Pub Date : 2026-01-23DOI: 10.1016/j.mfglet.2026.01.002
Emma Salatiello, Silvestro Vespoli, Andrea Grassi, Guido Guizzi
In the modern supply chain landscape, achieving effective coordination is challenging due to information asymmetry and misreporting behaviour. This paper proposes a hybrid architecture combining centralised data management through an Intelligent Mediator with decentralised decision-making. The architecture allows actors to maintain autonomy while promoting truthful information sharing via credibility scoring. By dynamically adjusting for data reliability and aligning individual objectives with overall supply chain goals, the system reduces misinformation impact and builds trust among actors. This framework provides a proactive solution through adaptive feedback loops, fostering stability in complex supply chains.
{"title":"An innovative hybrid architecture to overcome misreporting in supply chain coordination under information asymmetry","authors":"Emma Salatiello, Silvestro Vespoli, Andrea Grassi, Guido Guizzi","doi":"10.1016/j.mfglet.2026.01.002","DOIUrl":"10.1016/j.mfglet.2026.01.002","url":null,"abstract":"<div><div>In the modern supply chain landscape, achieving effective coordination is challenging due to information asymmetry and misreporting behaviour. This paper proposes a hybrid architecture combining centralised data management through an Intelligent Mediator with decentralised decision-making. The architecture allows actors to maintain autonomy while promoting truthful information sharing via credibility scoring. By dynamically adjusting for data reliability and aligning individual objectives with overall supply chain goals, the system reduces misinformation impact and builds trust among actors. This framework provides a proactive solution through adaptive feedback loops, fostering stability in complex supply chains.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 58-61"},"PeriodicalIF":2.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077929","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 : 2026-01-10DOI: 10.1016/j.mfglet.2026.01.001
Arivazhagan Anbalagan , John E Graves , Oliver James Curnick , Danijela Rostohar , Margret Anouncia Savarimuthu , Anthony Xavior Michael , Rajkumar Soundarapandiyan , Jeyapandiarajan Paulchamy , Mythili Thirugnanam
This study focuses on the machine learning (ML)-based identification of defects caused by hydrogen embrittlement (H2E) in the welded zones of 316 L/304 L stainless steels. It involves developing a robust SEM image dataset to train ML models for accurate defect identification. Initially, Gas Metal Arc Welding (GMAW) samples were manufactured with weld gap variations of 0.8 mm, 1.2 mm, and 1.5 mm. The welding parameters used were: (i) welding speeds of 15 mm/sec and 10 mm/sec, (ii) wire feed rate of 5.5 m/min, and (iii) voltage of 15.5 V. The samples were then exposed to a hydrogen gas environment at a pressure of 80 bar for 150 h. When analyzed using scanning electron microscopy (SEM) & electron backscatter diffraction (EBSD), H2E was observed on the surfaces of the welded zones (WZ) and heat-affected zones (HAZ). These defects, validated through literature, were segregated / sectioned as defect-based feature images and stored as a dataset. A preliminary analysis of the images validated after with 16 DOE’s using AlexNet, a convolutional neural network (CNN)-based ML model, showed significant identification of these defects with 90 % accuracy. The trained models helped identify areas and understand previously unidentified defects. Through a focused discussion on defect detection, supported by validation using classification (CNN Accuracy, Precision, Recall, and F1-Score) and regression metrics (R2 and Success Rate), the article demonstrates the potential of ML-based approaches in advancing welding diagnostics.
