Journal of Manufacturing Systems最新文献

AdaBoost-inspired co-evolution differential evolution for reconfigurable flexible job shop scheduling considering order splitting 考虑订单分割的可重构灵活作业车间调度的 AdaBoost启发协同进化差分进化论

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-16 DOI: 10.1016/j.jmsy.2024.11.003

Lixin Cheng , Shujun Yu , Qiuhua Tang , Liping Zhang , Zikai Zhang

With the increasing demand for personalized and diversified products, manufacturing industries are in urgent need of taking measures to reduce the differences among products and enhance flexibility and reconfigurability so as to accommodate these personalized and diversified products. Consequently, this research focuses on the reconfigurable flexible job shop scheduling problem with order splitting taken into consideration. A mixed-integer linear programming model is proposed with the aim of minimizing tardiness costs, reconfiguration costs and energy costs. To solve this problem efficiently, a co-evolution differential evolution algorithm is developed, which is enhanced by an AdaBoost-inspired multiple mutation strategies ensemble mechanism (AMMSE), an AdaBoost-inspired adaptive crossover mechanism (AAC), rule-based initialization, and variable neighborhood search. Among them, AMMSE can effectively ensemble the advantages of different mutation strategies by adaptively selecting a proper number of chromosomes to train mutation strategies with different performance weights. AAC can adaptively control the crossover rate of each gene by evaluating the average importance score of each gene based on the performance weight distribution of chromosomes. Experimental results demonstrate that combining the above improvements can significantly boost the performance of the differential evolution algorithm. As a result, the enhanced algorithm outperforms other state-of-the-art algorithms by a large margin. By using the enhanced algorithm to solve the studied problem, nearly 1.1 times of production costs can be saved.

随着个性化和多样化产品需求的不断增加，制造业迫切需要采取措施减少产品之间的差异，提高灵活性和可重构性，以适应这些个性化和多样化产品的需求。因此，本研究将重点放在考虑订单分割的可重构柔性作业车间调度问题上。本文提出了一个混合整数线性规划模型，目的是最大限度地降低延迟成本、重新配置成本和能源成本。为有效解决该问题，开发了一种协同进化差分进化算法，并通过 AdaBoost 启发的多重突变策略集合机制（AMMSE）、AdaBoost 启发的自适应交叉机制（AAC）、基于规则的初始化和变量邻域搜索对该算法进行了增强。其中，AMMSE 可通过自适应选择适当数量的染色体来训练具有不同性能权重的突变策略，从而有效集合不同突变策略的优势。AAC 可以根据染色体的性能权重分布，通过评估每个基因的平均重要性得分，自适应地控制每个基因的交叉率。实验结果表明，结合上述改进措施可以显著提高差分进化算法的性能。因此，增强算法的性能大大优于其他最先进的算法。使用增强算法解决所研究的问题，可节省近 1.1 倍的生产成本。

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引用次数: 0

An improved non-dominated sorting genetic algorithm II for distributed heterogeneous hybrid flow-shop scheduling with blocking constraints 用于具有阻塞约束的分布式异构混合流-车间调度的改进型非支配排序遗传算法 II

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-14 DOI: 10.1016/j.jmsy.2024.10.018

