Journal of Manufacturing Systems最新文献

{"title":"A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines","authors":"Jing Wang ,&nbsp;Deming Lei ,&nbsp;Debiao Li ,&nbsp;Xixing Li ,&nbsp;Hongtao Tang","doi":"10.1016/j.jmsy.2024.10.019","DOIUrl":"10.1016/j.jmsy.2024.10.019","url":null,"abstract":"<div><div>Distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with batch processing machines (BPMs) is rarely considered, let alone DEHFSP with BPMs and uncertainty. In this study, a fuzzy DEHFSP with BPMs at a middle stage and no precedence between some stages is presented, and a dynamic artificial bee colony (DABC) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and fuzzy total energy consumption. To produce high quality solutions, Metropolis criterion is used, dynamic employed bee phase based on neighborhood structure dynamic selection is implemented, and group-based onlooker bee phase with bidirectional communication is given. Migration operator is also adopted to replace scout bee phase. Extensive experiments are conducted, and the optimal combination of key parameters for DABC is decided by the Taguchi method. Comparative results and statistical analysis show that new strategies of DABC are effective, and DABC is highly competitive in solving the considered fuzzy DEHFSP.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"78 ","pages":"Pages 94-108"},"PeriodicalIF":12.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721390","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 carbon emissions accounting in the mixed production process of multi-pressure die-castingproducts based on cyber physical production system","authors":"Hongcheng Li ,&nbsp;Jian Peng ,&nbsp;Yachao Jia ,&nbsp;Rong Luo ,&nbsp;Huajun Cao ,&nbsp;Yunpeng Cao ,&nbsp;Yu Zhang ,&nbsp;Haihong Shi","doi":"10.1016/j.jmsy.2024.11.005","DOIUrl":"10.1016/j.jmsy.2024.11.005","url":null,"abstract":"<div><div>Die-casting is an efficient and precise casting process, but it consumes significant energy and contributes to severe environmental pollution. The characteristic features of the die-casting process chain include high demand for energy and resources, dynamic synergy among multiple processing equipment, and mixed production of various products. These characteristics lead to challenges in carbon emission accounting, such as the problem of carbon emission data haze. To address this issue, this study analyzes the dynamic characteristics of carbon emissions in the die-casting process chain to identify the sources of carbon emissions. Subsequently, a multi-source carbon data collection scheme is developed based on these sources, and an information-physical fusion-based model for carbon source data collection and integration is established. Following this, the correlation between carbon sources in the die-casting process chain and the production process is elucidated, and a carbon emission accounting model for mixed production of multiple die-casting products is developed. For model parameterization, time-series power data are systematically integrated. Finally, using the dynamic characteristics of carbon emissions from typical die-casting production and the carbon source data model as a foundation, a case study is conducted on the carbon emissions from mixed production in the die-casting process chain. The results demonstrate the effectiveness, feasibility, and reliability of the proposed carbon emission accounting model. This study lays the foundation for optimizing carbon reduction in the die-casting process chain and supports the transition to a low-carbon die-casting workshop.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"78 ","pages":"Pages 69-80"},"PeriodicalIF":12.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699295","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":"Flexible robotic cell scheduling with graph neural network based deep reinforcement learning","authors":"Donghai Wang ,&nbsp;Shun Liu ,&nbsp;Jing Zou ,&nbsp;Wenjun Qiao ,&nbsp;Sun Jin","doi":"10.1016/j.jmsy.2024.11.010","DOIUrl":"10.1016/j.jmsy.2024.11.010","url":null,"abstract":"<div><div>Flexible robotic cells are pivotal in flexible and customized manufacturing. An effective scheduling policy for such cells can significantly reduce the makespan and improve the production efficiency. This study introduces an innovative end-to-end real-time scheduling method leveraging deep reinforcement learning (DRL) to minimize the makespan in a flexible robotic cell. We introduce a heterogeneous disjunctive graph model for a nuanced representation of the scheduling problem, which incorporates transportation through specific disjunctive arcs. The DRL utilizes Graph Neural Network (GNN) for model feature extraction and employs Proximal Policy Optimization (PPO) to train the scheduling agent. Our methodology can also better leverage the transport robot capacity to mitigate system blockage and deadlock. Numerical experiments are conducted to demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"78 ","pages":"Pages 81-93"},"PeriodicalIF":12.