{"title":"SEM images data set development of ML models to predict hydrogen embrittlement in welded 316 L/304 L stainless steels","authors":"Arivazhagan Anbalagan , John E Graves , Oliver James Curnick , Danijela Rostohar , Margret Anouncia Savarimuthu , Anthony Xavior Michael , Rajkumar Soundarapandiyan , Jeyapandiarajan Paulchamy , Mythili Thirugnanam","doi":"10.1016/j.mfglet.2026.01.001","DOIUrl":"10.1016/j.mfglet.2026.01.001","url":null,"abstract":"<div><div>This study focuses on the machine learning (ML)-based identification of defects caused by hydrogen embrittlement (H2E) in the welded zones of 316 L/304 L stainless steels. It involves developing a robust SEM image dataset to train ML models for accurate defect identification. Initially, Gas Metal Arc Welding (GMAW) samples were manufactured with weld gap variations of 0.8 mm, 1.2 mm, and 1.5 mm. The welding parameters used were: (i) welding speeds of 15 mm/sec and 10 mm/sec, (ii) wire feed rate of 5.5 m/min, and (iii) voltage of 15.5 V. The samples were then exposed to a hydrogen gas environment at a pressure of 80 bar for 150 h. When analyzed using scanning electron microscopy (SEM) & electron backscatter diffraction (EBSD), H2E was observed on the surfaces of the welded zones (WZ) and heat-affected zones (HAZ). These defects, validated through literature, were segregated / sectioned as defect-based feature images and stored as a dataset. A preliminary analysis of the images validated after with 16 DOE’s using AlexNet, a convolutional neural network (CNN)-based ML model, showed significant identification of these defects with 90 % accuracy. The trained models helped identify areas and understand previously unidentified defects. Through a focused discussion on defect detection, supported by validation using classification (CNN Accuracy, Precision, Recall, and F1-Score) and regression metrics (R2 and Success Rate), the article demonstrates the potential of ML-based approaches in advancing welding diagnostics.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 51-57"},"PeriodicalIF":2.0,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037911","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 : 2026-01-06DOI: 10.1016/j.mfglet.2025.12.005
Mario Cvetkoski , Hossein Najaf Zadeh
This study explores a new-to-market polyhydroxyalkanoate (PHA) filament as a drop-in replacement for polylactic Acid (PLA) to reduce the environmental impact of FFF 3D printing. It provides insights into printability, mechanical performance, and biodegradability. PHA demonstrated comparable aesthetic print outcomes to PLA but exhibited bed adhesion issues and lower mechanical strength, except for toughness. Significantly, PHA samples recorded noticeable biodegradation within months under indoor soil and home compost conditions. These findings offer practical insight into the real-world performance of pure PHA filament, supporting its potential for low-load, eco-conscious prototyping applications where post-use home biodegradability is a desirable and achievable outcome.
{"title":"Exploring commercial PHA filament for more eco-friendly 3D printing","authors":"Mario Cvetkoski , Hossein Najaf Zadeh","doi":"10.1016/j.mfglet.2025.12.005","DOIUrl":"10.1016/j.mfglet.2025.12.005","url":null,"abstract":"<div><div>This study explores a new-to-market polyhydroxyalkanoate (PHA) filament as a drop-in replacement for polylactic Acid (PLA) to reduce the environmental impact of FFF 3D printing. It provides insights into printability, mechanical performance, and biodegradability. PHA demonstrated comparable aesthetic print outcomes to PLA but exhibited bed adhesion issues and lower mechanical strength, except for toughness. Significantly, PHA samples recorded noticeable biodegradation within months under indoor soil and home compost conditions. These findings offer practical insight into the real-world performance of pure PHA filament, supporting its potential for low-load, eco-conscious prototyping applications where post-use home biodegradability is a desirable and achievable outcome.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 32-37"},"PeriodicalIF":2.0,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977255","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}
Dissimilar metal joining presents a practical approach in materials engineering, especially for fabricating lightweight structures, heat exchangers, and components used in refrigeration and the oil and gas industries. The application of microwave energy for joining dissimilar metallic pipes remains unexplored, highlighting a significant gap in the existing literature. The microwave joining of Cu-SS304 is yet to be explored due to challenges caused by the higher thermal conductivity of copper material. This study examines the microwave-assisted joining of dissimilar metallic pipes (Copper -SS304) conducted at an exposure duration of 840 s. Microstructural characterization reveals the presence of finer grains on the SS304 side, whereas the copper side exhibits comparatively coarser grains. Grain Orientation Spread (GOS) analysis of the Cu–SS304 joint indicates a predominance of recrystallized grains, which correlates with improved mechanical performance as reflected in microhardness values.
