Journal of Manufacturing Systems最新文献

{"title":"Surface roughness prediction in machining using two-stage domain-incremental learning with input dimensionality expansion","authors":"Clayton Cooper ,&nbsp;Jianjing Zhang ,&nbsp;Y.B. Guo ,&nbsp;Robert X. Gao","doi":"10.1016/j.jmsy.2025.03.014","DOIUrl":"10.1016/j.jmsy.2025.03.014","url":null,"abstract":"<div><div>Accurate quantification of machined surface roughness is crucial to the characterization of part performance measures, including aerodynamics and biocompatibility. Given this cruciality, there exists a need for predictive metrology models which can predict the surface roughness before a part leaves a machine to reduce metrology-induced bottlenecks and improve production planning efficiency under emergent production paradigms, e.g., Industry 4.0. Current predictive metrology approaches in machining generally train machine learning models on all available input features at once. However, this approach yields a high number of free parameters during all states of training, possibly leading to suboptimal prediction results because of the complexity of simultaneously optimizing all parameters at once. In addition, previous machine learning-enabled surface roughness prediction studies have used limited test dataset sizes, which reduces the reliability and robustness of the reported results. To address these limitations, this study proposes a two-stage model training approach based on domain-incremental learning, wherein a second stage of training is performed using an expanded input domain. The proposed method is evaluated on a 3,000-element experimentally collected testing dataset of machined H13 tool steel surfaces, where it achieves 16.3 % roughness prediction error compared to the 29.5 % error of the conventional single-stage training approach, indicating the suitability of the two-stage training method for reducing surface roughness prediction error.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 503-510"},"PeriodicalIF":12.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A low-code modular CAD system oriented towards machining features for STEP-NC compliant manufacturing","authors":"Kaiyao Zhang,&nbsp;Wenlei Xiao,&nbsp;Xiangming Fan,&nbsp;Gang Zhao","doi":"10.1016/j.jmsy.2025.03.010","DOIUrl":"10.1016/j.jmsy.2025.03.010","url":null,"abstract":"<div><div>The next-generation STEP-NC technology requires automatic generation of machining strategies within manufacturing systems to implement flexible manufacturing in the future. Currently, the machining feature modeling based on STEP-NC is in its infancy, facing challenges such as cumbersome modeling processes, ineffective utilization of the STEP-NC standard, and low development efficiency. A low-code modular solution based on the STEP-NC data kernel for machining feature-oriented modeling is important to achieve more intelligent flexible manufacturing. This paper presents a low-code modular modeling method for machining features, based on the STEP-NC data structure and incorporating geometric, process, and machining information, aimed at part milling. A low-code modular CAD modeling platform based on STEP-NC was built using Rhino Grasshopper. Additionally, a toolpath generation algorithm was designed for milling feature models to enable the automatic generation of milling strategies. Finally, the feasibility of a low-code modular CAD system based on machining features for STEP-NC compliant manufacturing in engineering applications is validated through a case study involving part design, milling, and optimization.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 487-502"},"PeriodicalIF":12.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative life cycle assessment of kraft lignin and hemp straw fillers to improve ductility of polylactide (PLA) 3D printed parts","authors":"Dolor R. Enarevba ,&nbsp;Nebojsa I. Jaksic ,&nbsp;Karl R. Haapala","doi":"10.1016/j.jmsy.2025.03.015","DOIUrl":"10.1016/j.jmsy.2025.03.015","url":null,"abstract":"<div><div>This research is motivated by the increasing demand for bio-based materials, the recent growth of the U.S. hemp industry, and the broader trend of using sustainable filaments in additive manufacturing. This study presents a comparative life cycle assessment (LCA) of untreated hemp straw and kraft lignin as fillers for polylactic acid (PLA) 3D-printed tensile specimens to evaluate their environmental impacts. Both materials, being low-cost renewable bio-based fillers, can enhance elongation of PLA while reducing the environmental footprint of 3D-printed components. The environmental impacts of hemp straw-PLA and kraft lignin-PLA were assessed using several life cycle impact assessment (LCIA) methods, including ReCiPe 2016 Endpoint (H), Cumulative Energy Demand (CED), and IPCC GWP100. Hemp