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699296","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":"Novel deep learning based soft sensor feature extraction for part weight prediction in injection molding processes","authors":"Weilong Ding,&nbsp;Husnain Ali,&nbsp;Kaihua Gao,&nbsp;Zheng Zhang,&nbsp;Furong Gao","doi":"10.1016/j.jmsy.2024.11.011","DOIUrl":"10.1016/j.jmsy.2024.11.011","url":null,"abstract":"<div><div>In the current injection molding (IM) industry, it remains challenging to monitor and estimate production quality promptly. It is costly and time-consuming to measure part quality manually after each production cycle ends, which results in quality defects difficult to be captured in time. In this case, a soft sensor is essential to model the IM process and predict the final quality in real time with multi-source industrial production data. However, traditional data-driven modeling methods fail to take advantage of the information in complex high-frequency data from in-mold sensors, resulting in an inaccurate IM model and unsatisfactory quality prediction performance. To solve this problem, this paper proposes a novel soft sensor framework based on a teacher-student structure. After specialized preprocessing of multiple sensor time series data, a GRU-based autoencoder with an attention mechanism (GRU-A-AE) is trained as a teacher model, extracting deep implicit features involving valuable time sequential information. Then, a cascaded relationship among shallow feature points from sensor signals, deep features, and final part weights is established using back propagation neural networks (BPNNs). To demonstrate its effectiveness and superiority, the proposed soft sensor is trained and tested with practical IM data under normal and fluctuating production conditions, respectively. Compared with conventional methods, our method has higher prediction accuracy with testing RMSE of 0.1049 and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.9950 under normal conditions, which proves more valuable information in high-frequency sensor signals are explored from the teacher model and IM production dynamics are captured precisely. In addition, its better prediction performance in the case of production condition fluctuation verifies its strong robustness.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"78 ","pages":"Pages 58-68"},"PeriodicalIF":12.2,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699294","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":"Meta-learning enhanced adaptive robot control strategy for automated PCB assembly","authors":"Jieyang Peng ,&nbsp;Dongkun Wang ,&nbsp;Junkai Zhao ,&nbsp;Yunfei Teng ,&nbsp;Andreas Kimmig ,&nbsp;Xiaoming Tao ,&nbsp;Jivka Ovtcharova","doi":"10.1016/j.jmsy.2024.11.009","DOIUrl":"10.1016/j.jmsy.2024.11.009","url":null,"abstract":"<div><div>The assembly of printed circuit boards (PCBs) is one of the standard processes in chip production, directly contributing to the quality and performance of the chips. In the automated PCB assembly process, machine vision and coordinate localization methods are commonly employed to guide the positioning of assembly units. However, occlusion or poor lighting conditions can affect the effectiveness of machine vision-based methods. Additionally, the assembly of odd-form components requires highly specialized fixtures for assembly unit positioning, leading to high costs and low flexibility, especially for multi-variety and small-batch production. Drawing on these considerations, a vision-free, model-agnostic meta-method for compensating robotic position errors is proposed, which maximizes the probability of accurate robotic positioning through interactive feedback, thereby reducing the dependency on visual feedback and mitigating the impact of occlusions or lighting variations. The proposed method endows the robot with the capability to learn and adapt to various position errors, inspired by the human instinct for grasping under uncertainties. Furthermore, it is a self-adaptive method that can accelerate the robotic positioning process as more examples are incorporated and learned. Empirical studies show that the proposed method can handle a variety of odd-form components without relying on specialized fixtures, while achieving similar assembly efficiency to highly dedicated automation equipment. As of the writing of this paper, the proposed meta-method has already been implemented in a robotic-based assembly line for odd-form electronic components. Since PCB assembly involves various electronic components with different sizes, shapes, and functions, subsequent studies can focus on assembly sequence and assembly route optimization to further enhance assembly efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"78 ","pages":"Pages 46-57"},"PeriodicalIF":12.2,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699248","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 multivariate fusion collision detection method for dynamic operations of human-robot collaboration systems","authors":"Shukai Fang ,&nbsp;Shuguang Liu ,&nbsp;Xuewen Wang ,&nbsp;Jiapeng Zhang ,&nbsp;Jingquan Liu ,&nbsp;Qiang Ni","doi":"10.1016/j.jmsy.2024.11.007","DOIUrl":"10.1016/j.jmsy.2024.11.007","url":null,"abstract":"<div><div>Real-time human-robot collision detection is crucial for ensuring the safety of operators during human-robot collaboration(HRC) and for