Xueyan Sun , Weiming Shen , Jiaxin Fan , Birgit Vogel-Heuser , Chunjiang Zhang

Distributed manufacturing is a new trend to accommodate the economic globalization, which means multiple geographically-distributed factories can collaborate to meet urgent delivery requirements. However, such factories may vary due to layout adjustments and equipment aging, thus the production efficiency greatly depends on the allocation of orders. This scenario is frequently found in energy-intensive process industries, e.g., chemical and pharmaceutical and industries, where the lack of buffers usually results in extra non-blocking constraints and makes the production scheduling even harder. Therefore, this paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem (DHHBFSP) for minimizing the makespan and total energy consumption simultaneously, and proposes an improved non-dominated sorting genetic algorithm II (INSGA-II) to address the problem. First, two heuristic algorithms, i.e., bi-objective considered heuristic (BCH) and similarity heuristic (SH), are developed for the population initialization. Then, to speed-up the local search, a comparison method for non-dominated solutions is proposed to reserve more solutions that are likely to be further improved. Afterwards, a probabilistic model is developed to eliminate unnecessary operations during local search processes. Finally, the proposed INSGA-II is tested on benchmark instances and a real-world case for the validation. Numerical experiments suggest that the SH can generates high-quality solutions within a very short period of time, and the BCH has significantly improved average IGD and HV values for the initial population. Besides, the probabilistic model saves considerable computational time for the local search without compromising the solution quality. With the help of these strategies, the proposed INSGA-II improves average IGD and HV values by 68 % and 57 % for the basic NSGA-II respectively, and obtains better Pareto fronts compared to existing multi-objective algorithms on the majority of test instances. Moreover, the industrial case study shows that the proposed INSGA-II is capable of providing solid scheduling plans for a pharmaceutical enterprise with large-scale orders.

分布式生产是适应经济全球化的新趋势，这意味着多个地理位置分散的工厂可以协同合作，满足紧急交货要求。然而，这些工厂可能会因布局调整和设备老化而各不相同，因此生产效率在很大程度上取决于订单的分配。这种情况经常出现在能源密集型流程工业中，例如化工、制药和工业，在这些行业中，缓冲区的缺乏通常会导致额外的非阻塞约束，使生产调度变得更加困难。因此，本文研究了分布式异构混合阻塞流车间调度问题（DHHBFSP），以同时最小化生产进度和总能耗，并提出了一种改进的非支配排序遗传算法 II（INSGA-II）来解决该问题。首先，为种群初始化开发了两种启发式算法，即双目标考虑启发式（BCH）和相似性启发式（SH）。然后，为了加速局部搜索，提出了一种非主导解的比较方法，以保留更多可能进一步改进的解。然后，开发了一个概率模型，以消除局部搜索过程中不必要的操作。最后，提出的 INSGA-II 在基准实例和实际案例中进行了验证测试。数值实验表明，SH 可以在很短的时间内生成高质量的解，而 BCH 则显著提高了初始群体的平均 IGD 值和 HV 值。此外，概率模型还能在不影响解质量的前提下为局部搜索节省大量计算时间。在这些策略的帮助下，所提出的 INSGA-II 与基本 NSGA-II 相比，平均 IGD 值和 HV 值分别提高了 68% 和 57%，与现有的多目标算法相比，在大多数测试实例上都能获得更好的帕累托前沿。此外，工业案例研究表明，所提出的 INSGA-II 能够为具有大规模订单的制药企业提供可靠的调度计划。

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引用次数: 0

Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review 基于深度强化学习的动态调度，实现弹性和可持续制造：系统综述

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-13 DOI: 10.1016/j.jmsy.2024.10.026

Chao Zhang , Max Juraschek , Christoph Herrmann

Dynamic scheduling plays a pivotal role in smart manufacturing by enabling real-time adjustments to production schedules, thereby enhancing system resilience and promoting sustainability. By efficiently responding to disruptions, dynamic scheduling maintains productivity and stability, while also reducing resource consumption and environmental impact through optimized operations and the potential integration of renewable energy. Deep Reinforcement Learning (DRL), a cutting-edge artificial intelligence technique, shows promise in tackling the complexities of production scheduling, particularly in solving NP-hard combinatorial optimization problems. Despite its potential, a comprehensive study of DRL's impact on dynamic scheduling, especially regarding system resilience and sustainability, has been lacking. This paper addresses this gap by presenting a systematic review of DRL-based dynamic scheduling focusing on resilience and sustainability. Through an analysis of two decades of literature, key application scenarios of DRL in dynamic scheduling are examined, and specific indicators are defined to assess the resilience and sustainability of these systems. The findings demonstrate DRL's effectiveness across various production domains, surpassing traditional rule-based and metaheuristic algorithms, particularly in enhancing resilience. However, a significant gap remains in addressing sustainability aspects such as energy flexibility, resource utilization, and human-centric social impacts. This paper also explores current technical challenges, including multi-objective and multi-agent optimization, and proposes future research directions to better integrate resilience and sustainability in DRL-based dynamic scheduling, with an emphasis on real-world application.

