Journal of Manufacturing Systems最新文献

An improved large language model and knowledge graph integration method for automated machining process base construction 一种改进的大语言模型与知识图集成的自动化加工过程库构建方法

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.jmsy.2026.01.016

Fu Yan , Jie Liu , Liang Guo , Li Liu , XiangYu Geng

The Machining Process Knowledge Base (MPKB) is foundational to intelligent process decision-making, directly impacting manufacturing efficiency and quality. While Large Language Models (LLMs) have shown promise in automated MPKB construction, they face a critical challenge in manufacturing: industrial knowledge graph (KG) schemas often exceed the context windows of lightweight LLMs deployable by small and medium-sized enterprises (SMEs). This limitation forces the construction process to operate with incomplete schema information, leading to missed entity relationships, semantic heterogeneity, and conceptual ambiguities in the MPKB. This study proposes an improved LLM-KG collaborative framework that overcomes these limitations through: (1) employing a staged, schema-decoupled extraction strategy, which enables open triple collection without injecting the full schema; (2) introducing a Code-Style knowledge representation method that efficiently encodes complex machining schemas, reducing the semantic load while maintaining structural integrity; and (3) constructing a retrieval-driven pipeline for semantic standardization that integrates dynamic schema segmentation and bidirectional validation, utilizing LLMs to achieve interpretable synonym merging and eliminate heterogeneity. This study empirically validated the proposed approach using machining process data provided by an aviation enterprise. Experimental results demonstrate that our framework achieves at least a 3.3% improvement in MPKB construction quality and a 25% increase in machining process quality metrics compared to the other baseline models. The implementation and data have been made available on GitHub to facilitate reproducibility and further research.

加工过程知识库（MPKB）是智能工艺决策的基础，直接影响到制造效率和质量。虽然大型语言模型（llm）在自动化MPKB构建中表现出了希望，但它们在制造业中面临着一个关键挑战：工业知识图（KG）模式通常超过了中小型企业（sme）可部署的轻量级llm的上下文窗口。这种限制迫使构造过程使用不完整的模式信息进行操作，从而导致MPKB中的实体关系缺失、语义异构和概念模糊。本研究提出了一种改进的LLM-KG协作框架，通过以下方式克服了这些限制：(1)采用分阶段、模式解耦的提取策略，在不注入完整模式的情况下实现开放的三重收集；(2)引入编码式知识表示方法，对复杂的加工模式进行高效编码，在保持结构完整性的同时减少语义负荷；(3)构建检索驱动的语义标准化管道，集成动态模式分割和双向验证，利用llm实现可解释同义词合并，消除异构性。利用某航空企业的加工工艺数据对该方法进行了实证验证。实验结果表明，与其他基准模型相比，我们的框架在MPKB构造质量方面至少提高了3.3%，在加工过程质量指标方面提高了25%。实现和数据已在GitHub上提供，以促进可重复性和进一步的研究。

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引用次数: 0

Hybrid digital twins for smart manufacturing: Architectures, fusion paradigm, and implementation challenges 智能制造的混合数字孪生：架构、融合范式和实施挑战

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-09 DOI: 10.1016/j.jmsy.2025.12.029

Xi Zhang , Yiqun Kou , Xin Zhang , Qi Shi , Youmin Hu , Huapeng Wu , Shimin Liu , Pai Zheng

As a high-fidelity representation of physical objects, the digital twin (DT) emerges as a crucial enabling tool supporting intelligent monitoring, prediction, and decision-making for smart manufacturing. To achieve reliable, accurate, and explainable DT modeling under dynamic conditions, it is necessary to integrate multiple models, including first-principles knowledge, data-driven algorithms, and simulation. Furthermore, with the emergence of state-of-the-art artificial intelligence (AI) technologies, such as Generative AI and Large Language Models, new drivers for DT modeling can be provided. However, the specific paradigm for hybridizing these models varies significantly depending on the application scenario, the object, and the critical requirements. This diversity poses a significant challenge for systematically selecting and combining modeling techniques in smart manufacturing. This review addresses this gap by providing a systematic exploration of the Hybrid Digital Twin (HDT) modeling paradigm, which focuses on the integration of multiple heterogeneous models. Therefore, this paper aims to: (1) clarify the architecture and core characteristics of HDT; (2) categorize critical technologies and fusion paradigms for HDT implementation; and (3) outline potential future research directions. It is hoped that this paper will serve as a systematic reference for researchers and engineers seeking to apply HDT to build more accurate, reliable, and adaptive DT applications.

