Pub Date : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.048
Emmanuel Olorundaisi, Peter A. Olubambi
Microstructural changes and hardness behaviour of equal atomic Ni-Al-Co-Fe-Mn-Ti-Cr High-Entropy Alloy (HEA) subjected to heat treatment in oxidative and non-oxidative environments were investigated. The samples were annealed for four hours at a temperature of 700 °C. The microstructure revealed the formation of a well-refined granular and needle-like eutectic phase with an average size. An oxidized layer was observed on the surface of the sample heat-treated in an oxidized environment. The heat-treated samples exhibited improved ductility with a drop in hardness value from 136.3 HV for the non-heat-treated to 98.1 and 92.8 HV for the heat-treated in an oxidized and non-oxidized environment, respectively. The heat treatment results can be considered a promising approach for producing high-performance HEAs, particularly for advanced engineering applications.
{"title":"Evaluating microstructural changes and hardness in equal atomic Ni-Al-Co-Fe-Mn-Ti-Cr high-entropy alloy subjected to heat treatment in oxidative and non-oxidative environments","authors":"Emmanuel Olorundaisi, Peter A. Olubambi","doi":"10.1016/j.mfglet.2025.06.048","DOIUrl":"10.1016/j.mfglet.2025.06.048","url":null,"abstract":"<div><div>Microstructural changes and hardness behaviour of equal atomic Ni-Al-Co-Fe-Mn-Ti-Cr High-Entropy Alloy (HEA) subjected to heat treatment in oxidative and non-oxidative environments were investigated. The samples were annealed for four hours at a temperature of 700 °C. The microstructure revealed the formation of a well-refined granular and needle-like eutectic phase with an average size. An oxidized layer was observed on the surface of the sample heat-treated in an oxidized environment. The heat-treated samples exhibited improved ductility with a drop in hardness value from 136.3 HV for the non-heat-treated to 98.1 and 92.8 HV for the heat-treated in an oxidized and non-oxidized environment, respectively. The heat treatment results can be considered a promising approach for producing high-performance HEAs, particularly for advanced engineering applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 405-415"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926606","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 : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.016
Wenhang Dong , Shufei Li , Pai Zheng , Liang Liu , Shuo Chen
With the exploration of digital transformation in the industry, the introduction of the industrial metaverse is bringing unprecedented opportunities and challenges to the manufacturing industry. In the industrial metaverse, humans can interact safely and naturally with robots in high-fidelity digital environments, enabling non-technical operators to quickly validate industrial scenarios and help optimize decision-making and production processes. However, the complexity of Three-Dimensional (3D) modeling poses a challenge to achieving this goal. Additionally, programming-based Human Robot Interaction (HRI) also presents obstacles, as operators need significant time to learn how to control robots. Therefore, this paper proposes a 3D Gaussian Splatting (3DGS) and Large Language Model (LLM)-based physical-to-virtual approach for human-robot interactive manufacturing, which further facilitates digital interaction for non-technical operators in manufacturing environments. Specifically, 3DGS is first used for rapid visualization and reconstruction of the overall scene, achieving new perspective rendering and providing a gaussian ellipsoid representation. Then mesh extraction algorithms based on gaussian representation are used to build a physical-to-virtual transfer framework. Finally, LLM is utilized for understanding natural language commands and generating virtual robot Python programming to complete robot assembly tasks. This framework is implemented in the Isaac Sim simulator, and the case study shows that the proposed framework can quickly and accurately complete physical-to-virtual transfer and accomplish robot assembly manufacturing tasks in the simulator with low code.
