These experimental results, along with SEM images, reveal that larger duty cycles lead to better polishing performance, while DC pulses cause pitting corrosion. This study offers valuable insights into the efficient surface post-processing of MAM components.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"137 ","pages":"Pages 46-51"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911702","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 electrical discharge machining (EDM) process exhibits variations in discharge timing and debris removal timing depending on various machining conditions and environments, making it challenging to establish a universally optimal setting. If the material removal mechanism could be fully elucidated, it would become possible to control the timing of debris removal, thereby enabling the efficient conversion of discharge energy into material removal energy. Understanding the material removal mechanism has the potential to facilitate high-efficiency machining and achieve high-precision finishing using EDM techniques. In this study, three distinct phenomena occurring during single pulse electrical discharge were observed: discharge arcs, debris ejection, and shock waves. For the observation of debris ejection, a light source with higher brightness than the discharge arc was employed, and a band-pass filter was used to visualize the debris as shadows. The observation of shock waves was conducted using the Schlieren method. It was confirmed that material removal occurs multiple times within a single discharge pulse. Additionally, shock waves were observed coinciding with the ejection of debris. This report presents an investigation into the relationship between the number of debris ejection events during the material removal process, the volume of material removed from discharge craters, and the surface area of the resulting craters.
{"title":"Observation of Machining Phenomena by Single Pulse Discharge","authors":"Atsutoshi Hirao , Hiromitsu Gotoh , Yoshiki Tsujita , Takayuki Tani","doi":"10.1016/j.procir.2025.02.260","DOIUrl":"10.1016/j.procir.2025.02.260","url":null,"abstract":"<div><div>The electrical discharge machining (EDM) process exhibits variations in discharge timing and debris removal timing depending on various machining conditions and environments, making it challenging to establish a universally optimal setting. If the material removal mechanism could be fully elucidated, it would become possible to control the timing of debris removal, thereby enabling the efficient conversion of discharge energy into material removal energy. Understanding the material removal mechanism has the potential to facilitate high-efficiency machining and achieve high-precision finishing using EDM techniques. In this study, three distinct phenomena occurring during single pulse electrical discharge were observed: discharge arcs, debris ejection, and shock waves. For the observation of debris ejection, a light source with higher brightness than the discharge arc was employed, and a band-pass filter was used to visualize the debris as shadows. The observation of shock waves was conducted using the Schlieren method. It was confirmed that material removal occurs multiple times within a single discharge pulse. Additionally, shock waves were observed coinciding with the ejection of debris. This report presents an investigation into the relationship between the number of debris ejection events during the material removal process, the volume of material removed from discharge craters, and the surface area of the resulting craters.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"137 ","pages":"Pages 19-24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911711","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}
Pub Date : 2025-01-01DOI: 10.1016/j.procir.2025.01.112
Cheng-Fang Su , Chun-Hao Yang , Shih-Hsin Huang , Jung-Chou Hung , Hai-Ping Tsui
In this study, the relationship between spark quality and surface roughness of SKD11 die steel in wire electrical discharge machining (WEDM) was investigated. The effects of normal sparks (NS), arc sparks (AS), and short sparks (SS) on the surface roughness of the workpiece were examined. Using a machine learning model optimized with hyperparameter tuning, the surface roughness of the workpiece was predicted by employing three input parameters: NS, AS, and SS counts. The artificial neural network (ANN) model optimized using the tree-structured Parzen estimation (TPE) method was used to predict the surface roughness of the workpiece.
The results indicated that the TPE-optimized ANN model exhibited the good performance in terms of mean absolute percentage error (MAPE). The validation MAPE was 1.86%, and the prediction of an additional 10 test groups revealed a MAPE of 1.01%.
{"title":"Prediction Analysis of Surface Roughness Based on Electrical Spark Quality","authors":"Cheng-Fang Su , Chun-Hao Yang , Shih-Hsin Huang , Jung-Chou Hung , Hai-Ping Tsui","doi":"10.1016/j.procir.2025.01.112","DOIUrl":"10.1016/j.procir.2025.01.112","url":null,"abstract":"<div><div>In this study, the relationship between spark quality and surface roughness of SKD11 die steel in wire electrical discharge machining (WEDM) was investigated. The effects of normal sparks (NS), arc sparks (AS), and short sparks (SS) on the surface roughness of the workpiece were examined. Using a machine learning model optimized with hyperparameter tuning, the surface roughness of the workpiece was predicted by employing three input parameters: NS, AS, and SS counts. The artificial neural network (ANN) model optimized using the tree-structured Parzen estimation (TPE) method was used to predict the surface roughness of the workpiece.</div><div>The results indicated that the TPE-optimized ANN model exhibited the good performance in terms of mean absolute percentage error (MAPE). The validation MAPE was 1.86%, and the prediction of an additional 10 test groups revealed a MAPE of 1.01%.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"137 ","pages":"Pages 40-45"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144911714","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号