Pub Date : 2025-12-01Epub Date: 2025-09-03DOI: 10.1016/j.mfglet.2025.08.005
Grzegorz Miebs , Rafał A. Bachorz
Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from 3D models of products with exposed geometries. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps with a mean absolute error below 3 s making planning across varied product types easier.
{"title":"Technology prediction of a 3D model using neural network","authors":"Grzegorz Miebs , Rafał A. Bachorz","doi":"10.1016/j.mfglet.2025.08.005","DOIUrl":"10.1016/j.mfglet.2025.08.005","url":null,"abstract":"<div><div>Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from 3D models of products with exposed geometries. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps with a mean absolute error below 3 s making planning across varied product types easier.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 5-9"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019286","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 : 2025-12-01Epub Date: 2025-11-22DOI: 10.1016/j.mfglet.2025.11.007
Heidar Karimialavijeh , Waris Nawaz Khan , Mohsen Moradi , M. Proëbstle , Etienne Martin
Temperature-induced porosity (TIP) originates from gas entrapment during laser powder bed fusion (LPBF) processing, which evolves into defects during subsequent thermal exposure. In LPBF-A20X alloy, the rapid solidification of the melt pool can trap moisture present in the powder feedstock. During post-processing heat treatment, this moisture promotes oxidation reaction forming Al2O3 and releasing H2, contributing to TIP. Severity of TIP is closely linked to LPBF processing parameters, particularly scan speed. At high scan speeds, increased solidification rate limits the escape of moisture, resulting in notable reduction in relative density (1.0–1.9%) post heat treatment. In contrast, lower scan speeds extend melt pool lifetime, facilitating H2 escape and limiting density reduction (0.2–0.8%). These findings highlight the importance of managing powder moisture and optimizing laser parameters, post-processing heat treatments to mitigate TIP.
{"title":"Temperature induced porosity in laser powder bed fusion fabricated A20X alloy","authors":"Heidar Karimialavijeh , Waris Nawaz Khan , Mohsen Moradi , M. Proëbstle , Etienne Martin","doi":"10.1016/j.mfglet.2025.11.007","DOIUrl":"10.1016/j.mfglet.2025.11.007","url":null,"abstract":"<div><div>Temperature-induced porosity (TIP) originates from gas entrapment during laser powder bed fusion (LPBF) processing, which evolves into defects during subsequent thermal exposure. In LPBF-A20X alloy, the rapid solidification of the melt pool can trap moisture present in the powder feedstock. During post-processing heat treatment, this moisture promotes oxidation reaction forming Al<sub>2</sub>O<sub>3</sub> and releasing H<sub>2</sub>, contributing to TIP. Severity of TIP is closely linked to LPBF processing parameters, particularly scan speed. At high scan speeds, increased solidification rate limits the escape of moisture, resulting in notable reduction in relative density (1.0–1.9%) post heat treatment. In contrast, lower scan speeds extend melt pool lifetime, facilitating H<sub>2</sub> escape and limiting density reduction (0.2–0.8%). These findings highlight the importance of managing powder moisture and optimizing laser parameters, post-processing heat treatments to mitigate TIP.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 148-151"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614328","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 : 2025-12-01Epub Date: 2025-10-26DOI: 10.1016/j.mfglet.2025.10.011
Sandra Huffman, Kaitlyn Becker, John Liu, Rebecca Zubajlo, Warren Seering
With AI making it increasingly easy to cheat on tests, assessment methods must be adapted. Here, we document the creation, administration, and grading of short (7-minute) authentic oral assessments, and reflect on their implementation. In these oral assessments, students were asked to demonstrate authentic engineering practices such as weighing tradeoffs, data interpretation, troubleshooting, and design-for-manufacture. The teaching team was able to establish student buy-in through class discussions, clear rubrics, and ample opportunities for practice. Instructors developed the skills required to keep the oral assessments short, built fluency in the rubric, and took steps to reduce bias. Each of these actions helped create strong, fair oral assessments. Administering the oral assessments allowed instructors to better understand their students’ strengths and needs, and build better relationships in the classroom. Despite their brevity, instructors believed the oral assessments provided a clear picture of student understanding and helped bring nuance to different capabilities. Both students and instructors had a positive experience with the oral assessments; instructors will continue using them in future semesters. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