{"title":"A 3DGS and LLM-based physical-to-virtual approach for human-robot interactive manufacturing","authors":"Wenhang Dong , Shufei Li , Pai Zheng , Liang Liu , Shuo Chen","doi":"10.1016/j.mfglet.2025.06.016","DOIUrl":"10.1016/j.mfglet.2025.06.016","url":null,"abstract":"<div><div>With the exploration of digital transformation in the industry, the introduction of the industrial metaverse is bringing unprecedented opportunities and challenges to the manufacturing industry. In the industrial metaverse, humans can interact safely and naturally with robots in high-fidelity digital environments, enabling non-technical operators to quickly validate industrial scenarios and help optimize decision-making and production processes. However, the complexity of Three-Dimensional (3D) modeling poses a challenge to achieving this goal. Additionally, programming-based Human Robot Interaction (HRI) also presents obstacles, as operators need significant time to learn how to control robots. Therefore, this paper proposes a 3D Gaussian Splatting (3DGS) and Large Language Model (LLM)-based physical-to-virtual approach for human-robot interactive manufacturing, which further facilitates digital interaction for non-technical operators in manufacturing environments. Specifically, 3DGS is first used for rapid visualization and reconstruction of the overall scene, achieving new perspective rendering and providing a gaussian ellipsoid representation. Then mesh extraction algorithms based on gaussian representation are used to build a physical-to-virtual transfer framework. Finally, LLM is utilized for understanding natural language commands and generating virtual robot Python programming to complete robot assembly tasks. This framework is implemented in the Isaac Sim simulator, and the case study shows that the proposed framework can quickly and accurately complete physical-to-virtual transfer and accomplish robot assembly manufacturing tasks in the simulator with low code.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 121-128"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a novel gyroscopic spindle actuator designed for vibration suppression in robotic machining, addressing the limitations caused by the compliance and low stiffness of industrial robots. The actuator utilizes a rotating flywheel, driven by an air-pressure turbine, to generate stabilizing gyroscopic moments, enhancing machining precision and stability. Key design features include a lightweight structure, a wireless optical angular speed sensor, and an electro-pneumatic proportional valve for flywheel speed control. A proportional-integral control algorithm, using accelerometer feedback, enables real-time adjustment of the gyroscopic moment to counteract vibrations effectively. Experimental validation demonstrated significant suppression of low-frequency vibrations, particularly at about 6 Hz, alongside reductions in higher-frequency structural vibrations. These results highlight the actuator’s ability to improve surface quality and machining stability while maintaining reliable performance across various conditions. This work shows the potential of gyroscopic spindle actuators to overcome vibration-induced challenges in robotic machining and offers a foundation for future advancements in robotic manufacturing systems.
{"title":"A novel vibration suppressing method for robotic machining by inertial moment actuator using gyroscopic spindle","authors":"Jongyoup Shim, Jooho Hwang, Seung Guk Baek, Seung Kook Ro","doi":"10.1016/j.mfglet.2025.06.017","DOIUrl":"10.1016/j.mfglet.2025.06.017","url":null,"abstract":"<div><div>This study presents a novel gyroscopic spindle actuator designed for vibration suppression in robotic machining, addressing the limitations caused by the compliance and low stiffness of industrial robots. The actuator utilizes a rotating flywheel, driven by an air-pressure turbine, to generate stabilizing gyroscopic moments, enhancing machining precision and stability. Key design features include a lightweight structure, a wireless optical angular speed sensor, and an electro-pneumatic proportional valve for flywheel speed control. A proportional-integral control algorithm, using accelerometer feedback, enables real-time adjustment of the gyroscopic moment to counteract vibrations effectively. Experimental validation demonstrated significant suppression of low-frequency vibrations, particularly at about 6 Hz, alongside reductions in higher-frequency structural vibrations. These results highlight the actuator’s ability to improve surface quality and machining stability while maintaining reliable performance across various conditions. This work shows the potential of gyroscopic spindle actuators to overcome vibration-induced challenges in robotic machining and offers a foundation for future advancements in robotic manufacturing systems.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 129-135"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926615","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 : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.057
Pedro Doukas, Sha Ouyang, Jinjin Ha, Brad Kinsey
Manufacturing-based robotic platforms with various end effectors to replicate different processes can provide cheaper and space efficient point-of-need capabilities. One such process of interest is robotic blacksmithing that can, e.g., provide a means to post-process composite materials with voids and material imperfections that are fabricated through additive manufacturing. In this paper, a robotic forging end effector is developed and used to cold forge, actually coin, an AA6061-T6 block. Analyses of deformation, both experimental and numerical simulations, allows for the changes to the material, hardness, penetration of the strain hardening, and surface properties to be measured. Penetration data provides insight into the thickness and number of material layers that can be deposited in an additive manufacturing process prior to such a post-processing step to affect the material and layer interface characteristics. Analysis of surface properties reveal the physical changes to the metal that affect its material characteristics.
