Manufacturing Letters最新文献

英文中文

Learning through development of a digital manufacturing system in a learning factory using low-code/no-code platforms 通过使用低代码/无代码平台在学习型工厂中开发数字制造系统进行学习

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-09-12 DOI: 10.1016/j.mfglet.2025.09.001

Russel Bradley, Stanley S. Salim, Brian W. Anthony

This study demonstrates how low-code/no-code (LCNC) platforms can enable undergraduate students without software development backgrounds to design and build digital manufacturing systems. Students developed an IoT-enabled Manufacturing Execution System using Tulip Interfaces—an LCNC platform, focusing on applications like inventory tracking, machine monitoring, and digital work instructions in the FrED Factory—a learning factory at MIT. Evaluation through a pilot study showed students gained a strong understanding of smart manufacturing concepts while spending most of their time on systems design rather than software development. Individual interviews followed by a post-interview survey highlighted that the average percentage of time split between systems design and debugging the LCNC platform was 70–30% respectively. Additionally, all students responded with “strongly agree” to the question of whether the project enhanced their understanding of smart manufacturing concepts. LCNC platforms offer a practical, accessible approach to teaching digital manufacturing and can accelerate skill development in both educational and industrial settings.

本研究展示了低代码/无代码（LCNC）平台如何使没有软件开发背景的本科生能够设计和构建数字制造系统。学生们使用Tulip interface（一个LCNC平台）开发了一个支持物联网的制造执行系统，重点关注麻省理工学院FrED工厂（一个学习型工厂）的库存跟踪、机器监控和数字工作指令等应用。通过试点研究的评估表明，学生们在将大部分时间花在系统设计而不是软件开发上的同时，对智能制造概念有了深刻的理解。个人访谈和访谈后的调查显示，LCNC平台系统设计和调试的平均时间比例分别为70-30%。此外，对于该项目是否增强了他们对智能制造概念的理解，所有学生都表示“非常同意”。LCNC平台提供了一种实用的、可访问的方法来教授数字制造，并可以加速教育和工业环境中的技能发展。

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引用次数: 0

Data-driven optimization of temperature control for thick aluminum plate friction stir welding 厚铝板搅拌摩擦焊温度控制的数据驱动优化

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-11-19 DOI: 10.1016/j.mfglet.2025.11.004

Zhuo Sun, Xiaohong Lu, Banghua Yang

Temperature control in friction stir welding (FSW) of thick aluminum plates is critical for structural applications, yet direct temperature-based control targets remain undefined. Optimal temperature control targets for FSW of 18-mm-thick 2219 aluminum alloy were established through systematic analysis of 47 experimental datasets using response surface methodology and Pareto frontier analysis. An optimal temperature window (T_max: 510.4–514.2 °C, T_min: 419.5–423.4 °C) achieved balanced mechanical properties with ultimate tensile strength exceeding 290 MPa and elongation above 7 %. Validation experiments confirmed predictions with mean absolute percentage errors below 15 %. This framework provides direct temperature targets for industrial FSW control systems.

厚铝板搅拌摩擦焊（FSW）的温度控制是结构应用的关键，但直接基于温度的控制目标尚未明确。采用响应面法和Pareto边界分析法对47组实验数据进行了系统分析，确定了18mm厚2219铝合金FSW的最优温度控制目标。最佳温度窗（Tmax: 510.4-514.2℃，Tmin: 419.5-423.4℃）达到了平衡的力学性能，极限抗拉强度超过290 MPa，伸长率超过7%。验证实验证实了平均绝对百分比误差低于15%的预测。该框架为工业FSW控制系统提供了直接的温度目标。

引用次数: 0

Agentic AI for smart manufacturing 智能制造的人工智能代理

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-10-26 DOI: 10.1016/j.mfglet.2025.10.013

Jay Lee, Hanqi Su

The rise of data-rich manufacturing environments has created demand for artificial intelligence (AI) systems capable of autonomous, adaptive, and goal-oriented operations. Traditional AI methods, being largely task-specific, often lack the flexibility to perform effectively in dynamic, complex industrial settings. Recent advances in large language models (LLMs) have led to the emergence of Agentic AI, which extends AI capabilities through advanced reasoning, planning, tool integration, and multi-agent collaboration. While Agentic AI has been explored in domains such as computer science, healthcare, education, and finance, its adoption in manufacturing remains limited.