improving the efficiency of such collaboration. It plays an important role in promoting the development of intelligent manufacturing. To address this issue, our team developed a multi-faceted collision detection system using eXtended Reality (XR) technology, specifically designed for complex and dynamic human-robot collaborative operations. The system integrates three different methods: a Virtual Reality (VR) detection method that enables robots to better perceive and detect human operators. An Augmented Reality (AR) detection method that enhances the operator’s perception of the robot. And a fusion detection and evaluation method. This detection and evaluation method assesses the effectiveness of collaboration by analyzing key performance indicators, such as real-time distance between human and robot, changes in the operator’s Heart Rate(HR), and overall task completion time. Through empirical research on the human-robot collaborative assembly task of <em>T</em>-series spiral bevel gear reducers, the effectiveness of the innovative method is verified. The research results show that this method significantly improves safety and operational efficiency, providing a novel solution detection in industrial manufacturing environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"78 ","pages":"Pages 26-45"},"PeriodicalIF":12.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699247","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":"From caged robots to high-fives in robotics: Exploring the paradigm shift from human–robot interaction to human–robot teaming in human–machine interfaces","authors":"Filippo Sanfilippo ,&nbsp;Muhammad Hamza Zafar ,&nbsp;Timothy Wiley ,&nbsp;Fabio Zambetta","doi":"10.1016/j.jmsy.2024.10.015","DOIUrl":"10.1016/j.jmsy.2024.10.015","url":null,"abstract":"<div><div>Multi-modal human–machine interfaces have recently undergone a remarkable transformation, progressing from simple human–robot interaction (HRI) to more advanced human–robot collaboration (HRC) and, ultimately, evolving into the concept of human–robot teaming (HRT). The aim of this work is to delineate a progressive path in this evolving transition. A structured, position-oriented review is proposed. Rather than aiming for an exhaustive survey, our objective is to propose a structured approach in a field that has seen diverse and sometimes divergent definitions of HRI/C/T in the literature. This conceptual review seeks to establish a unified and systematic framework for understanding these paradigms, offering clarity and coherence amidst their evolving complexities. We focus on integrating multiple sensory modalities — such as visual, aural, and tactile inputs — within human–machine interfaces. Central to our approach is a running use case of a warehouse workflow, which illustrates key aspects including modelling, control, communication, and technological integration. Additionally, we investigate recent advancements in machine learning and sensing technologies, emphasising robot perception, human intention recognition, and collaborative task engagement. Current challenges and future directions, including ethical considerations, user acceptance, and the need for explainable systems, are also addressed. By providing a structured pathway from HRI to HRT, this work aims to foster a deeper understanding and facilitate further advancements in human–machine interaction paradigms.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"78 ","pages":"Pages 1-25"},"PeriodicalIF":12.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699297","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":"Handling features in assembly: Integrating manufacturing considerations early in design discussions","authors":"Nathaly Rea Minango ,&nbsp;Mikael Hedlind ,&nbsp;Antonio Maffei","doi":"10.1016/j.jmsy.2024.11.012","DOIUrl":"10.1016/j.jmsy.2024.11.012","url":null,"abstract":"<div><div>The early stages of product design are critical for incorporating manufacturing perspectives. Recognizing the significance of assembly in discrete product manufacturing, the study emphasizes the need to consider the intricacies of assembly early in the design stages. While existing research has addressed assembly features, especially for insertion, this study focuses on handling features, seeking to bridge the gap in their comprehensive representation within the product model. Based on a relational analysis, product characteristics relevant for handling were identified and represented by using a modelling strategy that facilitates their timely addition to the product model. A case study was developed to demonstrate its application. The main contributions of this work comprise an extensive list of product characteristics related to handling processes, a proposal for integrating these characteristics into the product model, and a collaborative way to define product features during product design. Future research directions point to the establishment of a model-based definition for assembly processes, paving the way for enhanced cross-disciplinary communication in the fields of product design and assembly planning.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 1077-1100"},"PeriodicalIF":12.