{"title":"Reflections on the implementation of Short, authentic oral assessments in a university manufacturing course","authors":"Sandra Huffman, Kaitlyn Becker, John Liu, Rebecca Zubajlo, Warren Seering","doi":"10.1016/j.mfglet.2025.10.011","DOIUrl":"10.1016/j.mfglet.2025.10.011","url":null,"abstract":"<div><div>With AI making it increasingly easy to cheat on tests, assessment methods must be adapted. Here, we document the creation, administration, and grading of short (7-minute) authentic oral assessments, and reflect on their implementation. In these oral assessments, students were asked to demonstrate authentic engineering practices such as weighing tradeoffs, data interpretation, troubleshooting, and design-for-manufacture. The teaching team was able to establish student buy-in through class discussions, clear rubrics, and ample opportunities for practice. Instructors developed the skills required to keep the oral assessments short, built fluency in the rubric, and took steps to reduce bias. Each of these actions helped create strong, fair oral assessments. Administering the oral assessments allowed instructors to better understand their students’ strengths and needs, and build better relationships in the classroom. Despite their brevity, instructors believed the oral assessments provided a clear picture of student understanding and helped bring nuance to different capabilities. Both students and instructors had a positive experience with the oral assessments; instructors will continue using them in future semesters. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 97-101"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416714","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 : 2025-12-01Epub Date: 2025-10-30DOI: 10.1016/j.mfglet.2025.10.018
Israa Azzam , Khalid Bello , Farid El Breidi , Faisal Aqlan
Extended Reality (XR) technology has shown promise in enhancing manufacturing training by providing realistic simulations in safe and controlled environments. Although many XR tools focus on single-user experiences to build individual skills, collaborative training plays a role in promoting teamwork and reinforcing production outcomes. Multi-user XR systems facilitate training on collaborative tasks and support the development of teamwork and communication skills. This study explores the use of a multi-user Mixed Reality (MR) training module in manufacturing education. The proposed MR module supports a multi-user experience, allowing trainees to work collaboratively in a shared virtual environment. The goal of this research is to assess how collaboration in MR-based training affects learning, particularly regarding how quickly tasks are completed and how effectively problems are solved. The study included 103 participants who experienced the collaborative MR module to design and assemble a hydraulic bike. The shared MR setup connected multiple HoloLens 2 headsets, allowing users to interact in the same virtual workspace and complete assigned tasks. To evaluate teamwork and problem-solving abilities, a survey focusing on team dynamics and collaboration was utilized. Participants’ experiences were also assessed using the System Usability Scale (SUS) and the Simulation Task Load Index (SIM-TLX) to understand the system usability and the mental and physical effort required during the training activity.
{"title":"Collaborative problem-solving in mixed reality manufacturing environments","authors":"Israa Azzam , Khalid Bello , Farid El Breidi , Faisal Aqlan","doi":"10.1016/j.mfglet.2025.10.018","DOIUrl":"10.1016/j.mfglet.2025.10.018","url":null,"abstract":"<div><div>Extended Reality (XR) technology has shown promise in enhancing manufacturing training by providing realistic simulations in safe and controlled environments. Although many XR tools focus on single-user experiences to build individual skills, collaborative training plays a role in promoting teamwork and reinforcing production outcomes. Multi-user XR systems facilitate training on collaborative tasks and support the development of teamwork and communication skills. This study explores the use of a multi-user Mixed Reality (MR) training module in manufacturing education. The proposed MR module supports a multi-user experience, allowing trainees to work collaboratively in a shared virtual environment. The goal of this research is to assess how collaboration in MR-based training affects learning, particularly regarding how quickly tasks are completed and how effectively problems are solved. The study included 103 participants who experienced the collaborative MR module to design and assemble a hydraulic bike. The shared MR setup connected multiple HoloLens 2 headsets, allowing users to interact in the same virtual workspace and complete assigned tasks. To evaluate teamwork and problem-solving abilities, a survey focusing on team dynamics and collaboration was utilized. Participants’ experiences were also assessed using the System Usability Scale (SUS) and the Simulation Task Load Index (SIM-TLX) to understand the system usability and the mental and physical effort required during the training activity.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 118-122"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Additive Manufacturing (AM) has gained wide attention in the past two decades and emerged as a significant method in the manufacturing sector. Advancements in AM have enhanced productivity, reduced lead times, and improved part quality while maintaining cost-effectiveness. Despite advancements in materials, technologies, and parameter optimization, the widespread AM adoption is limited by a lack of skilled workforce. This research presents a hybrid learning approach to address this gap through curricula and hands-on training. The proposed framework includes hybrid educational and training approaches in polymer-based FFF, SLA, SLS, and Metal FFF technologies toward the development of a workforce skilled in AM.