This paper defines Agentic AI in the manufacturing context, differentiates it from traditional AI agents, and presents a novel framework designed for smart manufacturing. The proposed framework integrates multiple LLM-based agents, a unified Data–Model–Knowledge (DMK) lake, and human expertise to enable advanced perception, reasoning, planning, orchestration, evaluation, optimization, and iterative improvement. A case study of a retrieval-augmented generation (RAG)-based LLM QA system demonstrates the framework’s feasibility. Key technical challenges are also discussed. The work aims to provide strategic guidance for the development and deployment of efficient, trustworthy Agentic AI systems in smart manufacturing.

数据丰富的制造环境的兴起创造了对能够自主、自适应和目标导向操作的人工智能（AI）系统的需求。传统的人工智能方法主要针对特定任务，往往缺乏灵活性，无法在动态、复杂的工业环境中有效执行。大型语言模型（llm）的最新进展导致了人工智能的出现，它通过高级推理、规划、工具集成和多智能体协作来扩展人工智能的能力。虽然人工智能已经在计算机科学、医疗保健、教育和金融等领域进行了探索，但它在制造业中的应用仍然有限。本文对制造环境下的人工智能agent进行了定义，并将其与传统的人工智能agent进行了区分，提出了一种新的智能制造框架。提出的框架集成了多个基于llm的代理、统一的数据模型知识（DMK）湖和人类专业知识，以实现高级感知、推理、规划、编排、评估、优化和迭代改进。一个基于检索增强生成（RAG）的LLM质量保证系统的实例研究证明了该框架的可行性。讨论了关键的技术挑战。这项工作旨在为智能制造中高效、可信赖的人工智能系统的开发和部署提供战略指导。

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引用次数: 0

Integrating feminist pedagogy into manufacturing education: a digital twin-based teaching module 将女性主义教学法融入制造业教育：基于数字双胞胎的教学模块

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-10-11 DOI: 10.1016/j.mfglet.2025.10.002

Anis Fatima, John L. Irwin

Integrating feminist pedagogy into engineering education offers a novel pathway to make technical learning more inclusive, participatory, and socially responsive. This paper presents the design and classroom implementation of a digital twin-based teaching module that combines sustainable manufacturing concepts with student-centered learning. A CNC milling machine was retrofitted and linked to its virtual counterpart using CAD/CAM tools, open-source controllers, and a custom-developed graphical user interface (GUI). The system captures real-time data on energy consumption and tool vibration, enabling students to explore how machining parameters impact sustainability factors such as power usage and vibration − induced tool wear. Grounded in feminist pedagogical principles, emphasizing collaboration, reflexivity, and co-creation of knowledge, the module was deployed in Smart Manufacturing and Internet of Things (IoT) courses. The approach fostered a more inclusive learning environment by encouraging active participation, shared authority, and critical thinking around engineering practices. Student surveys and course evaluations indicated improved engagement, deeper conceptual understanding, and greater satisfaction. These results highlight the potential of integrating feminist pedagogy with digital twin technology to enhance manufacturing education and better prepare students for the demands of Industry 4.0 and sustainable engineering.

将女性主义教学法整合到工程教育中，提供了一条使技术学习更具包容性、参与性和社会响应性的新途径。本文介绍了一个基于数字孪生的教学模块的设计和课堂实施，该模块将可持续制造概念与以学生为中心的学习相结合。利用CAD/CAM工具、开源控制器和定制开发的图形用户界面（GUI），对数控铣床进行了改造，并将其与虚拟铣床连接起来。该系统捕获能源消耗和刀具振动的实时数据，使学生能够探索加工参数如何影响可持续性因素，如电力使用和振动引起的刀具磨损。该模块以女权主义教学原则为基础，强调协作、反思性和知识的共同创造，被部署在智能制造和物联网（IoT）课程中。该方法通过鼓励积极参与、共享权威和围绕工程实践的批判性思考，培养了一个更具包容性的学习环境。学生调查和课程评估表明，学生的参与度提高了，对概念的理解加深了，满意度也提高了。这些结果突出了将女权主义教学法与数字孪生技术相结合的潜力，以增强制造业教育，并更好地为学生准备工业4.0和可持续工程的需求。

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引用次数: 0

Manufacturing hybrid carbon fiber laminates with 3D printed interlayers 用3D打印夹层制造混合碳纤维层压板