动态调度在智能制造中发挥着关键作用，它能够对生产计划进行实时调整，从而增强系统的弹性并促进可持续发展。通过对中断做出有效响应，动态调度可以保持生产率和稳定性，同时还能通过优化操作和潜在的可再生能源整合来减少资源消耗和环境影响。深度强化学习（DRL）是一种前沿的人工智能技术，有望解决生产调度的复杂性，尤其是在解决 NP 难度的组合优化问题方面。尽管 DRL 潜力巨大，但对 DRL 对动态调度的影响，尤其是对系统弹性和可持续性的影响，一直缺乏全面的研究。本文针对这一空白，对基于 DRL 的动态调度进行了系统性综述，重点关注其弹性和可持续性。通过分析二十年来的文献，研究了 DRL 在动态调度中的主要应用场景，并定义了评估这些系统的弹性和可持续性的具体指标。研究结果表明，DRL 在各种生产领域都很有效，超越了传统的基于规则的算法和元启发式算法，尤其是在增强复原力方面。然而，在解决能源灵活性、资源利用和以人为本的社会影响等可持续性问题方面仍存在巨大差距。本文还探讨了当前的技术挑战，包括多目标和多代理优化，并提出了未来的研究方向，以便在基于 DRL 的动态调度中更好地整合复原力和可持续性，重点关注现实世界的应用。

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引用次数: 0

Generative deep reinforcement learning method for dynamic parallel machines scheduling with adaptive maintenance activities 针对具有自适应维护活动的动态并行机器调度的生成性深度强化学习方法

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-12 DOI: 10.1016/j.jmsy.2024.11.004

Ming Wang , Jie Zhang , Peng Zhang , Wenbin Xiang , Mengyu Jin , Hongsen Li

In the process industries, where orders arrive at irregular intervals, inappropriate maintenance frequency often leads to unplanned shutdowns of high-speed parallel machines, resulting in unnecessary material consumption and a significant decline in the performance of the dynamic parallel machines scheduling. To address this issue, this paper proposes a generative deep reinforcement learning method that investigates the dynamic parallel machines scheduling problems with adaptive maintenance activities. Specifically, an enhanced Double DQN algorithm is proposed to schedule the dynamically arriving orders and maintenance activities, aiming to maximize average reliability while minimize the production costs. Additionally, a global exploration strategy is incorporated to enhance the scheduling and maintenance agent's global exploration capability, particularly in complex solution spaces with conflicting objectives. Furthermore, recognizing the difficulty of accurately capturing crucial scheduling and maintenance attributes within a predefined state space in a time-varying production environment, a guided Actor-Critic algorithm is introduced to autonomously generate the state space. Moreover, to tackle the unstable learning process caused by sparse rewards, a self-imitation learning is employed to guide the state space generation agent toward achieving rapid learning and convergence. Finally, simulation experiments validate that the proposed method not only autonomously enables state space generation but also exhibits superior performance for the investigated problem.

