Pub Date : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.078
Mark A. Rubeo
This paper presents the experimental validation of the amplitude ratio, a metric for milling stability identification. The amplitude ratio quantifies the severity of chatter by comparing the amplitude of the expected frequency content of a milling signal (i.e., tooth passing frequency, runout frequency, and harmonics) to the amplitude of the chatter frequency, if present. Through multiple iterations of a milling time domain simulation, the amplitude ratio diagram, which characterizes stable and unstable milling behavior over a range of spindle speeds and axial depths of cut, may be generated. In this paper, a comparison of the simulated and measured amplitude ratios for a series of milling test cuts is presented. It is shown that the amplitude ratio is suitable for identifying milling stability in both simulations and experiments. Additionally, it is shown that through judicious selection of low-cost sensors, implementation of the amplitude ratio is cost efficient. Direct comparison of the simulated and measured amplitude ratios demonstrates the effectiveness of the approach.
{"title":"Experimental validation of the amplitude ratio as a metric for milling stability identification","authors":"Mark A. Rubeo","doi":"10.1016/j.mfglet.2024.09.078","DOIUrl":"10.1016/j.mfglet.2024.09.078","url":null,"abstract":"<div><div>This paper presents the experimental validation of the amplitude ratio, a metric for milling stability identification. The amplitude ratio quantifies the severity of chatter by comparing the amplitude of the expected frequency content of a milling signal (i.e., tooth passing frequency, runout frequency, and harmonics) to the amplitude of the chatter frequency, if present. Through multiple iterations of a milling time domain simulation, the amplitude ratio diagram, which characterizes stable and unstable milling behavior over a range of spindle speeds and axial depths of cut, may be generated. In this paper, a comparison of the simulated and measured amplitude ratios for a series of milling test cuts is presented. It is shown that the amplitude ratio is suitable for identifying milling stability in both simulations and experiments. Additionally, it is shown that through judicious selection of low-cost sensors, implementation of the amplitude ratio is cost efficient. Direct comparison of the simulated and measured amplitude ratios demonstrates the effectiveness of the approach.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 610-618"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434409","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.039
David Fieser , Lingyue Zhang , Matthew Yao , Hugh Shortt , Peter Liaw , Anming Hu
This research explores the practicality of fusing Stellite 6, a cobalt-chromium alloy known for its high performance, with stainless steel, utilizing various laser welding approaches. The primary challenge addressed is the joining of dissimilar materials, which presents obstacles such as divergent melting points and disparate coefficients of thermal expansion. The aim is to achieve a metallurgical bond between Stellite and stainless steel that retains desirable properties. The study employs both continuous wave and femtosecond laser welding techniques, subjecting the resultant joints to rigorous analysis to assess their impact on the properties of the bond. Initial tensile testing delineated the intrinsic mechanical characteristics of the materials, revealing that while Stellite exhibits a lower ultimate tensile strength, it compensates with greater elongation compared to stainless steel. The use of continuous wave laser welding proved to be capable of creating the bond; however, it also precipitated a considerable decline in the tensile strength of the Stellite component as a result of the thermal processing involved. In contrast, femtosecond laser welding emerged as a more effective method, enhancing the joint’s overall strength and ductility. This improvement is attributed to the femtosecond laser’s precise control over thermal exposure, which confines the heat to the intended weld zone, thereby safeguarding the adjacent material from damage. Further insights were gleaned from Scanning Electron Microscopy, which showed a preferable intergranular fracture in samples welded with the femtosecond laser—a feature typically associated with ductile failure modes. The femtosecond laser welding approach culminated in a joint efficiency of 53.7%, mirroring the innate yield strength of the Stellite wire. This outcome suggests that such welded joints possess the requisite robustness for practical deployment, thus underscoring the potential of femtosecond laser welding in applications requiring the joining of Stellite to stainless steel.