作为物理对象的高保真表示，数字孪生（DT）成为支持智能制造智能监控、预测和决策的关键支持工具。为了在动态条件下实现可靠、准确和可解释的DT建模，需要集成多个模型，包括第一性原理知识、数据驱动算法和仿真。此外，随着最先进的人工智能（AI）技术的出现，如生成式AI和大型语言模型，可以为DT建模提供新的驱动因素。然而，混合这些模型的具体范例根据应用程序场景、对象和关键需求而有很大的不同。这种多样性对智能制造中建模技术的系统选择和组合提出了重大挑战。本文通过对混合数字孪生（HDT）建模范式的系统探索来解决这一差距，该范式侧重于多个异构模型的集成。因此，本文旨在：(1)阐明HDT的体系结构和核心特征；(2)对HDT实施的关键技术和融合范式进行分类；(3)概述了未来可能的研究方向。希望本文能够为寻求应用HDT构建更准确、可靠和自适应的DT应用的研究人员和工程师提供系统的参考。

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引用次数: 0

Spatial information bottleneck graph structure learning based multivariate time series prediction for industrial processes 基于空间信息瓶颈图结构学习的工业过程多变量时间序列预测

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1016/j.jmsy.2026.01.017

Xun Shi , Kuangrong Hao , Xianyi Zeng , Lei Chen , Haijian Li

Prediction-based graph structure learning enhances both prediction accuracy and interpretability by identifying the underlying causes of prediction fluctuations, making it particularly valuable for industrial process monitoring. However, industrial data often exhibit strong spatio-temporal heterogeneity due to the presence of diverse physical measurements and redundant sensor placements, posing significant challenges for effective graph structure learning. Furthermore, when increasing the look-back window length to improve prediction accuracy, the heterogeneity of time series introduces more noise, making it difficult for graph structure learning methods to establish effective edge connections. Meanwhile, homogeneous time series provide redundant spatial features, causing prediction-based graph structure learning methods to fail. This paper is the first to study how to control the learned graph structure density in a multivariate time series prediction model to achieve a reasonable balance between prediction accuracy and structural accuracy. This paper proposes a Spatial Information Bottleneck (SIB) method to simultaneously address the aforementioned two challenges. The SIB method introduces the spatial feature prioritization principle, whereby the prediction model preferentially utilizes neighborhood node features for forecasting in homogeneous time series pairs, thereby enabling graph structure learning to establish connections between homogeneous time series pairs. Second, SIB performs independent information compression on each time series feature, which suppresses prediction-irrelevant noise in heterogeneous time series to varying degrees, thereby mitigating the impact of noise on prediction accuracy under long-sequence inputs. Experiments on industrial process data with accessible ground truth graph structures show that the model based on this method not only enhances prediction accuracy but also generates graph structures that align with physical processes for result interpretation.

基于预测的图结构学习通过识别预测波动的潜在原因来提高预测的准确性和可解释性，使其对工业过程监控特别有价值。然而，由于存在不同的物理测量和冗余的传感器位置，工业数据往往表现出强烈的时空异质性，这对有效的图结构学习构成了重大挑战。此外，当增加回看窗口长度以提高预测精度时，时间序列的异质性引入了更多的噪声，使得图结构学习方法难以建立有效的边缘连接。同时，齐次时间序列提供了冗余的空间特征，导致基于预测的图结构学习方法失败。本文首次研究了如何在多元时间序列预测模型中控制学习到的图的结构密度，以达到预测精度和结构精度之间的合理平衡。本文提出了一种空间信息瓶颈（SIB）方法来同时解决上述两个挑战。SIB方法引入空间特征优先原则，预测模型优先利用邻域节点特征对同构时间序列对进行预测，从而实现图结构学习，建立同构时间序列对之间的联系。其次，SIB对每个时间序列特征进行独立的信息压缩，不同程度地抑制了异构时间序列中与预测无关的噪声，从而减轻了长序列输入下噪声对预测精度的影响。对具有可接近地真图结构的工业过程数据的实验表明，基于该方法的模型不仅提高了预测精度，而且生成了与物理过程一致的图结构，便于结果解释。