{"title":"Development and initial testing of robotic blacksmithing apparatus","authors":"Pedro Doukas, Sha Ouyang, Jinjin Ha, Brad Kinsey","doi":"10.1016/j.mfglet.2025.06.057","DOIUrl":"10.1016/j.mfglet.2025.06.057","url":null,"abstract":"<div><div>Manufacturing-based robotic platforms with various end effectors to replicate different processes can provide cheaper and space efficient point-of-need capabilities. One such process of interest is robotic blacksmithing that can, e.g., provide a means to post-process composite materials with voids and material imperfections that are fabricated through additive manufacturing. In this paper, a robotic forging end effector is developed and used to cold forge, actually coin, an AA6061-T6 block. Analyses of deformation, both experimental and numerical simulations, allows for the changes to the material, hardness, penetration of the strain hardening, and surface properties to be measured. Penetration data provides insight into the thickness and number of material layers that can be deposited in an additive manufacturing process prior to such a post-processing step to affect the material and layer interface characteristics. Analysis of surface properties reveal the physical changes to the metal that affect its material characteristics.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 481-486"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926533","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}
Industry 4.0 technologies have increased the complexity and interconnectivity of manufacturing systems, challenging the conventional scope of Value Stream Mapping (VSM). In response, this paper proposes a Multi-Layer Multi-Variable Value Stream Mapping (MLMV-VSM) framework that integrates operational, environmental, and social layers within a single methodology. The approach captures Key Performance Indicators (KPIs) and their interdependencies, enabling more balanced system optimization. Unlike traditional VSM, MLMV-VSM explicitly incorporates human-centric metrics, such as stress and fatigue, along with operational and environmental factors. An illustrative example demonstrates how operator skill development can influence production speed, energy consumption, and ergonomic outcomes, highlighting cross-layer trade-offs and synergies. The paper also addresses practical challenges, including the measurement of social metrics, the prioritization of competing KPIs, and the need for real-time adaptability. Finally, avenues for future work are identified, emphasizing the integration of Industry 4.0 technologies such as the Internet of Things (IoT) and data analytics to support dynamic decision-making and foster sustainable manufacturing practices.
{"title":"Multi-layer multi-variable value stream mapping: A comprehensive framework across operational, environmental, and social layers with integrated KPIs interrelationships","authors":"Ayoub Heydarzade , Niloofar Rezaei , Seyed Alireza Vaezi , Jaime A. Camelio","doi":"10.1016/j.mfglet.2025.06.023","DOIUrl":"10.1016/j.mfglet.2025.06.023","url":null,"abstract":"<div><div>Industry 4.0 technologies have increased the complexity and interconnectivity of manufacturing systems, challenging the conventional scope of Value Stream Mapping (VSM). In response, this paper proposes a Multi-Layer Multi-Variable Value Stream Mapping (MLMV-VSM) framework that integrates operational, environmental, and social layers within a single methodology. The approach captures Key Performance Indicators (KPIs) and their interdependencies, enabling more balanced system optimization. Unlike traditional VSM, MLMV-VSM explicitly incorporates human-centric metrics, such as stress and fatigue, along with operational and environmental factors. An illustrative example demonstrates how operator skill development can influence production speed, energy consumption, and ergonomic outcomes, highlighting cross-layer trade-offs and synergies. The paper also addresses practical challenges, including the measurement of social metrics, the prioritization of competing KPIs, and the need for real-time adaptability. Finally, avenues for future work are identified, emphasizing the integration of Industry 4.0 technologies such as the Internet of Things (IoT) and data analytics to support dynamic decision-making and foster sustainable manufacturing practices.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 184-194"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926536","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 : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.019
Dane Ungurait , Chuanshen Zhou , Kateland Hutt , Yunxia Chen , Adam Poniatowski , Joe Shaara , Paxton Howell , Yong Huang , Hitomi Yamaguchi
Due to challenges with sourcing tissues for autografts, allografts are becoming increasingly popular in the transplantation of human tissue, including bone grafting, and it is important that available donor tissue is processed efficiently while minimizing discarded tissue. This paper describes the development of a computed tomography (CT) image-based system to nondestructively measure cortical-bone thickness of a donor sample, which helps determine how the tissue should be processed to maximize tissue utilization. The system uses a CT scanner to collect three-dimensional data of the donor tissue. The data is then processed into two-dimensional tomograms, which are processed using software developed to measure cortical-bone thickness. Based on these measurements, a score is assigned to the cortical bone that helps determine the types and sizes of allografts that can be processed from the tissue. It was demonstrated that high-resolution (85–200 microns) images can be generated and analyzed quickly with scan times as fast as 8 min and software run times of less than 5 seconds for 464 thickness measurements. This paper concludes that this process is an effective and efficient method to generate quantitative metrics that can be used to make more informed decisions on the processing of bone tissue for allograft production.