{"title":"Femtosecond laser joining of Stellite and stainless steel","authors":"David Fieser , Lingyue Zhang , Matthew Yao , Hugh Shortt , Peter Liaw , Anming Hu","doi":"10.1016/j.mfglet.2024.09.039","DOIUrl":"10.1016/j.mfglet.2024.09.039","url":null,"abstract":"<div><div>This research explores the practicality of fusing Stellite 6, a cobalt-chromium alloy known for its high performance, with stainless steel, utilizing various laser welding approaches. The primary challenge addressed is the joining of dissimilar materials, which presents obstacles such as divergent melting points and disparate coefficients of thermal expansion. The aim is to achieve a metallurgical bond between Stellite and stainless steel that retains desirable properties. The study employs both continuous wave and femtosecond laser welding techniques, subjecting the resultant joints to rigorous analysis to assess their impact on the properties of the bond. Initial tensile testing delineated the intrinsic mechanical characteristics of the materials, revealing that while Stellite exhibits a lower ultimate tensile strength, it compensates with greater elongation compared to stainless steel. The use of continuous wave laser welding proved to be capable of creating the bond; however, it also precipitated a considerable decline in the tensile strength of the Stellite component as a result of the thermal processing involved. In contrast, femtosecond laser welding emerged as a more effective method, enhancing the joint’s overall strength and ductility. This improvement is attributed to the femtosecond laser’s precise control over thermal exposure, which confines the heat to the intended weld zone, thereby safeguarding the adjacent material from damage. Further insights were gleaned from Scanning Electron Microscopy, which showed a preferable intergranular fracture in samples welded with the femtosecond laser—a feature typically associated with ductile failure modes. The femtosecond laser welding approach culminated in a joint efficiency of 53.7%, mirroring the innate yield strength of the Stellite wire. This outcome suggests that such welded joints possess the requisite robustness for practical deployment, thus underscoring the potential of femtosecond laser welding in applications requiring the joining of Stellite to stainless steel.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 332-338"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434242","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.027
Vignesh Selvaraj , Aditya Nagaraj , Benjamin Gregory Whiffen, Sangkee Min
With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.
{"title":"Development of a wireless smart sensor system and case study on lifting risk assessment","authors":"Vignesh Selvaraj , Aditya Nagaraj , Benjamin Gregory Whiffen, Sangkee Min","doi":"10.1016/j.mfglet.2024.09.027","DOIUrl":"10.1016/j.mfglet.2024.09.027","url":null,"abstract":"<div><div>With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 229-240"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434250","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.030
Cristian I. Garcia, Marcus A. DiBattista, Tomás A. Letelier, Hunter D. Halloran, Jaime A. Camelio
In the era of Industry 4.0, the proliferation of data within manufacturing environments has presented both unprecedented opportunities and challenges. This paper introduces a framework that capitalizes on the capabilities of Large Language Models (LLMs) to revolutionize data integration and decision-making processes in manufacturing systems. Addressing the critical need for efficient data management, our framework streamlines the consolidation, processing, and generation of responses to essential inquiries, thus enhancing manufacturers’ capabilities to extract valuable insights. The focus of this paper is twofold. First to establish a framework for the use of LLM applications in manufacturing settings. Secondly, to provide an overview of the manufacturing connection between data, AI, and chat-bots, while also addressing a few pain points identified from the manufacturing literature. The paper then introduces FILLIS (Factory Integrated Logic and Language Interface System), a Large Language Model assistant, through a compelling case study. FILLIS showcases remarkable versatility, excelling in tasks ranging from elucidating machine operations to language translation. The study underscores FILLIS’s proficiency in handling specific contexts, answering questions from uploaded documents with precision. However, inherent limitations surface in tasks involving mathematical operations, emphasizing the need for external agents in specific scenarios. This pivotal opportunity is explored in the proposed framework as it advocates for integrating external agents alongside LLMs, creating a more versatile and comprehensive assistant tool. The findings of this paper and proposed framework position LLMs as transformative tools for intelligent data processing.