straw showed lower environmental impacts than kraft lignin across most categories, making it a more favorable option for eco-conscious prosumers. The ReCiPe 2016 results indicated that major impact categories for kraft lignin-PLA were fine particulate matter formation, global warming potential, and human toxicity, with filament production being the major contributor. For hemp straw-PLA, hemp straw pre-processing was the major contributor. The CED method revealed that nonrenewable fossil resources had the highest impact on both materials. IPCC GWP100 results aligned with CED, showing higher greenhouse gas emissions for kraft lignin-PLA, mainly due to fossil fuel use. Sensitivity analysis of transportation distances showed no significant differences in impact results, while alternative LCIA methods (TRACI and IMPACT World+) confirmed the consistency of the findings. To build upon this study, future work will explore the environmental performance of treated hemp materials as alternative fillers for 3D-printed components.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 479-486"},"PeriodicalIF":12.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An enhanced memetic algorithm for energy-efficient and low-carbon flexible job shop scheduling problem considering machine restart","authors":"Wentao Wang,&nbsp;Jing Zhao","doi":"10.1016/j.jmsy.2025.03.013","DOIUrl":"10.1016/j.jmsy.2025.03.013","url":null,"abstract":"<div><div>The technological advancements of Industry 5.0 place greater emphasis on environmental sustainability and resilience for production scheduling. The flexible job shop scheduling problem (FJSP) effectively adapts to complex production environments and diverse scheduling requirements, which has made it an essential tool for studying modern production scenarios. Against this backdrop, this paper proposes an energy-efficient and low-carbon flexible job shop scheduling problem considering machine restart (ELFJSP-MR), aiming to minimize the makespan and total carbon emissions of the system. To solve ELFJSP-MR, we present an enhanced memetic algorithm (EMA) and design machine restart strategy to balance energy consumption and equipment lifespan. A multi-population hybrid model initialization based on logistic population growth model is used to enhance initial population diversity. Two novel neighborhood search methods are developed to improve convergence speed and explore the solution space more thoroughly. To enhance the flexibility and efficiency of local search, an adaptive operator selection model is designed. Finally, EMA and four well-known algorithms are evaluated on various benchmark problem instances. Experimental results demonstrate that EMA achieves faster convergence and greater stability for ELFJSP-MR. Furthermore, EMA exhibits exceptional performance across eight instances of aerospace composite material processing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 457-478"},"PeriodicalIF":12.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of a thermo-mechanically controlled virtual finishing mill process with coupled interactive rolling automation and setup calculations","authors":"Joonas Ilmola ,&nbsp;Oskari Seppälä ,&nbsp;Joni Paananen ,&nbsp;Aarne Pohjonen ,&nbsp;Juha Pyykkönen ,&nbsp;Jari Larkiola","doi":"10.1016/j.jmsy.2025.03.018","DOIUrl":"10.1016/j.jmsy.2025.03.018","url":null,"abstract":"<div><div>The steel industry is facing a big change as they are reducing carbon dioxide emissions. The transition to carbon neutral or fossil free steel manufacturing requires investments in new technologies e.g. direct reduction of iron or scrap-based production. Consequently, compact hot strip rolling lines and electric arc furnaces will replace conventional hot strip rolling processes operating with slab reheating furnaces. In compact strip production lines, the casting size is significantly larger compared to conventional process with individual slabs. This reduces the possibility of experimental rolling tests and therefore experimental testing should be replaced with modelling and simulations. In order to create a comprehensive model of the finishing mill to reduce experimental tests significantly, the virtual finishing mill is required. Therefore, the virtual finishing mill is developed to simulate hot strip production considering the boundary conditions of an industrial scale finishing rolling mill using a finite element model. Virtual finishing mill contains implemented virtual rolling automation which performs setup calculations for mill stands, mass flow control and strip tensioning between sequential rolling stands and carries out roll gap clearance adjustments for six stands finishing mill. This research focuses on mechanical process stability in full scale FE-model of finishing mill. Also, the reliable boundary conditions addressed to the hot strip in the finishing mill process are produced. Developed virtual finishing mill delivers a thorough thermo-mechanical state of the strip through the finishing mill to be further utilized by sub-models of metallurgical phenomena like recovery, precipitation and grain growth. The validation for setup calculations and FE-model are completed by comparing results of mathematical methods and comparison to industrial data.