{"title":"Computed Tomography Image-Based Measurements of Cortical Bone Thickness for Improved Bone Tissue Processing and Decision-Making","authors":"Dane Ungurait , Chuanshen Zhou , Kateland Hutt , Yunxia Chen , Adam Poniatowski , Joe Shaara , Paxton Howell , Yong Huang , Hitomi Yamaguchi","doi":"10.1016/j.mfglet.2025.06.019","DOIUrl":"10.1016/j.mfglet.2025.06.019","url":null,"abstract":"<div><div>Due to challenges with sourcing tissues for autografts, allografts are becoming increasingly popular in the transplantation of human tissue, including bone grafting, and it is important that available donor tissue is processed efficiently while minimizing discarded tissue. This paper describes the development of a computed tomography (CT) image-based system to nondestructively measure cortical-bone thickness of a donor sample, which helps determine how the tissue should be processed to maximize tissue utilization. The system uses a CT scanner to collect three-dimensional data of the donor tissue. The data is then processed into two-dimensional tomograms, which are processed using software developed to measure cortical-bone thickness. Based on these measurements, a score is assigned to the cortical bone that helps determine the types and sizes of allografts that can be processed from the tissue. It was demonstrated that high-resolution (85–200 microns) images can be generated and analyzed quickly with scan times as fast as 8 min and software run times of less than 5 seconds for 464 thickness measurements. This paper concludes that this process is an effective and efficient method to generate quantitative metrics that can be used to make more informed decisions on the processing of bone tissue for allograft production.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 148-156"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926666","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 : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.040
Wenjian Cao , Andrea Ghiotti , Stefania Bruschi
An innovative approach in electrochemical polishing (ECP) has been developed to enhance surface quality and precision in post-processing additive manufacturing surfaces, with a particular focus on leveling adhered powders and mitigating surface waviness. This study introduces a novel 2D model for quantitatively simulating the material removal process of spherical powder residues and waviness on sintered surfaces, utilizing adaptive triangular meshing technology. The initial geometric profiles of surface defects were modeled using the ellipse equation for spherical powder particles and the sine function for surface waviness. Key profile control nodes were tracked to observe changes over time, with detailed analyses of electric field strength, current density, material removal thickness, and removal rate. Predictive modeling results indicate that the electric field direction remains parallel to the surface, and the current density is approximately 0.23A cm−2 after ECP, achieving a consistent material removal rate of 0.28 μm min−1 during polishing. Surface roughness measurements, taken over a sampling length of 500 µm, showed a reduction from Ra 3.74 μm to Ra 0.21 μm, and the comparison of simulated and experimental surface profiles was presented with an error of only 0.04 μm, demonstrating the method’s efficacy in finishing both adhered powders and waviness. This study provides a new perspective to investigate the mechanism of ECP additive manufacturing parts.