{"title":"Framework for LLM applications in manufacturing","authors":"Cristian I. Garcia, Marcus A. DiBattista, Tomás A. Letelier, Hunter D. Halloran, Jaime A. Camelio","doi":"10.1016/j.mfglet.2024.09.030","DOIUrl":"10.1016/j.mfglet.2024.09.030","url":null,"abstract":"<div><div>In the era of Industry 4.0, the proliferation of data within manufacturing environments has presented both unprecedented opportunities and challenges. This paper introduces a framework that capitalizes on the capabilities of Large Language Models (LLMs) to revolutionize data integration and decision-making processes in manufacturing systems. Addressing the critical need for efficient data management, our framework streamlines the consolidation, processing, and generation of responses to essential inquiries, thus enhancing manufacturers’ capabilities to extract valuable insights. The focus of this paper is twofold. First to establish a framework for the use of LLM applications in manufacturing settings. Secondly, to provide an overview of the manufacturing connection between data, AI, and chat-bots, while also addressing a few pain points identified from the manufacturing literature. The paper then introduces FILLIS (<em>Factory Integrated Logic and Language Interface System</em>), a Large Language Model assistant, through a compelling case study. FILLIS showcases remarkable versatility, excelling in tasks ranging from elucidating machine operations to language translation. The study underscores FILLIS’s proficiency in handling specific contexts, answering questions from uploaded documents with precision. However, inherent limitations surface in tasks involving mathematical operations, emphasizing the need for external agents in specific scenarios. This pivotal opportunity is explored in the proposed framework as it advocates for integrating external agents alongside LLMs, creating a more versatile and comprehensive assistant tool. The findings of this paper and proposed framework position LLMs as transformative tools for intelligent data processing.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 253-263"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434278","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.031
Philip A. Olubodun, Joseph D. Fischer, Douglas A. Bristow
Since the advent of robots, many tasks that were originally performed by humans have now been tasked to industrial robots. From a manufacturing standpoint, robots have primarily been used in pick-and-place or other non-machining operations that require high repeatability. However, with the increasing availability of CAD/CAM software and the development of high-precision metrology, comes the opportunity to integrate robots into a wider variety of manufacturing processes through the use of feedback control. One such machining operation that is being explored is precision grinding of metal parts. Most other work in this area has focused on force regulation to improve grind quality; however, this paper takes a different approach. In this work, an Iterative Learning Control (ILC) algorithm is implemented to correct the geometric error directly by altering the toolpath trajectory. Specifically, in this framework, a conservative initial cutting trajectory is implemented using a 6-DoF robotic grinding system, and the resulting part geometry is measured via a high-precision laser scanner. Based on the resultant geometric error, the toolpath is corrected and then rerun on the part. This process is then repeated iteratively until sufficient accuracy is achieved. Due to the inability to replace material in overground regions, the controller is designed with an emphasis on reducing overshoot which cannot be corrected. The controller is experimentally validated by grinding an elliptical pocket which meets FAA specifications for corrosion removal in aircraft. The results showed that within seven iterations the entire error surface could be brought to a tolerance of ±0.150 mm for the given geometry.