在订单不定时到达的流程工业中，不恰当的维护频率往往会导致高速并联机器的意外停机，从而造成不必要的材料消耗和动态并联机器调度性能的显著下降。针对这一问题，本文提出了一种生成式深度强化学习方法，用于研究具有自适应维护活动的动态并行机调度问题。具体来说，本文提出了一种增强型双 DQN 算法来调度动态到达的订单和维护活动，旨在最大化平均可靠性，同时最小化生产成本。此外，该算法还采用了全局探索策略，以增强调度和维护代理的全局探索能力，尤其是在目标相互冲突的复杂求解空间中。此外，考虑到在时变的生产环境中很难在预定义的状态空间内准确捕捉到关键的调度和维护属性，因此引入了一种引导式行动者批判算法来自主生成状态空间。此外，为了解决奖励稀疏导致学习过程不稳定的问题，还采用了自我模仿学习法来引导状态空间生成代理实现快速学习和收敛。最后，模拟实验验证了所提出的方法不仅能自主生成状态空间，而且在所研究的问题上表现出卓越的性能。

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引用次数: 0

Enhancing manual inspection in semiconductor manufacturing with integrated augmented reality solutions 利用集成增强现实解决方案加强半导体制造中的人工检测

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-11 DOI: 10.1016/j.jmsy.2024.10.028

Chih-Hsing Chu, Chen-Yu Weng, Yu-Tzu Chen

On-site routine inspection often remains a manual operation in the semiconductor manufacturing industry because implementing automated solutions can be costly and technically challenging in such a highly controlled and complex environment. The manual inspection is prone to errors due to the impact of demanding physical and mental workloads. This paper presents an integrated Augmented Reality (AR) solution developed to assist manual inspection tasks in the supporting areas of semiconductor manufacturing, referred to as the sub-fab. The solution is accessible to a human worker wearing an AR headset during the inspection process at the location. We propose a system framework to deploy computational intelligences of varying granularity provided by the solution across cloud, edge, and device levels, accommodating constraints within the sub-fab. A machine maintenance module helps estimate and monitor the health condition of running scrubbers. Incorrect intentions performed by the worker on the scrubber control panel are detected through hand gesture recognition. This instantly prompts warning messages in the AR headset to prevent subsequent wrong actions. The solution can also identify abnormal device states through 6D pose estimation of objects enabled by machine learning models. A test scenario demonstrates how these functional features enhance the inspection efficiency and quality by reducing human workloads. This work demonstrates that semiconductor manufacturing may require AR-assisted functions different from those needed or common in other industrial sectors. It also highlights the potential of AR technology for reducing operational human errors in manual tasks.

在半导体制造业中，现场例行检查通常仍是人工操作，因为在这样一个高度受控的复杂环境中，实施自动化解决方案不仅成本高昂，而且在技术上具有挑战性。由于高强度的体力和脑力劳动的影响，人工检测很容易出错。本文介绍了一种集成的增强现实（AR）解决方案，用于辅助半导体制造辅助区域（称为子工厂）的人工检测任务。佩戴 AR 头显的人类工人可在现场检测过程中使用该解决方案。我们提出了一个系统框架，用于在云端、边缘和设备层面部署该解决方案提供的不同粒度的计算智能，以适应子工厂内的各种限制。机器维护模块有助于估计和监控运行中的洗涤器的健康状况。通过手势识别，可检测到工人在洗地机控制面板上执行的不正确意图。这会立即在 AR 头显中提示警告信息，以防止后续的错误操作。该解决方案还可以通过机器学习模型对物体进行 6D 姿态估计，识别异常设备状态。一个测试场景演示了这些功能特性如何通过减少人工工作量来提高检测效率和质量。这项工作表明，半导体制造所需的 AR 辅助功能可能不同于其他工业部门所需或常见的功能。它还凸显了 AR 技术在减少人工任务中人为操作失误方面的潜力。

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引用次数: 0

A new cause-mechanism independence estimation based cross-domain learning method for machining deformation prediction 一种基于原因机制独立性估计的新型跨域学习方法，用于加工变形预测

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-09 DOI: 10.1016/j.jmsy.2024.11.002