{"title":"Modeling of adhered powder particles and waviness on additive manufacturing part surface in electrochemical polishing","authors":"Wenjian Cao , Andrea Ghiotti , Stefania Bruschi","doi":"10.1016/j.mfglet.2025.06.040","DOIUrl":"10.1016/j.mfglet.2025.06.040","url":null,"abstract":"<div><div>An innovative approach in electrochemical polishing (ECP) has been developed to enhance surface quality and precision in post-processing additive manufacturing surfaces, with a particular focus on leveling adhered powders and mitigating surface waviness. This study introduces a novel 2D model for quantitatively simulating the material removal process of spherical powder residues and waviness on sintered surfaces, utilizing adaptive triangular meshing technology. The initial geometric profiles of surface defects were modeled using the ellipse equation for spherical powder particles and the sine function for surface waviness. Key profile control nodes were tracked to observe changes over time, with detailed analyses of electric field strength, current density, material removal thickness, and removal rate. Predictive modeling results indicate that the electric field direction remains parallel to the surface, and the current density is approximately 0.23A cm<sup>−2</sup> after ECP, achieving a consistent material removal rate of 0.28 μm min<sup>−1</sup> during polishing. Surface roughness measurements, taken over a sampling length of 500 µm, showed a reduction from Ra 3.74 μm to Ra 0.21 μm, and the comparison of simulated and experimental surface profiles was presented with an error of only 0.04 μm, demonstrating the method’s efficacy in finishing both adhered powders and waviness. This study provides a new perspective to investigate the mechanism of ECP additive manufacturing parts.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 332-338"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926827","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 : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.086
Samuel Stencel , Nathan Hartman
Manufacturing contributes a significant amount of value to the economy while consuming nearly one-third of the total energy produced within the nation. Computer-aided manufacturing tools arose to streamline the process of manufacturing parts, but they lack energy-conscious practices. With this gap, research has been done in the development of mechanistic and data-driven models to accurately predict the energy consumption of this process. However, validation carried out in the experimental methodology in many of the research models is ill-fit to represent their realistic counterparts. In this paper, a data-driven deep learning model is developed, which properly accounts for the complexities associated with CNC machining, as it compensates for variations in operations observed during CNC machining. Furthermore, this deep learning model makes predictions by processing supplemented NC programs sequentially. These programs include additional information regarding the material removal process. Four variants of the model are created to provide insights into the effects of supplementing the program with different material removal variables. The variables include depth of cut, width of cut, material removal rate, and the volume of material removed per numerically controlled instruction. The prediction capability of these four models are then compared using several statistical tests.
{"title":"Using machine learning with supplemented NC code to predict machining energy","authors":"Samuel Stencel , Nathan Hartman","doi":"10.1016/j.mfglet.2025.06.086","DOIUrl":"10.1016/j.mfglet.2025.06.086","url":null,"abstract":"<div><div>Manufacturing contributes a significant amount of value to the economy while consuming nearly one-third of the total energy produced within the nation. Computer-aided manufacturing tools arose to streamline the process of manufacturing parts, but they lack energy-conscious practices. With this gap, research has been done in the development of mechanistic and data-driven models to accurately predict the energy consumption of this process. However, validation carried out in the experimental methodology in many of the research models is ill-fit to represent their realistic counterparts. In this paper, a data-driven deep learning model is developed, which properly accounts for the complexities associated with CNC machining, as it compensates for variations in operations observed during CNC machining. Furthermore, this deep learning model makes predictions by processing supplemented NC programs sequentially. These programs include additional information regarding the material removal process. Four variants of the model are created to provide insights into the effects of supplementing the program with different material removal variables. The variables include depth of cut, width of cut, material removal rate, and the volume of material removed per numerically controlled instruction. The prediction capability of these four models are then compared using several statistical tests.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 734-745"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926833","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 : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.011
Wei Li , Barrie R. Nault
For one-stage production, operations management faces the following three challenges to make decisions, which are inconsistencies between key performance indicators (KPIs) for production, trade-offs between the expected return and the risk in modern portfolio theory (MPT), and uncertainties in processing times. Traditionally, total completion time (TCT) and variance of completion times (VCT) are two KPIs for one-stage production scheduling, which relate to the first and second moments of completion times, respectively. We question whether the third moment of completion times is good to address the three challenges. In this paper, we introduce the skewness of completion times (SCT) in scheduling, and propose the ToB() heuristics for trade-off balancing. Through case studies with 5 levels of processing time uncertainties and compared to existing ToB() heuristics which balance trade-offs between TCT and VCT, we show that our ToB() heuristics dominate ToB() heuristics in terms of smaller expected values (E) of weighted sum of deviations from the best solutions of KPIs and smaller risks () associated with these KPI deviations. Therefore, our ToB() heuristics are more robust to balance trade-offs between the three KPIs under processing time uncertainties.