{"title":"Iterative correction of robotic grinding using spatial feedback for precision applications","authors":"Philip A. Olubodun, Joseph D. Fischer, Douglas A. Bristow","doi":"10.1016/j.mfglet.2024.09.031","DOIUrl":"10.1016/j.mfglet.2024.09.031","url":null,"abstract":"<div><div>Since the advent of robots, many tasks that were originally performed by humans have now been tasked to industrial robots. From a manufacturing standpoint, robots have primarily been used in pick-and-place or other non-machining operations that require high repeatability. However, with the increasing availability of CAD/CAM software and the development of high-precision metrology, comes the opportunity to integrate robots into a wider variety of manufacturing processes through the use of feedback control. One such machining operation that is being explored is precision grinding of metal parts. Most other work in this area has focused on force regulation to improve grind quality; however, this paper takes a different approach. In this work, an Iterative Learning Control (ILC) algorithm is implemented to correct the geometric error directly by altering the toolpath trajectory. Specifically, in this framework, a conservative initial cutting trajectory is implemented using a 6-DoF robotic grinding system, and the resulting part geometry is measured via a high-precision laser scanner. Based on the resultant geometric error, the toolpath is corrected and then rerun on the part. This process is then repeated iteratively until sufficient accuracy is achieved. Due to the inability to replace material in overground regions, the controller is designed with an emphasis on reducing overshoot which cannot be corrected. The controller is experimentally validated by grinding an elliptical pocket which meets FAA specifications for corrosion removal in aircraft. The results showed that within seven iterations the entire error surface could be brought to a tolerance of ±0.150 mm for the given geometry.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 264-269"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434279","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.096
Akshar Kota, Shohom Bose-Bandyopadhyay, Asif Rashid, Shreyes N. Melkote
The Hybrid Wire-Arc Direct Energy Deposition (Hybrid Wire-Arc DED) process integrates Wire-Arc Direct Energy Deposition (Wire-Arc DED) with machining (typically milling) interventions, offering the potential for creating intricate geometries and finished surfaces. However, if milling is employed as a hybrid intervention rather than as a final part-finishing process, the interplay between these processes remains under-investigated. This paper examines the influence of milling interventions on the geometry of a wall-shaped structure, quantified by the transverse cross-sectional width, built using a Hybrid Wire-Arc DED. Through experiments on mild steel, the underlying causes of observed wall-width variations are analyzed. Initial observations suggested that thermo-mechanical deformations from milling influence the width variations. However, evidence indicates the significant role of additional remelting cycles experienced by the milled surface layer during subsequent layer depositions. The study also reveals that the observed increase in wall width for each milling intervention occurs at approximately the same depth below the milled surface. A mechanistic explanation for this observation is given. Crucially, the findings suggest that unless milling is done at higher frequencies, like after each layer deposition, the resultant unevenness might render the Hybrid Wire-Arc DED process less efficient in terms of surface quality and dimensional accuracy than its non-hybrid counterpart.
{"title":"Influence of milling interventions on the geometry of wall-shaped structures in hybrid wire-arc direct energy deposition","authors":"Akshar Kota, Shohom Bose-Bandyopadhyay, Asif Rashid, Shreyes N. Melkote","doi":"10.1016/j.mfglet.2024.09.096","DOIUrl":"10.1016/j.mfglet.2024.09.096","url":null,"abstract":"<div><div>The Hybrid Wire-Arc Direct Energy Deposition (Hybrid Wire-Arc DED) process integrates Wire-Arc Direct Energy Deposition (Wire-Arc DED) with machining (typically milling) interventions, offering the potential for creating intricate geometries and finished surfaces. However, if milling is employed as a hybrid intervention rather than as a final part-finishing process, the interplay between these processes remains under-investigated. This paper examines the influence of milling interventions on the geometry of a wall-shaped structure, quantified by the transverse cross-sectional width, built using a Hybrid Wire-Arc DED. Through experiments on mild steel, the underlying causes of observed wall-width variations are analyzed. Initial observations suggested that thermo-mechanical deformations from milling influence the width variations. However, evidence indicates the significant role of additional remelting cycles experienced by the milled surface layer during subsequent layer depositions. The study also reveals that the observed increase in wall width for each milling intervention occurs at approximately the same depth below the milled surface. A mechanistic explanation for this observation is given. Crucially, the findings suggest that unless milling is done at higher frequencies, like after each layer deposition, the resultant unevenness might render the Hybrid Wire-Arc DED process less efficient in terms of surface quality and dimensional accuracy than its non-hybrid counterpart.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 772-779"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434288","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.100
Daniyar Syrlybayev, Andrei Yankin, Asma Perveen, Didier Talamona
This study, designed new lattice structures using vertical struts that taper off. The degree of tapering was controlled using a parameter called “α”. To fabricate these structures, 3D-printing technology known as SLM (selected laser melting) was used. These lattice structures were also simulated using finite element analysis (FEA) and tested experimentally. The used material was 316L stainless steel. Stress–strain curves provided insights into their deformation behavior, revealing a noteworthy occurrence: the unloading modulus exceeded the loading modulus. The mechanical properties of these absolute and density-normalized lattice structures, demonstrated improvement with higher values of the shape parameter α. Yield stress increased by 31 %, loading modulus by 21 %, and energy absorption by 33 %. Specific yield stress improved by 24 %, and specific energy absorption increased by 27 %. While simulation and experimental results exhibited a correlation, they differed significantly in modulus estimation, with simulations overestimating it by more than 30 %.