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.mfglet.2025.09.002

Alice Proietti, Fabrizio Quadrini, Loredana Santo

Hybrid laminates were manufactured with a honeycomb interlayer of PETG between composite plies. The interlayer was obtained by 3D printing either on the machine bed (Hybrid-B) and on the prepreg surface (Hybrid-S). Compression molding was performed for consolidation. Hybrid-B exhibited an accumulation of PETG at the warp/weft intersection of the composite fabric while a more uniform distribution was shown by Hybrid-S. The bending strengths of Hybrid-B and Hybrid-S were 726 MPa and 718 MPa, respectively. Hybridization led to improvements in the damping behavior as the loss factor at room temperature increased of 55.7 % and 58.8 % for Hybrid-B and Hybrid-S, respectively.

在复合材料层间添加蜂窝状PETG夹层，制备了杂化层合板。通过在机床床（Hybrid-B）和预浸料表面（Hybrid-S）上进行3D打印获得中间层。压缩成型进行巩固。Hybrid-B表现出PETG在复合织物经纬交点的积累，而Hybrid-S表现出更均匀的分布。Hybrid-B和Hybrid-S的抗弯强度分别为726 MPa和718 MPa。Hybrid-B和Hybrid-S的室温损耗因子分别提高了55.7%和58.8%，从而改善了阻尼性能。

引用次数: 0

Development of digital laboratory modules using computer simulation for enhanced learning experience in manufacturing education 利用计算机模拟开发数字实验室模块，以增强制造业教育的学习体验

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-10-12 DOI: 10.1016/j.mfglet.2025.10.009

S.M. Atikur Rahman , Selim Molla , Jakia Sultana , Richard Y. Chiou , Tzu-Liang (Bill) Tseng , Md. Fashiar Rahman

The complexity of modern manufacturing environments, characterized by interactions among various entities, variability, and randomness, presents significant challenges for learners. Understanding these dynamics is essential, but traditional classroom-only focused education often falls short in providing students with practical insights. Hands-on experimentation is vital for students to observe interactions and experience process manipulations, yet such experimental setups can be costly and impractical for many institutions. This paper presents the development of digital laboratory modules to enhance students’ learning experience in manufacturing education through computer simulation techniques. Two modules were created to address complex manufacturing issues: production design under demand uncertainty, manufacturing layout design, and different maintenance schedules. These modules allow users to control process parameters, design experiments, run simulations, and observe outcomes, promoting informed decision-making without wasting resources. This approach is particularly valuable for resource-constrained industries, facilitating rapid decision-making and process efficiency. Each module uses case studies with background information, problem statements, datasets, and expected results. The paper details the development process and case studies and includes experimentation guidelines for using the modules effectively in educational settings.

现代制造环境的复杂性，以各种实体之间的相互作用、可变性和随机性为特征，对学习者提出了重大挑战。了解这些动态是必不可少的，但传统的课堂教育往往不能为学生提供实用的见解。动手实验对于学生观察互动和体验过程操作是至关重要的，然而这种实验设置对许多机构来说可能是昂贵和不切实际的。本文介绍了利用计算机仿真技术开发数字化实验模块，以提高学生在制造教育中的学习体验。创建了两个模块来解决复杂的制造问题：需求不确定性下的生产设计、制造布局设计和不同的维护计划。这些模块允许用户控制过程参数，设计实验，运行模拟，并观察结果，促进明智的决策而不浪费资源。这种方法对于资源受限的行业特别有价值，可以促进快速决策和流程效率。每个模块都使用带有背景信息、问题陈述、数据集和预期结果的案例研究。本文详细介绍了开发过程和案例研究，并包括在教育环境中有效使用这些模块的实验指南。

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引用次数: 0

Utilizing Taguchi and ANOVA methods to investigate standard deviation of programmed torque for aluminum 6061-T6 friction stir welding with adaptive torque monitoring and control 采用田口法和方差分析方法研究6061-T6铝搅拌摩擦焊接程序转矩的标准差，并进行自适应转矩监测与控制

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-09-01 DOI: 10.1016/j.mfglet.2025.08.004

Austin Clark , Ihab Ragai

A Taguchi L₉ orthogonal array and Analysis of Variance (ANOVA) test for equal variance were used to determine variation in torque when adaptive torque monitoring and control is used in a Friction Stir Welding (FSW) application on AA6061-T6. Standard deviation was analyzed against the parameters of Programmed Torque (PT) and Feed Rate (FR). PT for the Z-axis motor determined the axial force at the tool during welding. PT values of 35, 40 and 45 Nm and FR of 100, 200 and 300 mm/min were studied in this paper. PT values of 35, 40 and 45 Nm correlated to 7.33, 8.38 and 9.43 kN axial force, respectively. It was found that the optimal parameter set with the lowest variation in torque through the entirety of the weld was conducted with a PT (45 Nm/9.43 kN) and an FR of 100 mm/min. These were the maximum and minimum values for PT and FR, respectively. Higher levels of torque variation occurred with higher FR and lower PT. This study offers insight into the effects process parameters have on torque variation when adaptive torque monitoring and control is used.