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引用次数: 0

Explicating visual tacit knowledge in industrial welding inspection with context-aware cognitive pathway graph 用情境感知认知路径图解释工业焊接检测中的视觉隐性知识

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-02-06 DOI: 10.1016/j.jmsy.2026.02.001

Ji Wang, Weibin Zhuang, Xing Wu, Congmao Chen, Jinsong Bao, Xinyu Li

The profound reliance of industrial smart manufacturing on human expert experience has emerged as a critical bottleneck, as traditional methods struggle to effectively computationalize the deep, contextualized tacit knowledge inherent in expert visual intuition. To address this challenge, this paper proposes a systematic methodology for the explicitation and contextualized modeling of expert Visual Tacit Knowledge. First, to address the foundational challenge of formalizing expert intuition, this work defines Visual Tacit Knowledge and its transformation pathway from tacit intuition to explicit rules, and introduces Weld-VTK, a multimodal dataset for welding inspection that provides a solid data foundation. Second, an explicit analysis method is proposed to distill structured attention cues from unstructured raw visual behavior, providing the critical structured input needed for establishing contextual associations. Finally, to model the expert’s cognitive process, a Visual Chain-of-Thought is introduced, leveraging Large Language Models to establish contextual semantic associations between cues. These chains are then aggregated to construct a hierarchical Context-Aware Cognitive Pathway Graph, completely reconstructing the expert’s cognitive strategy. Quantitative results demonstrate that the proposed method outperforms baseline models, and expert evaluations confirm its exceptional performance in causal validity and diagnostic precision. This methodology provides a new paradigm for the contextualized modeling and structured explicitation of expert Visual Tacit Knowledge.

工业智能制造对人类专家经验的高度依赖已成为一个关键瓶颈，因为传统方法难以有效地计算专家视觉直觉中固有的深度、情境化隐性知识。为了解决这一挑战，本文提出了一种系统的可视化隐性知识的描述和情境化建模方法。首先，为了解决专家直觉形式化的基础挑战，本工作定义了视觉隐性知识及其从隐性直觉到显式规则的转换途径，并引入了用于焊接检测的多模态数据集Weld-VTK，该数据集提供了坚实的数据基础。其次，提出了一种明确的分析方法，从非结构化的原始视觉行为中提取结构化的注意线索，为建立上下文关联提供关键的结构化输入。最后，为了模拟专家的认知过程，引入了视觉思维链，利用大型语言模型来建立线索之间的上下文语义关联。然后将这些链聚合成一个分层的上下文感知认知路径图，完全重建专家的认知策略。定量结果表明，该方法优于基线模型，专家评价证实了其在因果效度和诊断精度方面的卓越性能。该方法为专家视觉隐性知识的情境化建模和结构化表达提供了一种新的范式。

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引用次数: 0

Integrating data and domain knowledge for predictive intelligence: A comprehensive review of DKF-DPM in intelligent manufacturing 集成数据和领域知识用于预测智能：智能制造中的DKF-DPM综述

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-02-04 DOI: 10.1016/j.jmsy.2026.02.003

Zheng Ren , Yutao Chen , Zihao Zhu , Linhuhu Nong , Wenyu Yang , Junyong Qiu , Tianhua Ling

Data and knowledge fusion-driven predictive model (DKF-DPM) has garnered significant attention for their ability to achieve high accuracy and robustness in complex manufacturing scenarios. By integrating data-driven learning with physical and domain knowledge, DKF-DPM is capable of more reliable modeling and prediction of nonlinear, multi-source and highly uncertain processes. This review systematically surveys recent advances of DKF-DPM in intelligent manufacturing, focusing on their modeling frameworks, representative applications in failure and fatigue life, cutting force and residual stress, machining quality and optimal processing parameters. In addition, current limitations and future research directions are discussed to highlight key challenges and opportunities. Overall, this study concludes that the integration of data-driven methods with domain knowledge is a critical pathway toward developing more reliable, interpretable, and adaptive predictive systems for intelligent manufacturing.