Yang Ni , Yingguang Li , Changqing Liu , Xu Liu

Monitoring data-based machining deformation prediction is fundamental for accurate deformation control and product quality guarantee. For problems where involved unobservable variables like residual stress that can lead to data distribution bias, causal cross-domain learning methods have prominent advantages over other pure data-driven methods by shifting cause distributions and mechanisms. However, existing causal methods are based on the hypothesis that cause and mechanism are independent, which ignores the corresponding changes of mechanism across domains and can limit accuracies. This paper proposes a new causal cross-domain learning method based on cause-mechanism independence estimation, where the hypothesis is broken by taking the dependence of cause and mechanism into consideration. A cause-mechanism independence estimator is established by introducing the structural integral of mechanism derivative multiplies cause distribution, and the estimation value can measure the cross-domain changes of mechanism. As a result, the proposed method based predicting model can make efficient distribution shifts according to the estimation. The machining of aero-engine casings is taken as a case study, and experimental results show that the proposed method could predict the deformation well with limited target domain data. Besides, the proposed method can be readily extended to other cross-domain regression problems involved with unobservable variables.

基于数据监测的加工变形预测是精确控制变形和保证产品质量的基础。对于涉及残余应力等不可观测变量、可能导致数据分布偏差的问题，跨域因果学习方法通过转移原因分布和机制，与其他纯数据驱动方法相比具有突出优势。然而，现有的因果学习方法都是基于原因和机制相互独立的假设，忽略了机制在不同领域间的相应变化，会限制学习的准确性。本文提出了一种基于原因-机制独立性估计的新型因果跨域学习方法，该方法通过考虑原因和机制的依赖性打破了这一假设。通过引入机制导数乘以原因分布的结构积分，建立了原因-机制独立性估计器，其估计值可以衡量机制的跨域变化。因此，所提出的基于预测模型的方法可以根据估计值进行有效的分布转移。以航空发动机壳体的加工为例，实验结果表明所提出的方法能在有限的目标域数据下很好地预测变形。此外，提出的方法还可扩展到其他涉及不可观测变量的跨域回归问题。

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引用次数: 0

Simulation-based Digital Twin for enhancing human-robot collaboration in assembly systems 基于仿真的数字孪生系统，用于加强装配系统中的人机协作

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-09 DOI: 10.1016/j.jmsy.2024.10.024

Antonio Cimino , Francesco Longo , Letizia Nicoletti , Vittorio Solina

The advent of new technologies and paradigms such as the Internet of Things (IoTs), Digital Twin (DT), Human-Robot Collaboration (HRC), is offering immense opportunities to improve the performance of manufacturing systems, but also opening new challenges. The current scientific literature highlights the presence of numerous theoretical studies, but limited real-life applications, and the need to address interoperability issues, with the aim of valorizing the data continuously generated by humans, robots, machines. This research presents a novel simulation-based DT, designed for supporting HRC optimization in assembly systems. The proposed approach is tested and validated, through a case study in the automotive sector, specifically focusing on an assembly line for car front doors. The results show that it is possible to achieve HRC improvements through the assessment of different working configurations. Furthermore, it is explained how the simulation-based DT, by leveraging the FIWARE/FIROS paradigm, can effectively and efficiently interact with other systems, to enable real-time data exchange, which is nowadays one of the main open research challenges.

物联网（IoTs）、数字孪生（DT）、人机协作（HRC）等新技术和新模式的出现，为提高制造系统的性能提供了巨大的机遇，同时也带来了新的挑战。目前的科学文献强调了大量理论研究的存在，但现实生活中的应用却很有限，而且需要解决互操作性问题，目的是使人类、机器人和机器不断产生的数据发挥价值。本研究提出了一种新颖的基于模拟的 DT，旨在支持装配系统中的热轧卷优化。通过对汽车行业的案例研究，特别是对汽车前门装配线的研究，对所提出的方法进行了测试和验证。结果表明，通过评估不同的工作配置，可以实现 HRC 的改进。此外，还解释了基于仿真的 DT 如何利用 FIWARE/FIROS 范式，有效地与其他系统进行交互，从而实现实时数据交换，这也是当今主要的开放式研究挑战之一。