{"title":"Balancing trade-offs between first three moments of completion times for one-stage production","authors":"Wei Li , Barrie R. Nault","doi":"10.1016/j.mfglet.2025.06.011","DOIUrl":"10.1016/j.mfglet.2025.06.011","url":null,"abstract":"<div><div>For one-stage production, operations management faces the following three challenges to make decisions, which are inconsistencies between key performance indicators (KPIs) for production, trade-offs between the expected return and the risk in modern portfolio theory (MPT), and uncertainties in processing times. Traditionally, total completion time (<em>TCT</em>) and variance of completion times (<em>VCT</em>) are two KPIs for one-stage production scheduling, which relate to the first and second moments of completion times, respectively. We question whether the third moment of completion times is good to address the three challenges. In this paper, we introduce the skewness of completion times (<em>SCT</em>) in scheduling, and propose the ToB(<span><math><mrow><mi>a</mi><mo>,</mo><mi>b</mi></mrow></math></span>) heuristics for trade-off balancing. Through case studies with 5 levels of processing time uncertainties and compared to existing ToB(<span><math><mrow><mi>α</mi></mrow></math></span>) heuristics which balance trade-offs between <em>TCT</em> and <em>VCT</em>, we show that our ToB(<span><math><mrow><mi>a</mi><mo>,</mo><mi>b</mi></mrow></math></span>) heuristics dominate ToB(<span><math><mrow><mi>α</mi></mrow></math></span>) heuristics in terms of smaller expected values (<em>E</em>) of weighted sum of deviations from the best solutions of KPIs and smaller risks (<span><math><mrow><mi>σ</mi></mrow></math></span>) associated with these KPI deviations. Therefore, our ToB(<span><math><mrow><mi>a</mi><mo>,</mo><mi>b</mi></mrow></math></span>) heuristics are more robust to balance trade-offs between the three KPIs under processing time uncertainties.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 70-79"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926662","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 : 2025-08-01DOI: 10.1016/j.mfglet.2025.06.024
Fatemeh Mozaffar, Logan Smith, Beshoy Morkos
This study investigates the application of musical and voice-based auditory nudges in enhancing human-AI interactions within a manufacturing setting, utilizing nudge theory to improve worker productivity, trust, and engagement. As AI technologies become more widespread in manufacturing environments, effective methods for fostering trust and collaboration between human operators and AI are essential. The increasing demand for customized products and rapid technological advancements in Industry 4.0 (I4.0) necessitate rapid employee adaptation, with humans playing a key role as the Human Component (HC) for its success. Therefore, the relationship between artificial intelligence (AI) and humans as inseparable parts of I4.0 need to be studied. Research has been done on improving the interaction between and performance of AI and humans. The impact of different nudge methods on worker productivity has also been studied, but not as an effective communication tool in human and AI teams. This study proposes a research framework that aims to explore using music as a medium for non-verbal cues, which has been shown to influence emotional perception and enhance task performance such as task continuity and worker productivity. This study employs a mixed-methods approach, incorporating quantitative metrics such as task completion times, alongside qualitative feedback to assess the impact of varied auditory nudges—including musical elements like tempo and pitch—on worker behavior and emotional response. Results from this experimental study will help to demonstrate the viability of musical nudges in increasing trust and efficiency in human-AI collaboration, providing insights into innovative strategies for optimizing Industry 4.0 environments.
{"title":"Tunes of trust: A framework for auditory nudges in human-ai manufacturing collaboration","authors":"Fatemeh Mozaffar, Logan Smith, Beshoy Morkos","doi":"10.1016/j.mfglet.2025.06.024","DOIUrl":"10.1016/j.mfglet.2025.06.024","url":null,"abstract":"<div><div>This study investigates the application of musical and voice-based auditory nudges in enhancing human-AI interactions within a manufacturing setting, utilizing nudge theory to improve worker productivity, trust, and engagement. As AI technologies become more widespread in manufacturing environments, effective methods for fostering trust and collaboration between human operators and AI are essential. The increasing demand for customized products and rapid technological advancements in Industry 4.0 (I4.0) necessitate rapid employee adaptation, with humans playing a key role as the Human Component (HC) for its success. Therefore, the relationship between artificial intelligence (AI) and humans as inseparable parts of I4.0 need to be studied. Research has been done on improving the interaction between and performance of AI and humans. The impact of different nudge methods on worker productivity has also been studied, but not as an effective communication tool in human and AI teams. This study proposes a research framework that aims to explore using music as a medium for non-verbal cues, which has been shown to influence emotional perception and enhance task performance such as task continuity and worker productivity. This study employs a mixed-methods approach, incorporating quantitative metrics such as task completion times, alongside qualitative feedback to assess the impact of varied auditory nudges—including musical elements like tempo and pitch—on worker behavior and emotional response. Results from this experimental study will help to demonstrate the viability of musical nudges in increasing trust and efficiency in human-AI collaboration, providing insights into innovative strategies for optimizing Industry 4.0 environments.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 195-204"},"PeriodicalIF":2.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926722","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