{"title":"Metallurgical investigation on the joining of dissimilar metallic pipes (Cu-SS304) using microwave energy","authors":"Ankush Thakur , Raman Bedi , Virinder Kumar , Ashwani Kumar Singh","doi":"10.1016/j.mfglet.2025.12.004","DOIUrl":"10.1016/j.mfglet.2025.12.004","url":null,"abstract":"<div><div>Dissimilar metal joining presents a practical approach in materials engineering, especially for fabricating lightweight structures, heat exchangers, and components used in refrigeration and the oil and gas industries. The application of microwave energy for joining dissimilar metallic pipes remains unexplored, highlighting a significant gap in the existing literature. The microwave joining of Cu-SS304 is yet to be explored due to challenges caused by the higher thermal conductivity of copper material. This study examines the microwave-assisted joining of dissimilar metallic pipes (Copper -SS304) conducted at an exposure duration of 840 s. Microstructural characterization reveals the presence of finer grains on the SS304 side, whereas the copper side exhibits comparatively coarser grains. Grain Orientation Spread (GOS) analysis of the Cu–SS304 joint indicates a predominance of recrystallized grains, which correlates with improved mechanical performance as reflected in microhardness values.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 38-45"},"PeriodicalIF":2.0,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977814","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 : 2026-01-04DOI: 10.1016/j.mfglet.2025.12.003
Anna Wąsik , Beata Leszczyńska-Madej , Marcin Madej
This study compares the influence of processing route and SiC content on the microstructure, mechanical properties and tribological behavior of Al–4Cu–xSiC (x = 0, 5, 10 wt%) composites. Materials were fabricated by powder metallurgy using two routes: conventional pressing and sintering (PM) and hot extrusion before sintering (HE). Hot extrusion improved particle distribution, grain refinement and oxide film disruption. Relative density decreased from 98.74 % (unreinforced) to 96.29 % (10 wt% SiC) in PM composites, while HE samples showed ∼91.9 % but more uniform density. Hardness rose from 55 to 60 HB with SiC in PM, while extruded materials remained near 56 HB. Tribological tests (dry sliding, 50 N, 500 m) revealed that extrusion enhanced wear resistance and friction stability. The best result was for 5 wt% SiC HE composite, with lowest friction (≈0.27) and minimal loss of mass (0.00123 g). SEM confirmed adhesive and abrasive wear mechanisms.
{"title":"Enhanced wear resistance in Al–Cu–SiC composites through extrusion-assisted powder metallurgy","authors":"Anna Wąsik , Beata Leszczyńska-Madej , Marcin Madej","doi":"10.1016/j.mfglet.2025.12.003","DOIUrl":"10.1016/j.mfglet.2025.12.003","url":null,"abstract":"<div><div>This study compares the influence of processing route and SiC content on the microstructure, mechanical properties and tribological behavior of Al–4Cu–xSiC (x = 0, 5, 10 wt%) composites. Materials were fabricated by powder metallurgy using two routes: conventional pressing and sintering (PM) and hot extrusion before sintering (HE). Hot extrusion improved particle distribution, grain refinement and oxide film disruption. Relative density decreased from 98.74 % (unreinforced) to 96.29 % (10 wt% SiC) in PM composites, while HE samples showed ∼91.9 % but more uniform density. Hardness rose from 55 to 60 HB with SiC in PM, while extruded materials remained near 56 HB. Tribological tests (dry sliding, 50 N, 500 m) revealed that extrusion enhanced wear resistance and friction stability. The best result was for 5 wt% SiC HE composite, with lowest friction (≈0.27) and minimal loss of mass (0.00123 g). SEM confirmed adhesive and abrasive wear mechanisms.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 25-31"},"PeriodicalIF":2.0,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926435","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 : 2026-01-02DOI: 10.1016/j.mfglet.2025.12.002
Zhetao Liang , Peng Li , Jiayan Lv , Shuhui Wang , Yinbao Tian