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引用次数: 0

LogicLSTM: Logically-driven long short-term memory model for fault diagnosis in gearboxes LogicLSTM：用于齿轮箱故障诊断的逻辑驱动长短期记忆模型

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-06 DOI: 10.1016/j.jmsy.2024.10.003

Eduard Hogea , Darian M. Onchiş , Ruqiang Yan , Zheng Zhou

This article introduces LogicLSTM, a hybrid neuro-symbolic model obtained by logically guiding a pretrained Long Short-Term Memory (LSTM) network with the support of a customized Logic Tensor Network (LTN). The model is further optimized by explainable AI techniques, for a refined fault classification of time-series data coming from industrial gearboxes. The framework leverages the intrinsic strengths of LSTMs deep recurrent networks for temporal data processing with logical reasoning capabilities, to improve prediction accuracy and interpretability of the classification. Our approach addresses the challenges of extracting relevant data features and integrating connectionist and symbolic methodologies to form a cohesive predictive model. Results from extensive testing show that our model significantly outperforms traditional LSTM models, particularly in complex fault scenarios where conventional methods may fail. Specifically, the hybrid model demonstrates a 16.03% average improvement in accuracy over standard LSTM models under conditions of sufficient data availability, and a 8.56% improvement in scenarios where data is scarce. This research not only demonstrates the potential of hybrid models in industrial applications but also highlights the importance of explainability in AI systems for critical decision-making processes. The proposed model’s ability to interpret and explain its predictions makes it a valuable tool for advancing predictive maintenance strategies within the Industry 4.0 framework.

本文介绍的 LogicLSTM 是一种混合神经符号模型，它是在定制的逻辑张量网络（LTN）的支持下，通过逻辑引导预训练的长短期记忆（LSTM）网络而获得的。该模型通过可解释人工智能技术进一步优化，可对来自工业齿轮箱的时间序列数据进行精细故障分类。该框架利用 LSTMs 深度递归网络在时间数据处理方面的固有优势和逻辑推理能力，提高了预测的准确性和分类的可解释性。我们的方法解决了提取相关数据特征、整合连接主义和符号方法以形成一个内聚预测模型的难题。大量测试结果表明，我们的模型明显优于传统的 LSTM 模型，尤其是在传统方法可能失效的复杂故障场景中。具体来说，在数据充足的条件下，混合模型比标准 LSTM 模型的平均准确率提高了 16.03%，而在数据稀缺的情况下，平均准确率提高了 8.56%。这项研究不仅证明了混合模型在工业应用中的潜力，还强调了人工智能系统在关键决策过程中可解释性的重要性。所提出的模型能够解释和说明其预测结果，这使其成为在工业 4.0 框架内推进预测性维护策略的重要工具。

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引用次数: 0

Towards data-driven quality monitoring for advanced metal inert gas welding processes in body-in-white 对先进的白车身金属惰性气体焊接工艺进行数据驱动的质量监测

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-05 DOI: 10.1016/j.jmsy.2024.10.013

Michael Luttmer , Matthias Weigold , Heiko Thaler , Jürgen Dongus , Anton Hopf

In recent years, numerous monitoring approaches have been developed in the field of intelligent welding manufacturing to predict quality-related characteristics using process data and artificial intelligence-based techniques. While most investigations have focused on welding steel with conventional gas metal arc welding processes, the welding of aluminum and its alloys using advanced process variants has been less explored. This work addresses this gap by investigating data-driven methods for fault diagnosis and detection in an advanced metal inert gas welding process commonly used in body-in-white manufacturing. To this end, electrical, acoustic, and spectroscopic signals were recorded from numerous welding tests simulating typical fault causes. Various predictive models, ranging from traditional machine learning algorithms to state-of-the-art deep learning techniques, were trained and evaluated for classifying faulty seams and identifying their root causes. The results demonstrate that combining sensor data enhances the performance of predictive models compared to using individual sensors alone. However, a deep learning approach based solely on electrical signals emerged as the best solution for both use cases, considering both the results and practical aspects. Overall, the experiments highlight the significant potential of data-driven techniques to enhance quality monitoring in advanced MIG welding processes, promoting their more widespread adoption in body-in-white manufacturing.