数据和知识融合驱动的预测模型（DKF-DPM）因其在复杂制造场景中实现高精度和鲁棒性的能力而受到广泛关注。通过将数据驱动学习与物理和领域知识相结合，DKF-DPM能够对非线性、多源和高度不确定的过程进行更可靠的建模和预测。本文系统综述了DKF-DPM在智能制造中的最新进展，重点介绍了DKF-DPM的建模框架、失效和疲劳寿命、切削力和残余应力、加工质量和最佳加工参数等方面的代表性应用。此外，讨论了当前的局限性和未来的研究方向，以突出关键的挑战和机遇。总体而言，本研究得出结论，将数据驱动方法与领域知识相结合是开发更可靠、可解释和自适应的智能制造预测系统的关键途径。

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引用次数: 0

A comprehensive framework for computationally efficient system-level design optimization of machine tools 一个计算效率高的机床系统级设计优化的综合框架

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-02-10 DOI: 10.1016/j.jmsy.2026.02.005

Deniz Bilgili , Erhan Budak , Jasmin Jelovica

Mass reduction of machine tool components is a crucial task that can improve performance, accuracy, and energy efficiency. System-level optimization, where multiple components are simultaneously optimized, is noted in the literature as a challenging necessity for complete performance improvement of machine tools. Existing methods focus on optimizing the machine tool components individually, neglecting the critical effects of simultaneous modification on the machine performance and thorough exploration of the design space. To the authors’ knowledge, for the first time in the literature, this paper presents a comprehensive framework for system-level machine tool design optimization considering the most significant multi-objective performance indicators for the machining process. Static and dynamic stiffness, thermal and dynamic stability, and fatigue life are evaluated as performance indicators using a multi-objective finite element response set that includes coupled thermal-structural, modal, and frequency response analyses. A minimal parameter set approach is proposed which uses the linear guide joints to minimize the number of design variables, addressing the challenge of increased computational cost in system-level modeling. Machine responses during optimization iterations are predicted by a machine learning model trained on the machine tool’s multi-objective finite element response set, achieving higher accuracy than commonly used polynomial-based methods. A constraint relaxation method is proposed that permits limited degradation relative to the base design, yielding designs that substantially outperform those obtained from unconstrained optimization while avoiding over-constraining. Up to 20 % mass reduction is achieved across the machine tool components while the performance indicators are either improved or maintained with negligible degradation.

减少机床部件的质量是一项至关重要的任务，可以提高性能，精度和能源效率。系统级优化，其中多个组件同时优化，在文献中被指出为机床的完全性能改进的一个具有挑战性的必要性。现有方法侧重于对机床部件进行单独优化，忽略了同时修改对机床性能的关键影响和对设计空间的深入探索。据作者所知，在文献中，本文首次提出了考虑加工过程中最重要的多目标性能指标的系统级机床设计优化的综合框架。静态和动态刚度、热稳定性和动态稳定性以及疲劳寿命作为性能指标进行评估，使用多目标有限元响应集，包括耦合热结构、模态和频率响应分析。针对系统级建模中计算成本增加的问题，提出了一种最小参数集方法，该方法利用线性导轨关节来最小化设计变量的数量。优化迭代过程中的机器响应通过在机床多目标有限元响应集上训练的机器学习模型进行预测，比常用的基于多项式的方法具有更高的精度。提出了一种约束松弛方法，允许相对于基本设计的有限退化，产生的设计在避免过度约束的同时大大优于无约束优化获得的设计。整个机床部件的质量减少高达20% %，同时性能指标得到改善或保持在可忽略不计的退化。

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引用次数: 0

Real-time dynamic integrated process planning and scheduling with reconfigurable manufacturing cells via multi-agent reinforcement learning 基于多智能体强化学习的可重构制造单元实时动态集成工艺规划与调度

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-13 DOI: 10.1016/j.jmsy.2026.01.004