To overcome the low surface hardness of Ti-6Al-4V alloy, Ti-Fe deposition layers were fabricated on a Ti-6Al-4V substrate using dual-wire arc additive manufacturing (D-WAAM) with Ti and Fe wires. The microstructure, phase composition, and mechanical properties of the deposited layer were systematically investigated. Elemental analysis revealed a gradual diffusion of Fe and Ti along the build height, forming a compositional transition zone near the substrate. XRD and EDS results confirmed that the deposited layer consisted mainly of α-Ti and Ti2Fe phases. The microstructure evolved from plate-like to needle-like and eventually to fibrous morphologies with increasing deposition height, indicating progressive refinement. The microhardness increased from 320 HV0.2 at the substrate to a maximum of 677 HV0.2 at the top, primarily due to Fe solid-solution strengthening, Ti2Fe precipitation, and grain refinement. The tensile strength reached 713 MPa, slightly higher than Ti-6Al-4V, while fracture analysis showed brittle features caused by Ti2Fe formation. These findings demonstrate that in-situ alloying via D-WAAM effectively enhances surface hardness, offering a promising approach to strengthening Ti alloys.
{"title":"Microstructure and properties of Ti-Fe deposition layers fabricated in situ by wire arc additive manufacturing","authors":"Zhetao Liang , Peng Li , Jiayan Lv , Shuhui Wang , Yinbao Tian","doi":"10.1016/j.mfglet.2025.12.002","DOIUrl":"10.1016/j.mfglet.2025.12.002","url":null,"abstract":"<div><div>To overcome the low surface hardness of Ti-6Al-4V alloy, Ti-Fe deposition layers were fabricated on a Ti-6Al-4V substrate using dual-wire arc additive manufacturing (D-WAAM) with Ti and Fe wires. The microstructure, phase composition, and mechanical properties of the deposited layer were systematically investigated. Elemental analysis revealed a gradual diffusion of Fe and Ti along the build height, forming a compositional transition zone near the substrate. XRD and EDS results confirmed that the deposited layer consisted mainly of α-Ti and Ti<sub>2</sub>Fe phases. The microstructure evolved from plate-like to needle-like and eventually to fibrous morphologies with increasing deposition height, indicating progressive refinement. The microhardness increased from 320 HV<sub>0.2</sub> at the substrate to a maximum of 677 HV<sub>0.2</sub> at the top, primarily due to Fe solid-solution strengthening, Ti<sub>2</sub>Fe precipitation, and grain refinement. The tensile strength reached 713 MPa, slightly higher than Ti-6Al-4V, while fracture analysis showed brittle features caused by Ti<sub>2</sub>Fe formation. These findings demonstrate that in-situ alloying via D-WAAM effectively enhances surface hardness, offering a promising approach to strengthening Ti alloys.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 46-50"},"PeriodicalIF":2.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037912","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 : 2025-12-06DOI: 10.1016/j.mfglet.2025.12.001
Wei Guo, Kalie Miera, James Nguyen, Alexandra Botts, Paulomi Nandy, Thomas Wenning, Jennifer Travis
This study examines the effectiveness of the US Department of Energy’s Better Plants Program Bootcamps, which are designed to enhance participants’ technical skills in improving energy efficiency and optimizing operations in manufacturing facilities. Through the analysis of survey data collected from 529 participants across 9 bootcamps, the research investigates the motivations, benefits, and demographic trends of attendees. The findings reveal that skill acquisition and improvement are primary drivers for participation, with key benefits including hands-on training on diagnostic equipment and software tools, networking opportunities, and access to technical resources. The analysis shows strong participation from sectors characterized by high energy consumption and employment, such as chemical and transportation equipment manufacturing. Over 50% of participants have job titles that include “EHS” or “Energy” showing their key roles in leading energy efficiency and energy management efforts in manufacturing. Furthermore, the analysis highlights the distribution of participants across managerial, engineering, and technical roles, revealing a higher representation of managers and engineers. This observation suggests a need for targeted outreach to engage technicians, equipment operators, maintenance staff, and floor workers to ensure comprehensive workforce development. The post-bootcamp survey showed that the participants highly valued the opportunities for peer learning and idea exchange, and the benefits they gained from them. This research contributes to the advancement of manufacturing education by demonstrating the efficacy of specialized training in addressing critical industry challenges and fostering a more competent and empowered workforce.