近年来，在智能焊接制造领域开发了许多监测方法，利用过程数据和基于人工智能的技术预测与质量相关的特性。大多数研究都集中在使用传统气体金属弧焊工艺焊接钢材方面，而对使用先进工艺变体焊接铝及其合金的研究则较少。本研究针对这一空白，研究了白车身制造中常用的先进金属惰性气体焊接工艺的故障诊断和检测的数据驱动方法。为此，从模拟典型故障原因的大量焊接测试中记录了电气、声学和光谱信号。从传统的机器学习算法到最先进的深度学习技术，对各种预测模型进行了训练和评估，以对故障焊缝进行分类并确定其根本原因。结果表明，与单独使用单个传感器相比，结合传感器数据可提高预测模型的性能。不过，考虑到结果和实际情况，仅基于电信号的深度学习方法成为这两种使用情况下的最佳解决方案。总之，实验凸显了数据驱动技术在加强先进 MIG 焊接工艺质量监控方面的巨大潜力，从而促进了其在白车身制造中的更广泛应用。

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引用次数: 0

Joint production, maintenance, and quality control in manufacturing systems with imperfect inspection 不完善检验制造系统中的联合生产、维护和质量控制

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2024-11-04 DOI: 10.1016/j.jmsy.2024.10.020

Abdessamad Ait El Cadi , Ali Gharbi , Karem Dhouib , Abdelhakim Artiba

This paper proposed a joint production control, preventive maintenance, and inspection policy for manufacturing systems prone to failures, quality degradation and quality inspection errors. A stochastic mathematical model is developed taking into account all possible scenarios contingent to imperfect quality inspection errors, while integrating age-based preventive maintenance, dynamic production rates, and sampling inspection plans. The model accounts for both Type I and Type II inspection errors and optimizes the joint policy key parameters, including safety stock levels, preventive maintenance thresholds, and inspection sample size. The model is validated using a 95 % confidence interval obtained from experiments with simulation model that imitates the studied system dynamics when it is controlled by the proposed joint policy. A sensitivity analysis is carried out to give a deeper comprehension of the problem and the complex interactions at play. The study explores the impact of system’s parameters on the new joint policy that accounts for inspection errors, thereby contributing valuable insights to the field of manufacturing systems management. Ultimately, a comprehensive comparative analysis seeks to establish the superiority of the proposed joint policy over existing ones documented in the literature. The proposed policy consistently outperformed alternative approaches, with an overall cost reduction of up to 87 %.

本文针对容易发生故障、质量下降和质量检验错误的制造系统，提出了一种生产控制、预防性维护和检验联合政策。该论文建立了一个随机数学模型，考虑了所有可能出现的质量检验误差不完善的情况，同时整合了基于年龄的预防性维护、动态生产率和抽样检验计划。该模型考虑了 I 类和 II 类检验误差，并优化了联合政策的关键参数，包括安全库存水平、预防性维护阈值和检验样本量。该模型通过仿真模型实验获得 95% 的置信区间进行验证，仿真模型模仿了所研究的系统动态，并由建议的联合政策进行控制。为了更深入地理解问题和复杂的相互作用，还进行了敏感性分析。研究探讨了系统参数对考虑检测误差的新联合策略的影响，从而为制造系统管理领域提供了宝贵的见解。最后，通过全面的比较分析，力求确定所提出的联合政策优于文献中记载的现有政策。所提出的政策始终优于其他方法，总体成本降低高达 87%。

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