Liang Zheng , Xiaodi Chen , Jianhua Liu , Cunbo Zhuang

Amid the transformation driven by Industry 4.0 and 5.0, manufacturing is rapidly advancing toward greater intelligence and flexibility. Reconfigurable Matrix-structured Manufacturing Systems (RMMS) improve adaptability through dynamic structural and resource reconfiguration, while Integrated Process Planning and Scheduling (IPPS) jointly optimizes process routes and scheduling for optimal resource allocation and responsiveness. This study focuses on Dynamic IPPS with Reconfigurable Manufacturing Cells (DIPPS-RMC) in RMMS, and proposes a real-time scheduling approach based on multi-agent Proximal Policy Optimization (PPO) to reduce average tardiness and enhance system efficiency. A Mixed Integer Linear Programming model is established to address the complexity of process flows and dynamic scheduling, providing a solid theoretical foundation. The scheduling problem is further formulated as a Partially Observable Markov Decision Process to capture the uncertainty and partial observability of real manufacturing environments. To alleviate the credit assignment problem and enhance inter-agent coordination, a delayed reward-sharing mechanism is designed. A multi-agent PPO algorithm with centralized training and decentralized execution is introduced, leveraging parallel environment sampling to improve training efficiency and generalization. Extensive experiments on 270 cases across 27 scenarios show that the proposed method outperforms state-of-the-art multi-agent reinforcement learning algorithms in training speed, generalization, and scheduling performance. Its application to real-world cases further demonstrates effective handling of dynamic job arrivals and RMC breakdowns, validating its robustness and practical utility. These results confirm the method’s effectiveness and applicability in dynamic, complex manufacturing environments, offering an innovative solution for real-time scheduling in RMMS.

在工业4.0和工业5.0的推动下，制造业正迅速向更智能和更灵活的方向发展。可重构矩阵结构制造系统（RMMS）通过动态结构重构和资源重构来提高系统的适应性，而集成工艺规划与调度（IPPS）则通过联合优化工艺路线和调度来实现资源的最优分配和响应。针对RMMS中具有可重构制造单元的动态IPPS (DIPPS-RMC)，提出了一种基于多智能体近端策略优化（PPO）的实时调度方法，以降低平均延迟，提高系统效率。针对复杂的工艺流程和动态调度问题，建立了混合整数线性规划模型，提供了坚实的理论基础。将调度问题进一步表述为部分可观察马尔可夫决策过程，以捕捉真实制造环境的不确定性和部分可观察性。为了缓解信用分配问题，增强代理间的协调能力，设计了一种延迟奖励共享机制。提出了一种集中训练、分散执行的多智能体PPO算法，利用并行环境采样来提高训练效率和泛化能力。在27个场景的270个案例中进行的大量实验表明，所提出的方法在训练速度、泛化和调度性能方面优于最先进的多智能体强化学习算法。它在实际案例中的应用进一步证明了动态工作到达和RMC故障的有效处理，验证了它的鲁棒性和实用性。这些结果证实了该方法在动态、复杂制造环境中的有效性和适用性，为RMMS的实时调度提供了一种创新的解决方案。

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引用次数: 0

The SHOP4CF modular reference architecture for flexible process-oriented, data-driven smart manufacturing 面向灵活流程、数据驱动的智能制造的SHOP4CF模块化参考架构

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.jmsy.2026.01.010

Paul Grefen , Michał Zimniewicz , Irene Vanderfeesten , Kostas Traganos , Pieter Becue , Anders Pedersen , Genessis Perez Rivera

Context

Organizations in the smart industry domain face an increasing complexity of their functions and processes, both in the intra- and inter-organizational scopes. This has a direct effect on the digital systems that support their operations: they grow more complex too. At the same time, the organizations need to increase their agility: they must be able to flexibly adapt their processes to market changes. Especially SMEs in the manufacturing domain get lost in this combination of complexity and changeability.

Objectives

To help SME organizations in the smart manufacturing domain with their digital transformation, we develop the SHOP4CF modular reference architecture for digital manufacturing support.

Methods

We develop the SHOP4CF base architecture in an iterative way by application and evaluation in 36 real-world industrial cases, organized in three waves. We base our design partly on successful existing work, specifically the outcomes of the HORSE EU project, and align it with main manufacturing standards like ISA-95 and RAMI 4.0. We next distill the SHOP4CF reference architecture by abstracting the SHOP4CF base architecture, based on explicit design principles. We then specialize the reference architecture for process-oriented and data-driven manufacturing.