{"title":"Key insights from US Department of Energy Better Plants workforce development bootcamps (2022–2025)","authors":"Wei Guo, Kalie Miera, James Nguyen, Alexandra Botts, Paulomi Nandy, Thomas Wenning, Jennifer Travis","doi":"10.1016/j.mfglet.2025.12.001","DOIUrl":"10.1016/j.mfglet.2025.12.001","url":null,"abstract":"<div><div>This study examines the effectiveness of the US Department of Energy’s Better Plants Program Bootcamps, which are designed to enhance participants’ technical skills in improving energy efficiency and optimizing operations in manufacturing facilities. Through the analysis of survey data collected from 529 participants across 9 bootcamps, the research investigates the motivations, benefits, and demographic trends of attendees. The findings reveal that skill acquisition and improvement are primary drivers for participation, with key benefits including hands-on training on diagnostic equipment and software tools, networking opportunities, and access to technical resources. The analysis shows strong participation from sectors characterized by high energy consumption and employment, such as chemical and transportation equipment manufacturing. Over 50% of participants have job titles that include “EHS” or “Energy” showing their key roles in leading energy efficiency and energy management efforts in manufacturing. Furthermore, the analysis highlights the distribution of participants across managerial, engineering, and technical roles, revealing a higher representation of managers and engineers. This observation suggests a need for targeted outreach to engage technicians, equipment operators, maintenance staff, and floor workers to ensure comprehensive workforce development. The post-bootcamp survey showed that the participants highly valued the opportunities for peer learning and idea exchange, and the benefits they gained from them. This research contributes to the advancement of manufacturing education by demonstrating the efficacy of specialized training in addressing critical industry challenges and fostering a more competent and empowered workforce.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 20-24"},"PeriodicalIF":2.0,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798342","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 : 2025-12-04DOI: 10.1016/j.mfglet.2025.11.008
Vikash Kumar, Atosh Kumar Sinha, Susanta Pramanik, Krishna P. Yagati
Functionally Graded Material (FGM) was fabricated with three different graded layers (10, 15, and 20 wt% CF-PLA) using Fused Deposition Modelling (FDM) technique. This work investigates compression, impact behavior, and the structure of AM-fabricated FGM. This work compares pure PLA, 20 wt% CF-PLA, and FGM. FGM recorded the highest impact strength of 25 ± 1.3 kJ/m2 and compressive strength of 121.57 ± 1.7 MPa. The 20 wt% CF-PLA showed the highest compressive strength of 134.98 ± 2.1 MPa. FGMs with strong interlayer bonding between the graded layers were fabricated. Pure PLA and 20 wt% CF-PLA recorded ductile and brittle behaviour, respectively.