采用田口L9正交试验和方差分析（ANOVA）等方差检验来确定自适应扭矩监测和控制在AA6061-T6搅拌摩擦焊（FSW）应用中的扭矩变化。根据程序转矩（PT）和进给速率（FR）参数分析了标准偏差。z轴电机的PT决定了焊接过程中工具的轴向力。研究了PT值为35、40和45 Nm， FR为100、200和300 mm/min。PT值为35、40和45 Nm时，轴向力分别为7.33、8.38和9.43 kN。结果表明，在PT （45 Nm/9.43 kN）和FR为100 mm/min时，整个焊缝扭矩变化最小的最佳参数设置。这分别是PT和FR的最大值和最小值。高FR和低PT会导致更高的扭矩变化水平。该研究深入了解了在使用自适应扭矩监测和控制时，工艺参数对扭矩变化的影响。

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引用次数: 0

Contribution of deep reinforcement learning to solve reconfigurable facilities layout problems 深度强化学习在解决可重构设施布局问题中的贡献

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.mfglet.2025.09.003

Amine Chiboub , Julien Francois , Thècle Alix , Rémy Dupas

The Facilities Layout Problem involves arranging facilities within a given space to achieve specific objectives, such as minimizing transportation costs or reducing energy consumption. This issue arises in advanced manufacturing, particularly in Reconfigurable Manufacturing Systems (RMS), which allow layout adjustments based on changing product mixes, volumes, or processes. This paper compares the Double Dueling Deep Q-Network with traditional Q-learning and simulated annealing metaheuristic to assess the effectiveness of Deep Reinforcement Learning in addressing such challenges. Specifically, the study evaluates DDDQN performance in interactive environments where workstations are represented using a discrete approach, highlighting the role of reconfigurability in adjusting workstation implantation, orientation, and pickup/drop-off locations as required in RMS.

设施布局问题涉及在给定空间内安排设施以实现特定目标，例如最小化运输成本或减少能源消耗。这个问题出现在先进制造中，特别是在可重构制造系统（RMS）中，它允许根据变化的产品组合、数量或工艺进行布局调整。本文将双决斗深度q网络与传统q学习和模拟退火元启发式方法进行比较，以评估深度强化学习在解决此类挑战方面的有效性。具体而言，该研究评估了DDDQN在交互式环境中的性能，其中工作站使用离散方法表示，突出了可重构性在调整工作站植入，方向和RMS要求的取/落位置中的作用。

引用次数: 0

Surface topographical and morphological features of submerged waterjet peened AZ91D Mg alloy surfaces – A preliminary study 浸没水射流喷丸AZ91D镁合金表面形貌及形态特征的初步研究

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.mfglet.2025.09.004

Mugilvalavan Mohan , Thirumavalavan Krishnamurthy , Muruganandhan Radhakrishnan , Arunkumar Thirugnanasambandam

This research examines surface modifications in AZ91D magnesium alloy through submerged waterjet peening with varying parameters. A significant outcome is the achievement of a S_ku > 3, indicating a valley-dominated surface profile highly favourable for micro-lubricant retention and improved corrosion and tribological performance. Maximum surface variations were observed at D_c = 0.75 mm, v = 90 mm/min, and NOP = 5, resulting in a S_ku value of 10.107 ± 0.32 and also S_sk value indicating a valley-dominated profile. The enhanced S_ku value is attributed to intensified cavitation effects from the cumulative bubble collapse under high-pressure waterjet. Moreover, a maximum microhardness of 155 ± 13.9HV_0.1 was obtained at D_c = 1 mm, v = 90 mm/min, and NOP = 1, highlighting an optimal balance between plastic deformation and surface integrity. Surface morphology of selected peened samples indicates that prolonged exposure to high-pressure waterjet under submerged conditions led to intensified erosion, characterised by deeper valleys, micro-depressions, and craters, correlating with energy dispersion and material erosion, which indicates that SWP effectively modifies surfaces using only water and mechanical energy, avoiding chemical treatments and hazardous by-products. This makes SWP a sustainable surface modification technique and a promising green alternative for improving material performance across various industrial applications. However, further investigations are needed to optimise parameters and fully understand surface characteristics.