Results

The result of our work is a modular, flexible software reference architecture for smart manufacturing solutions. To facilitate its use, the reference architecture is coupled with manufacturing software life cycle models. Centered on a component marketplace, the life cycle for functional module developers is linked to the life cycle for module users, including explicit attention to the role of technology integrators. To illustrate its applicability, we describe three application cases in this paper.

Conclusion

The reference architecture provides a demonstrated point of departure for SMEs in the manufacturing domain to design their digital support in a complex and dynamic industry ecosystem. The modularity of the architecture and its coupling to software life cycles provide a new level of flexibility.

智能工业领域的组织在组织内部和组织间都面临着越来越复杂的功能和流程。这对支持它们运作的数字系统产生了直接影响：它们也变得更加复杂。同时，组织需要增加他们的敏捷性：他们必须能够灵活地调整他们的过程以适应市场变化。尤其是制造业领域的中小企业，在这种复杂性和可变性的组合中迷失了方向。为了帮助智能制造领域的中小企业组织进行数字化转型，我们开发了用于数字化制造支持的SHOP4CF模块化参考架构。方法通过对36个实际工业案例的应用和评估，以迭代的方式开发SHOP4CF基础架构。我们的设计部分基于成功的现有工作，特别是HORSE欧盟项目的成果，并使其与ISA-95和RAMI 4.0等主要制造标准保持一致。接下来，我们根据显式设计原则，通过抽象SHOP4CF基本体系结构，提炼出SHOP4CF参考体系结构。然后，我们专门研究面向过程和数据驱动制造的参考体系结构。我们的工作成果是智能制造解决方案的模块化、灵活的软件参考架构。为了便于使用，参考体系结构与制造软件生命周期模型相结合。以组件市场为中心，功能模块开发人员的生命周期与模块用户的生命周期相关联，包括对技术集成商角色的明确关注。为了说明其适用性，本文描述了三个应用案例。该参考架构为制造业领域的中小企业在复杂而动态的行业生态系统中设计其数字支持提供了一个示范出发点。体系结构的模块化及其与软件生命周期的耦合提供了新的灵活性级别。

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引用次数: 0

A4PS: Agentic AI-assisted advanced planning and scheduling with large language models for smart manufacturing A4PS：智能制造大语言模型，人工智能辅助高级规划调度

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.jmsy.2026.01.003

Mingxing Li , Qu Zhou , Wanshan Li , Ting Qu , Maolin Yang , Pingyu Jiang

Advanced Planning and Scheduling (APS) for manufacturing systems is becoming more complex against the backdrop of intelligent transformation and increasing demand for customisation. In real-world APS applications subject to multi-source dynamics, objective alterations, constraints removals/additions, algorithm upgrades are inevitable. Such structural changes of APS, requiring seamless coordination among experts such as production managers, modelling engineers, algorithm developers, are often lengthy and less flexible. This poses new challenges in cross-domain/inter-process coordination and rapid multi-disciplinary knowledge integration/reuse. This paper proposes a novel Agentic AI-Assisted APS (A⁴PS) framework, utilising Large Language Models (LLMs) and agents to assist modification/update processes of APS. Firstly, a multi-agentic AI-enabled workflow is designed following standard operating procedure of APS to facilitate the cross-domain agent coordination. Secondly, a multi-step knowledge augmentation method is proposed to endow LLM agents with specialised APS knowledge. Thirdly, a Retrieval-Augmented Generation (RAG) and Chain of Thought (CoT)-enhanced method is developed for knowledge use and interaction. Experiments are conducted with an APS dataset which is created based on classical APS cases and manufacturing researchers. Compared with basic LLMs, A⁴PS exhibited substantially superior performance across both basic and complex cases in metrics such as modelling task success rate, absolute percentage error of solution results, optimisation algorithm code logic completion rate, and code executability rate. Case study demonstrates that A⁴PS enables LLMs to coordinate, learn APS knowledge, and imitate experts in the reasoning process, and ultimately realise APS assistance using natural language. This work proposes a novel solution that uses LLMs and agentic AI to assist APS modification/update process, contributing to AI-driven smart manufacturing in Industry 4.0.