{"title":"Functionally graded materials fabricated through fused deposition modelling technique-mechanical property evaluation and failure mechanism","authors":"Vikash Kumar, Atosh Kumar Sinha, Susanta Pramanik, Krishna P. Yagati","doi":"10.1016/j.mfglet.2025.11.008","DOIUrl":"10.1016/j.mfglet.2025.11.008","url":null,"abstract":"<div><div>Functionally Graded Material (FGM) was fabricated with three different graded layers (10, 15, and 20 wt% CF-PLA) using Fused Deposition Modelling (FDM) technique. This work investigates compression, impact behavior, and the structure of AM-fabricated FGM. This work compares pure PLA, 20 wt% CF-PLA, and FGM. FGM recorded the highest impact strength of 25 ± 1.3 kJ/m<sup>2</sup> and compressive strength of 121.57 ± 1.7 MPa. The 20 wt% CF-PLA showed the highest compressive strength of 134.98 ± 2.1 MPa. FGMs with strong interlayer bonding between the graded layers were fabricated. Pure PLA and 20 wt% CF-PLA recorded ductile and brittle behaviour, respectively.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 15-19"},"PeriodicalIF":2.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798341","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 : 2025-12-01DOI: 10.1016/j.mfglet.2025.11.010
M.C.A. van der Pas , A.E. Akçay , R.M. Dijkman , I.J.B.F. Adan
Root Cause Analysis (RCA) of problems in a manufacturing process is becoming increasingly complex due to the growing complexity of products and processes. On the other hand, due to digitalisation and Industry 4.0, various systems collect an increasing volume of data about the processes. These data can be used to improve the RCA, but it can be challenging because the data is scattered across different systems. Therefore, this paper presents a framework to assist engineers with location-time RCA. The proposed framework is based on the Case-Based Reasoning cycle. It includes four steps: collect production trace data, enrich the collected data, identify the production trace, and finally compare traces to identify the location of the root cause. Techniques from the Semantic Web and Process Mining communities are combined in the framework to offer an integrated solution from data collection to root cause localisation.
{"title":"Combining case-based reasoning and process mining for root cause analysis in complex manufacturing environments","authors":"M.C.A. van der Pas , A.E. Akçay , R.M. Dijkman , I.J.B.F. Adan","doi":"10.1016/j.mfglet.2025.11.010","DOIUrl":"10.1016/j.mfglet.2025.11.010","url":null,"abstract":"<div><div>Root Cause Analysis (RCA) of problems in a manufacturing process is becoming increasingly complex due to the growing complexity of products and processes. On the other hand, due to digitalisation and Industry 4.0, various systems collect an increasing volume of data about the processes. These data can be used to improve the RCA, but it can be challenging because the data is scattered across different systems. Therefore, this paper presents a framework to assist engineers with location-time RCA. The proposed framework is based on the Case-Based Reasoning cycle. It includes four steps: collect production trace data, enrich the collected data, identify the production trace, and finally compare traces to identify the location of the root cause. Techniques from the Semantic Web and Process Mining communities are combined in the framework to offer an integrated solution from data collection to root cause localisation.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"46 ","pages":"Pages 156-160"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684483","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}
We consider a warehouse where loads are transported by a fleet of elementary robots that can be aggregated to form poly-robots. These poly-robots can be dynamically reconfigured to handle various load types. Our work focuses on scheduling this robotic fleet with the goal of minimizing the total time required to complete all transportation operations. We introduce an integer linear programming model for two scenarios, with and without robot reconfiguration. We demonstrate that enabling reconfiguration can greatly reduce execution time in some situations. We also show that the linear programs can be solved for the industrial case study under consideration.
{"title":"Reconfigurable poly-robots for warehouse transport operations","authors":"Mari Chaikovskaia , Jean-Philippe Gayon , Zine Elabidine Chebab , Jean-Christophe Fauroux","doi":"10.1016/j.mfglet.2025.11.009","DOIUrl":"10.1016/j.mfglet.2025.11.009","url":null,"abstract":"<div><div>We consider a warehouse where loads are transported by a fleet of elementary robots that can be aggregated to form poly-robots. These poly-robots can be dynamically reconfigured to handle various load types. Our work focuses on scheduling this robotic fleet with the goal of minimizing the total time required to complete all transportation operations. We introduce an integer linear programming model for two scenarios, with and without robot reconfiguration. We demonstrate that enabling reconfiguration can greatly reduce execution time in some situations. We also show that the linear programs can be solved for the industrial case study under consideration.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"47 ","pages":"Pages 11-14"},"PeriodicalIF":2.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749722","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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