{"title":"Hybrid education and training approaches enabling workforce development in additive manufacturing","authors":"Abhishek Singh, Pinyi Wu, Bhavana Komaraju, Chinedum E. Okwudire, Mihaela Banu","doi":"10.1016/j.mfglet.2025.10.007","DOIUrl":"10.1016/j.mfglet.2025.10.007","url":null,"abstract":"<div><div>Additive Manufacturing (AM) has gained wide attention in the past two decades and emerged as a significant method in the manufacturing sector. Advancements in AM have enhanced productivity, reduced lead times, and improved part quality while maintaining cost-effectiveness. Despite advancements in materials, technologies, and parameter optimization, the widespread AM adoption is limited by a lack of skilled workforce. This research presents a hybrid learning approach to address this gap through curricula and hands-on training. The proposed framework includes hybrid educational and training approaches in polymer-based FFF, SLA, SLS, and Metal FFF technologies toward the development of a workforce skilled in AM.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 65-71"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362555","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 : 2025-12-01Epub Date: 2025-10-17DOI: 10.1016/j.mfglet.2025.10.012
Mateusz Kopec , Tomasz Durejko
In this paper, fatigue performance of Ti-5553 alloy fabricated using Laser Engineered Net Shaping (LENS) was investigated. Mechanical testing revealed high tensile strength (UTS: 1377 MPa) and good ductility (16 %). Fatigue tests under fully reversed loading expose superior endurance, with crack initiation mechanisms transitioning from surface-induced at high stresses to internal defect-assisted at lower amplitudes. Fractography exposed unmelted particles as initiation sites under moderate cyclic loads. The results establish LENS as a reliable method for manufacturing high-strength Ti-5553 components for high-performance applications.
{"title":"Superior fatigue response of LENS-manufactured Ti-5553 alloy","authors":"Mateusz Kopec , Tomasz Durejko","doi":"10.1016/j.mfglet.2025.10.012","DOIUrl":"10.1016/j.mfglet.2025.10.012","url":null,"abstract":"<div><div>In this paper, fatigue performance of Ti-5553 alloy fabricated using Laser Engineered Net Shaping (LENS) was investigated. Mechanical testing revealed high tensile strength (UTS: 1377 MPa) and good ductility (16 %). Fatigue tests under fully reversed loading expose superior endurance, with crack initiation mechanisms transitioning from surface-induced at high stresses to internal defect-assisted at lower amplitudes. Fractography exposed unmelted particles as initiation sites under moderate cyclic loads. The results establish LENS as a reliable method for manufacturing high-strength Ti-5553 components for high-performance applications.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 50-55"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362553","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 : 2025-12-01Epub Date: 2025-10-13DOI: 10.1016/j.mfglet.2025.10.005
Ziling Chen, Zhen Zhao, John Liu
Remote manufacturing education often struggles to deliver authentic hands-on experiences. We developed TeleopLab, an intuitive teleoperation system that enables students to control a robotic arm and laboratory equipment remotely through a smartphone motion interface and commonly available meeting software. Designed for accessibility and minimal setup, TeleopLab preserves the interactivity and complexity of physical laboratories in an online environment. When deployed in an advanced manufacturing course, it supported iterative 3D printing optimization tasks in real time. The educational impact of TeleopLab was evaluated using the Motivated Strategies for Learning Questionnaire (MSLQ), with pre- and post-use data collected from six students. The results showed improvements in self-efficacy and motivation to re-engage, along with a reduction in fear of making mistakes among students during the lab activities. TeleopLab highlights the potential of a scalable, cost-effective solution for remote hands-on learning.