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 436-456"},"PeriodicalIF":12.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-LLM collaboration in generative design for customization","authors":"Xingzhi Wang ,&nbsp;Zhoumingju Jiang ,&nbsp;Yi Xiong ,&nbsp;Ang Liu","doi":"10.1016/j.jmsy.2025.03.012","DOIUrl":"10.1016/j.jmsy.2025.03.012","url":null,"abstract":"<div><div>Generative design enables the rapid creation of diverse designs, making it a promising means for customization. However, due to the multidisciplinary knowledge required for operation, the full potential of generative design for customization (GDfC) remains under-explored. Recently, large language models (LLM) have attracted significant attention from designers. Unlike traditional text-based generative models, LLM’s expansive knowledge base and unique interaction capabilities offer clear advantages for assuming more proactive roles in GDfC. Against the background, this paper explores the potential of LLM in redefining GDfC. Based on the division of the generative design process, this paper identifies three human-LLM collaboration schemes to demonstrate the potential roles of LLM in GDfC. Additionally, this paper proposes a process framework based on the characteristics of required design knowledge, which aids designers in selecting the appropriate LLM performance enhancement strategy for their customization tasks. A case study of vehicle interior customization is presented to demonstrate the application of the proposed framework.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 425-435"},"PeriodicalIF":12.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time decision-making for Digital Twin in additive manufacturing with Model Predictive Control using time-series deep neural networks","authors":"Yi-Ping Chen ,&nbsp;Vispi Karkaria ,&nbsp;Ying-Kuan Tsai ,&nbsp;Faith Rolark ,&nbsp;Daniel Quispe ,&nbsp;Robert X. Gao ,&nbsp;Jian Cao ,&nbsp;Wei Chen","doi":"10.1016/j.jmsy.2025.03.009","DOIUrl":"10.1016/j.jmsy.2025.03.009","url":null,"abstract":"<div><div>Digital Twin – a virtual replica of a physical system enabling real-time monitoring, model updating, prediction, and decision-making – combined with recent advances in machine learning, offers new opportunities for proactive control strategies in autonomous manufacturing. However, achieving real-time decision-making with Digital Twins requires efficient optimization driven by accurate predictions of highly nonlinear manufacturing systems. This paper presents a simultaneous multi-step Model Predictive Control (MPC) framework for real-time decision-making, using a multivariate deep neural network, named Time-Series Dense Encoder (TiDE), as the surrogate model. Unlike conventional MPC models which only provide one-step ahead prediction, TiDE is capable of predicting future states within the prediction horizon in one shot (multi-step), significantly accelerating the MPC. Using Directed Energy Deposition (DED) additive manufacturing as a case study, we demonstrate the effectiveness of the proposed MPC in achieving melt pool temperature tracking to ensure part quality, while reducing porosity defects by regulating laser power to maintain melt pool depth constraints. In this work, we first show that TiDE is capable of accurately predicting melt pool temperature and depth. Second, we demonstrate that the proposed MPC achieves precise temperature tracking while satisfying melt pool depth constraints within a targeted dilution range (10%–30%), reducing potential porosity defects. Compared to Proportional–Integral–Derivative (PID) controller, the MPC results in smoother and less fluctuating laser power profiles with competitive or superior melt pool temperature control performance. This demonstrates the MPC’s proactive control capabilities, leveraging time-series prediction and real-time optimization, positioning it as a powerful tool for future Digital Twin applications and real-time process optimization in manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 412-424"},"PeriodicalIF":12.