{"title":"SLM-printed lattice structures with tapered vertical struts: Design, simulation and experimentation","authors":"Daniyar Syrlybayev, Andrei Yankin, Asma Perveen, Didier Talamona","doi":"10.1016/j.mfglet.2024.09.100","DOIUrl":"10.1016/j.mfglet.2024.09.100","url":null,"abstract":"<div><div>This study, designed new lattice structures using vertical struts that taper off. The degree of tapering was controlled using a parameter called “α”. To fabricate these structures, 3D-printing technology known as SLM (selected laser melting) was used. These lattice structures were also simulated using finite element analysis (FEA) and tested experimentally. The used material was 316L stainless steel. Stress–strain curves provided insights into their deformation behavior, revealing a noteworthy occurrence: the unloading modulus exceeded the loading modulus. The mechanical properties of these absolute and density-normalized lattice structures, demonstrated improvement with higher values of the shape parameter α. Yield stress increased by 31 %, loading modulus by 21 %, and energy absorption by 33 %. Specific yield stress improved by 24 %, and specific energy absorption increased by 27 %. While simulation and experimental results exhibited a correlation, they differed significantly in modulus estimation, with simulations overestimating it by more than 30 %.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 803-809"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434289","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.098
Riccardo C. Clemente, Seyed A. Niknam
The recent Additive manufacturing (AM) literature has primarily concentrated on exploring new avenues for improving the current technology and its applicability. It has also delved into research investments aimed at addressing the last remaining prediction challenges associated with current AM processes, particularly focusing to surface quality, accuracy, and internal composition. These limitations can often be mitigated though the application of post-processing techniques. Such techniques are often very costly both in time and monetary terms.
When it comes to the impact that shape complexity has on post-fabrication costs for AM parts, a gap in the literature is apparent. In recent years, more attention has been devoted to researching a general shape complexity metric. It has been suggested in the literature to combine multiple of complexity metrics techniques, to reach more comprehensive model. This aspect has not received enough attention in previous works. In addition, the relationship between shape complexity and post-processing costs has not been assessed. And there are no predictive models for post-processing costs based on complexity.
In this study, AM shapes for prototyping application are analysed. In this regard, previously established complexity metrics are used, together with expert’s assessments of post-processing costs, to create a model capable of predicting post-processing costs. This is achieved through a regression analysis using costs and complexity metrics values. The result of this research are two regression models, named 7 V and Vol/Sur Models, capable of predicting post-processing costs for AM parts produced through DMLS techniques with SS316L stainless steel powder. The accuracy of the two models is discussed.