{"title":"Application of phone-based robotic arm teleoperation in remote hands-on labs for engineering education","authors":"Ziling Chen, Zhen Zhao, John Liu","doi":"10.1016/j.mfglet.2025.10.005","DOIUrl":"10.1016/j.mfglet.2025.10.005","url":null,"abstract":"<div><div>Remote manufacturing education often struggles to deliver authentic hands-on experiences. We developed <em>TeleopLab</em>, an intuitive teleoperation system that enables students to control a robotic arm and laboratory equipment remotely through a smartphone motion interface and commonly available meeting software. Designed for accessibility and minimal setup, TeleopLab preserves the interactivity and complexity of physical laboratories in an online environment. When deployed in an advanced manufacturing course, it supported iterative 3D printing optimization tasks in real time. The educational impact of TeleopLab was evaluated using the Motivated Strategies for Learning Questionnaire (MSLQ), with pre- and post-use data collected from six students. The results showed improvements in self-efficacy and motivation to re-engage, along with a reduction in fear of making mistakes among students during the lab activities. TeleopLab highlights the potential of a scalable, cost-effective solution for remote hands-on learning.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 111-117"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416613","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 : 2025-12-01Epub Date: 2025-10-24DOI: 10.1016/j.mfglet.2025.10.003
Israa Azzam , Khalid Bello , Farid El Breidi , Faisal Aqlan
With the advancement of Industry 4.0, extended reality (XR) technologies, such as Virtual Reality (VR) and Mixed Reality (MR), have become integral tools in manufacturing training and education. These technologies proved to lower training costs by eliminating the need for physical equipment and reducing safety concerns. However, both VR and MR still face challenges related to user experience (UX), interaction quality, environmental functionality, and hardware/software limitations, which affects their overall effectiveness in training. This research examines the unique features, strengths, and limitations of VR and MR environments based on users’ experiences during manufacturing assembly tasks, aiming to select the optimal XR environment for a better user experience. A study was conducted with 95 undergraduate engineering students using interactive training modules in both VR and MR settings. The modules included sensory inputs, such as sound and visuals, to help assess UX. Quantitative and qualitative data were collected and analyzed. Statistical methods were used for the numerical data and Natural Language Processing (NLP), specifically the Latent Dirichlet Allocation (LDA) model, was used to analyze the qualitative data. The findings showed that MR was more effective than VR for manufacturing training. Participants reported that MR provided a more immersive and interactive experience, reduced discomfort, and improved safety by allowing real-time interaction with the physical environment through holograms, while still maintaining awareness of their surroundings.
{"title":"Analysis of user experience in extended reality: a comparative study of VR and MR for manufacturing training","authors":"Israa Azzam , Khalid Bello , Farid El Breidi , Faisal Aqlan","doi":"10.1016/j.mfglet.2025.10.003","DOIUrl":"10.1016/j.mfglet.2025.10.003","url":null,"abstract":"<div><div>With the advancement of Industry 4.0, extended reality (XR) technologies, such as Virtual Reality (VR) and Mixed Reality (MR), have become integral tools in manufacturing training and education. These technologies proved to lower training costs by eliminating the need for physical equipment and reducing safety concerns. However, both VR and MR still face challenges related to user experience (UX), interaction quality, environmental functionality, and hardware/software limitations, which affects their overall effectiveness in training. This research examines the unique features, strengths, and limitations of VR and MR environments based on users’ experiences during manufacturing assembly tasks, aiming to select the optimal XR environment for a better user experience. A study was conducted with 95 undergraduate engineering students using interactive training modules in both VR and MR settings. The modules included sensory inputs, such as sound and visuals, to help assess UX. Quantitative and qualitative data were collected and analyzed. Statistical methods were used for the numerical data and Natural Language Processing (NLP), specifically the Latent Dirichlet Allocation (LDA) model, was used to analyze the qualitative data. The findings showed that MR was more effective than VR for manufacturing training. Participants reported that MR provided a more immersive and interactive experience, reduced discomfort, and improved safety by allowing real-time interaction with the physical environment through holograms, while still maintaining awareness of their surroundings.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 77-82"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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