在智能转型和不断增长的定制需求的背景下，制造系统的高级计划和调度（APS）变得越来越复杂。在现实世界的APS应用中，受制于多源动态、目标改变、约束移除/添加、算法升级是不可避免的。APS的这种结构变化，需要生产经理、建模工程师、算法开发人员等专家之间的无缝协调，通常是冗长而不灵活的。这对跨领域/跨过程的协调和多学科知识的快速集成/重用提出了新的挑战。本文提出了一种新的代理人工智能辅助APS （A4PS）框架，利用大型语言模型（LLMs）和代理来辅助APS的修改/更新过程。首先，根据APS的标准操作流程，设计了一个支持ai的多代理工作流，以促进跨域代理的协调。其次，提出了一种多步知识增强方法，赋予LLM agent专门的APS知识。第三，提出了一种检索增强生成（RAG）和思维链（CoT）增强的知识使用和交互方法。基于经典APS案例和制造业研究人员创建的APS数据集进行了实验。与基本llm相比，A4PS在基本和复杂情况下都表现出明显更好的性能，例如建模任务成功率、解决结果的绝对错误率、优化算法代码逻辑完成率和代码可执行率。案例研究表明，A4PS使llm能够协调、学习APS知识，并在推理过程中模仿专家，最终实现使用自然语言的APS辅助。本研究提出了一种新颖的解决方案，使用llm和人工智能代理来协助APS修改/更新过程，为工业4.0中人工智能驱动的智能制造做出贡献。

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引用次数: 0

A collaborative process parameter recommender system for fleets of networked manufacturing machines — with application to 3D printing 一个协作过程参数推荐系统的车队网络化制造机器-应用于3D打印

IF 14.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2026-04-01 Epub Date: 2026-01-02 DOI: 10.1016/j.jmsy.2025.12.028

Sicong Guo , Weishi Wang , Chenhuan Jiang , Mohamed Elidrisi , Myungjin Lee , Harsha V. Madhyastha , Raed Al Kontar , Chinedum E. Okwudire

Fleets of networked manufacturing machines of the same type, that are collocated or geographically distributed, are growing in popularity. An excellent example is the rise of 3D print farms, which consist of multiple networked 3D printers operating in parallel, enabling faster production and efficient mass customization. However, optimizing process parameters across a fleet of manufacturing machines, even of the same type, remains a challenge due to machine-to-machine variability. Traditional trial-and-error approaches are inefficient, requiring extensive testing to determine optimal process parameters for an entire fleet. In this work, we introduce a machine learning-based collaborative recommender system that optimizes process parameters for each machine in a fleet by modeling the problem as a sequential matrix completion task. Our approach leverages spectral clustering and alternating least squares to iteratively refine parameter predictions, enabling real-time collaboration among the machines in a fleet while minimizing the number of experimental trials. We validate our method using a mini 3D print farm consisting of ten 3D printers for which we optimize acceleration and speed settings to minimize surface roughness and printing time, thus maximizing print quality and productivity. Our approach achieves significantly faster convergence to optimal process parameters relative to a comparable non-collaborative technique.

同一类型的联网制造机器的机群，无论是在同一地点还是在不同地理位置，都越来越受欢迎。3D打印农场的兴起就是一个很好的例子，它由多台并行运行的联网3D打印机组成，从而实现了更快的生产和高效的大规模定制。然而，由于机器对机器的可变性，在一组制造机器（即使是同一类型）之间优化工艺参数仍然是一个挑战。传统的试错法效率低下，需要大量的测试来确定整个车队的最佳工艺参数。在这项工作中，我们引入了一个基于机器学习的协作推荐系统，该系统通过将问题建模为顺序矩阵完成任务来优化车队中每台机器的过程参数。我们的方法利用光谱聚类和交替最小二乘来迭代地改进参数预测，实现车队中机器之间的实时协作，同时最大限度地减少实验试验的数量。我们使用一个由10台3D打印机组成的迷你3D打印农场来验证我们的方法，我们优化了加速和速度设置，以最大限度地减少表面粗糙度和打印时间，从而最大限度地提高了打印质量和生产率。我们的方法实现了显著更快的收敛到最优工艺参数相对于一个可比的非协作技术。

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