近期的增材制造（AM）文献主要集中在探索改进当前技术及其适用性的新途径。此外，还深入开展研究投资，旨在解决与当前增材制造工艺相关的最后遗留的预测难题，尤其侧重于表面质量、精度和内部组成。这些限制通常可以通过应用后处理技术来缓解。当谈到形状复杂性对 AM 零件后加工成本的影响时，文献中的空白是显而易见的。近年来，越来越多的人开始关注通用形状复杂性指标的研究。有文献建议结合多种复杂度度量技术，以建立更全面的模型。在以往的研究中，这方面还没有得到足够的重视。此外，形状复杂度与后处理成本之间的关系也没有得到评估。本研究分析了用于原型制作的 AM 形状。在这方面，使用了之前建立的复杂性指标，并结合专家对后处理成本的评估，创建了一个能够预测后处理成本的模型。这是通过使用成本和复杂性指标值进行回归分析实现的。这项研究的成果是两个回归模型，分别命名为 7 V 和 Vol/Sur 模型，能够预测使用 SS316L 不锈钢粉末通过 DMLS 技术生产的 AM 零件的后处理成本。本文讨论了这两个模型的准确性。
{"title":"Applying design complexity metrics for post-processing cost modeling in metal additive manufacturing","authors":"Riccardo C. Clemente, Seyed A. Niknam","doi":"10.1016/j.mfglet.2024.09.098","DOIUrl":"10.1016/j.mfglet.2024.09.098","url":null,"abstract":"<div><div>The recent Additive manufacturing (AM) literature has primarily concentrated on exploring new avenues for improving the current technology and its applicability. It has also delved into research investments aimed at addressing the last remaining prediction challenges associated with current AM processes, particularly focusing to surface quality, accuracy, and internal composition. These limitations can often be mitigated though the application of post-processing techniques. Such techniques are often very costly both in time and monetary terms.</div><div>When it comes to the impact that shape complexity has on post-fabrication costs for AM parts, a gap in the literature is apparent. In recent years, more attention has been devoted to researching a general shape complexity metric. It has been suggested in the literature to combine multiple of complexity metrics techniques, to reach more comprehensive model. This aspect has not received enough attention in previous works. In addition, the relationship between shape complexity and post-processing costs has not been assessed. And there are no predictive models for post-processing costs based on complexity.</div><div>In this study, AM shapes for prototyping application are analysed. In this regard, previously established complexity metrics are used, together with expert’s assessments of post-processing costs, to create a model capable of predicting post-processing costs. This is achieved through a regression analysis using costs and complexity metrics values. The result of this research are two regression models, named 7 V and Vol/Sur Models, capable of predicting post-processing costs for AM parts produced through DMLS techniques with SS316L stainless steel powder. The accuracy of the two models is discussed.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 787-794"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434291","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 : 2024-10-01DOI: 10.1016/j.mfglet.2024.09.089
Mandar Shinde, Irving E. Ramirez-Chavez, Alexander Potts, Dhruv Bhate
Densification strain is an essential parameter in the characterization of energy absorption of additively manufactured cellular structures. In addition to its own merits as a metric that indicates usable stroke length for energy absorbers, it is central to the computation of energy absorbed by the structure. However, at least four different approaches have been used in the literature, each with its own limitations. In this work, a critical review of these approaches is first presented. While the maximum efficiency approach has been demonstrated to be optimal for cellular foams, this work shows how, for some additively manufactured cellular materials, it can fail to estimate densification strain accurately due to its sensitivity to instantaneous stress values in the plateau region. An alternative method is proposed in this work that leverages peak stress instead to determine the onset strain of densification and is shown to be consistently accurate across a range of cellular materials. The method is validated with the results from an experimental study of energy absorption in six different types of cellular structures across three relative densities, with identical geometries fabricated in two different base materials and processes: AlSi10Mg with Laser Powder Bed Fusion, and Nylon-12 with Selective Laser Sintering.