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707068","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 new proposal for energy efficiency in industrial manufacturing systems based on machine learning techniques","authors":"Rômulo César Cunha Lima ,&nbsp;Leonardo Adriano Vasconcelos de Oliveira ,&nbsp;Suane Pires Pinheiro da Silva ,&nbsp;José Daniel de Alencar Santos ,&nbsp;Rebeca Gomes Dantas Caetano ,&nbsp;Francisco Nélio Costa Freitas ,&nbsp;Venício Soares de Oliveira ,&nbsp;Andreyson de Freitas Bonifácio ,&nbsp;Pedro Pedrosa Rebouças Filho","doi":"10.1016/j.jmsy.2024.10.025","DOIUrl":"10.1016/j.jmsy.2024.10.025","url":null,"abstract":"<div><div>This research presents a novel methodology for enhancing energy efficiency in industrial manufacturing systems through machine learning techniques. Specifically, the study focuses on the automatic classification of five steel types — ABNT SAE 1020, 1045, 4140, 4340, and VC — based on electrical and mechanical characteristics observed during turning operations. The methodology includes the prediction of energy consumption for these steel types, applying regression models, under various machining conditions, including different rotation speeds and feed rates. To the best of the authors’ knowledge, this study is the first to address this issue using this specific approach. The proposed method was validated through computational experiments using multiple machine learning algorithms, with the Multilayer Perceptron (MLP) neural network achieving the highest classification accuracy of 95.52%. In terms of energy consumption prediction, MLP models demonstrated superior performance in 13 out of 15 turning scenarios. The regression analysis further confirmed the effectiveness of these models, achieving low Root Mean Squared Error (RMSE) values across different configurations. The results indicate that integrating machine learning into machining processes can significantly improve energy efficiency, leading to more sustainable industrial practices.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 1062-1076"},"PeriodicalIF":12.2,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707067","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":"Digital twin based photogrammetry field-of-view evaluation and 3D layout optimisation for reconfigurable manufacturing systems","authors":"Zi Wang ,&nbsp;Likun Wang ,&nbsp;Giovanna Martínez-Arellano ,&nbsp;Joseph Griffin ,&nbsp;David Sanderson ,&nbsp;Svetan Ratchev","doi":"10.1016/j.jmsy.2024.11.001","DOIUrl":"10.1016/j.jmsy.2024.11.001","url":null,"abstract":"<div><div>Photogrammetry is extensively used in manufacturing processes due to its non-contact nature and rapid data acquisition. Positioning photogrammetry cameras requires knowledge of the manufacturing process and time in manual field-of-view (FoV) adjustment. Such a lengthy and labour-intensive process is not suitable for modern manufacturing systems, where automation, robotics and dynamic reconfigurable layout are used to shorten production time and adapt to demand changes. Hence, there exists the need for a fast layout planning approach ensuring manufacturing process feasibility and maximising camera FoV effectiveness. This paper introduces a digital twin based FoV evaluation method and a computationally efficient 3D layout optimisation framework for reconfigurable manufacturing systems. The framework computes optimal layout for photogrammetry cameras and the object of interest (OOI). The automated nature of the proposed framework can speed up planning processes and shorten dynamic system commissioning time. At a technical level, the framework takes advantage of a 3D digital twin, and uses point clouds to represent the camera FoV. Iterative Closest Point (ICP) registration and K-Dimensional Tree (KDTree) intersection techniques are applied to calculate OOI visibility and target coverage ratio. Experimental validation attested to a digital-physical similarity exceeding 93%, indicating a high level of fidelity and the feasibility of station-level 3D layout design in digital twin environments. Feeding into the 3D layout planning, the optimisation framework considers robot reachability, FoV effectiveness, and estimated uncertainty. Given characteristics of the objective function, genetic algorithm, simulated annealing, and Bayesian optimisation were evaluated within a computational budget (100 function calls). The optimised results are compared against a baseline best obtained through brute force grid search. All tested algorithms achieved results within 98% of the grid search’s best solution within 5 min. Genetic algorithm and simulated annealing outperformed the baseline best by 0.16% and 0.25% respectively for OOI visibility, and Bayesian optimisation exceeded the baseline best by 0.12% for target coverage. These findings emphasise the feasibility of the proposed approach and the efficiency of the overall framework, highlighting its applicability across various development stages from design to execution in a dynamic manufacturing environment.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 1045-1061"},"PeriodicalIF":12.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707066","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}