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic flexible flow shop scheduling via cross-attention networks and multi-agent reinforcement learning","authors":"Jinlong Zheng ,&nbsp;Yixin Zhao ,&nbsp;Yinya Li ,&nbsp;Jianfeng Li ,&nbsp;Liangeng Wang ,&nbsp;Di Yuan","doi":"10.1016/j.jmsy.2025.03.005","DOIUrl":"10.1016/j.jmsy.2025.03.005","url":null,"abstract":"<div><div>With the increasing uncertainty in production environments and changes in market demand, flexible and efficient scheduling solutions have become particularly critical. However, existing research mainly focuses on static scheduling or relatively simple dynamic scheduling problems, which are inadequate to address the complexities of actual production processes. This paper considers the dynamic flexible flow shop scheduling problem (DFFSP) characterized by diverse processes, complexity, and high flexibility, and proposes a multi-agent reinforcement learning algorithm based on cross-attention networks (MARL_CA). First, this paper proposes a novel state feature representation method, which represents the job processing data and the production Gantt chart as a state matrix, fully reflecting the environment state in the scheduling process. In addition, a cross-attention network is proposed to extract state features, enabling efficient discovery of complex relationships between jobs and machines, thereby enhancing the model's ability to understand intricate features. The model is trained using an independent proximal policy optimization (IPPO) based on the actor-critic method to help agents learn accurate and efficient scheduling strategies. Experimental results on a large number of static and dynamic scheduling instances demonstrate that the proposed algorithm outperforms traditional heuristic rules and other advanced algorithms, exhibiting strong learning efficiency and generalization capability.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 395-411"},"PeriodicalIF":12.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-adaptive production performance monitoring framework under different operating regimes","authors":"Joao Paulo Jacomini Prioli ,&nbsp;Nur Banu Altinpulluk ,&nbsp;Jeremy L. Rickli ,&nbsp;Murat Yildirim","doi":"10.1016/j.jmsy.2025.02.011","DOIUrl":"10.1016/j.jmsy.2025.02.011","url":null,"abstract":"<div><div>Dynamic operational regimes in modern manufacturing systems generate a myriad of challenges for production performance monitoring applications. Heterogeneous data streams and fast production changeovers often complicate sensor information, leading to misinterpretation of systemic performance issues. Conventional methods address this problem by explicitly modeling these operational regimes. However, it requires significant engineering hours and expertise, constituting a substantial adoption barrier for small-to-medium enterprises (SMEs). This paper proposes a self-adaptive smart monitoring framework that autonomously discovers and accounts for operational regime changes to offer accurate predictions on systemic performance despite the complexities in continuous multi-sourced data acquisition and dynamic regime behavior of machines. Computational experiments tested the methodology using a predictive system in two manufacturing cells under dynamic operational regimes. The proposed framework outperforms benchmark policies commonly used in prediction models by improving prediction accuracy from 3% to 62%, along with a better convergence rate. The results demonstrated that the proposed framework can positively impact smart maintenance implementation for SMEs with limited resources.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 380-394"},"PeriodicalIF":12.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-enhanced digital twins for predictive analytics in battery pack assembly","authors":"Muhammad Waseem ,&nbsp;Changbai Tan ,&nbsp;Seog-Chan Oh ,&nbsp;Jorge Arinez ,&nbsp;Qing Chang","doi":"10.1016/j.jmsy.2025.03.007","DOIUrl":"10.1016/j.jmsy.2025.03.007","url":null,"abstract":"<div><div>The electric vehicle (EV) market is rapidly growing, with battery modules playing a central role in this transformation. However, optimizing production throughput in battery module assembly is challenging due to the complexity of multi-stage processes and bottlenecks that limit overall efficiency. Traditional solutions, such as direct shop floor adjustments, simulation models, and digital twins (DT), can be costly and less scalable. This study proposes a digital twin surrogate (DTS) model, integrating machine learning techniques—Linear Regression, Support Vector Regression, K-Nearest Neighbors, Random Forest Regression, Deep Neural Networks, XGBoost, and Long Short-Term Memory networks—to estimate throughput and predict future machine states. The impact of dataset size and aggregation methods on model performance is also examined, providing shop managers with insights into how production line variations affect throughput.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 344-355"},"PeriodicalIF":12.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}