{"title":"A critical assessment of the onset strain of densification in the evaluation of energy absorption for additively manufactured cellular materials","authors":"Mandar Shinde, Irving E. Ramirez-Chavez, Alexander Potts, Dhruv Bhate","doi":"10.1016/j.mfglet.2024.09.089","DOIUrl":"10.1016/j.mfglet.2024.09.089","url":null,"abstract":"<div><div>Densification strain is an essential parameter in the characterization of energy absorption of additively manufactured cellular structures. In addition to its own merits as a metric that indicates usable stroke length for energy absorbers, it is central to the computation of energy absorbed by the structure. However, at least four different approaches have been used in the literature, each with its own limitations. In this work, a critical review of these approaches is first presented. While the maximum efficiency approach has been demonstrated to be optimal for cellular foams, this work shows how, for some additively manufactured cellular materials, it can fail to estimate densification strain accurately due to its sensitivity to instantaneous stress values in the plateau region. An alternative method is proposed in this work that leverages peak stress instead to determine the onset strain of densification and is shown to be consistently accurate across a range of cellular materials. The method is validated with the results from an experimental study of energy absorption in six different types of cellular structures across three relative densities, with identical geometries fabricated in two different base materials and processes: AlSi10Mg with Laser Powder Bed Fusion, and Nylon-12 with Selective Laser Sintering.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 708-719"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434298","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}
Traditional top-down approaches for producing metallic nanostructures, despite being capable of producing arbitrary 2-D shapes, often use vacuum-based deep sub-micron lithographic fabrication technologies. This makes their use for single-use devices like chemical and bio-sensing substrates difficult to economically justify. Here, the authors demonstrate a manufacturing pathway that only uses such techniques to produce a master. This reusable master, coupled with a unique and facile electrochemical imprinting process, Solid-State Superionic Stamping (S4), is used to produce several replicated metallic nanostructures, thus demonstrating an economically feasible manufacturing pathway for single-use, nano-enabled devices.
This paper uses plasmonic image reproduction as an easy-to-visualize proxy for single-use devices such as plasmonic sensors and Surface Enhanced Raman Spectroscopy (SERS) substrates that require nanopatterned metallic structures. It demonstrates a process for replicating a picture by a set of metallic structures that plasmonically produce the desired colors locally. It uses a digitizing computational tool, direct-write Two-Photon Lithography (TPL) and a dry-etch process to rapidly produce a silicon master. This master is used to hot emboss nano-patterns in superionic glass blanks that, in turn, are used for electrochemical imprinting with S4 to reproduce the patterns on Ag substrates. The different steps in this process flow are described along with their role and effectiveness in contributing to a high-fidelity plasmonic image reproduction.
{"title":"Plasmonic image reproduction with solid-state superionic stamping (S4)","authors":"Boqiang Qian, Papia Sultana, Ricardo Toro, Glennys Mensing, Placid Ferreira","doi":"10.1016/j.mfglet.2024.09.073","DOIUrl":"10.1016/j.mfglet.2024.09.073","url":null,"abstract":"<div><div>Traditional top-down approaches for producing metallic nanostructures, despite being capable of producing arbitrary 2-D shapes, often use vacuum-based deep sub-micron lithographic fabrication technologies. This makes their use for single-use devices like chemical and bio-sensing substrates difficult to economically justify. Here, the authors demonstrate a manufacturing pathway that only uses such techniques to produce a master. This reusable master, coupled with a unique and facile electrochemical imprinting process, Solid-State Superionic Stamping (S4), is used to produce several replicated metallic nanostructures, thus demonstrating an economically feasible manufacturing pathway for single-use, nano-enabled devices.</div><div>This paper uses plasmonic image reproduction as an easy-to-visualize proxy for single-use devices such as plasmonic sensors and Surface Enhanced Raman Spectroscopy (SERS) substrates that require nanopatterned metallic structures. It demonstrates a process for replicating a picture by a set of metallic structures that plasmonically produce the desired colors locally. It uses a digitizing computational tool, direct-write Two-Photon Lithography (TPL) and a dry-etch process to rapidly produce a silicon master. This master is used to hot emboss nano-patterns in superionic glass blanks that, in turn, are used for electrochemical imprinting with S4 to reproduce the patterns on Ag substrates. The different steps in this process flow are described along with their role and effectiveness in contributing to a high-fidelity plasmonic image reproduction.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"41 ","pages":"Pages 575-580"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434402","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}
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