研究了不同参数下浸没水射流强化AZ91D镁合金的表面改性。一个重要的成果是实现了Sku >； 3，表明山谷主导的表面轮廓非常有利于微润滑剂的保留，并改善了腐蚀和摩擦学性能。在Dc = 0.75 mm, v = 90 mm/min和NOP = 5时，表面变化最大，Sku值为10.107±0.32，Ssk值也显示山谷主导剖面。高压水射流作用下，累积气泡破裂引起的空化作用加剧了Sku值的增大。此外，在Dc = 1 mm, v = 90 mm/min, NOP = 1时，获得的最大显微硬度为155±13.9HV0.1，突出了塑性变形和表面完整性之间的最佳平衡。所选喷淋样品的表面形貌表明，在淹没条件下长期暴露于高压水射流导致侵蚀加剧，其特征是更深的山谷、微洼地和陨石坑，与能量分散和物质侵蚀相关，这表明SWP仅利用水和机械能有效地改变表面，避免了化学处理和有害的副产品。这使得SWP成为一种可持续的表面改性技术，也是一种有前途的绿色替代方案，可以改善各种工业应用中的材料性能。然而，需要进一步的研究来优化参数并充分了解表面特征。

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引用次数: 0

Designing an LLM-based copilot for manufacturing equipment selection 基于llm的副驾驶机制造设备选型设计

IF 2 Q3 ENGINEERING, MANUFACTURING

Manufacturing Letters

Pub Date : 2025-12-01 Epub Date: 2025-10-29 DOI: 10.1016/j.mfglet.2025.10.017

Jonas Werheid , Oleksandr Melnychuk , Hans Zhou , Meike Huber , Christoph Rippe , Dominik Joosten , Zozan Keskin , Max Wittstamm , Sathya Subramani , Benny Drescher , Amon Göppert , Anas Abdelrazeq , Robert H. Schmitt

Effective decision-making in automation equipment selection is critical for reducing ramp-up time and maintaining production quality, especially in the face of increasing product variation and market demands. However, limited expertise and resource constraints often result in inefficiencies during the ramp-up phase when new products are integrated into production lines. Existing methods often lack structured and tailored solutions to support automation engineers in reducing ramp-up time, leading to compromises in quality. This research investigates whether large-language models (LLMs), combined with Retrieval-Augmented Generation (RAG), can assist in streamlining equipment selection in ramp-up planning. We propose a factual-driven copilot integrating LLMs with structured and semi-structured knowledge retrieval for three component types (robots, feeders and vision systems), providing a guided and traceable state-machine process for decision-making in automation equipment selection. The system was demonstrated to an industrial partner, who tested it on three internal use-cases. Their feedback affirmed its capability to provide logical and actionable recommendations for automation equipment. More specifically, among 47 equipment prompts analyzed, 24 involved selecting the correct equipment while considering most requirements, and in 20 cases, all requirements were fully met.

在自动化设备选择中有效的决策对于减少爬坡时间和保持生产质量至关重要，特别是面对不断增加的产品变化和市场需求。然而，有限的专业知识和资源限制往往导致在新产品集成到生产线上的提升阶段效率低下。现有的方法通常缺乏结构化和定制的解决方案来支持自动化工程师减少启动时间，从而导致质量上的妥协。本研究探讨了大语言模型（llm）与检索增强生成（RAG）相结合是否有助于简化爬升计划中的设备选择。我们提出了一种事实驱动的副驾驶，将llm与三种组件类型（机器人、喂食器和视觉系统）的结构化和半结构化知识检索集成在一起，为自动化设备选择决策提供了一个指导和可追溯的状态机过程。该系统向一个工业合作伙伴进行了演示，后者在三个内部用例上对其进行了测试。他们的反馈肯定了其为自动化设备提供逻辑和可操作建议的能力。更具体地说，在分析的47个设备提示中，有24个涉及在考虑大多数要求的情况下选择正确的设备，有20个案例完全满足所有要求。

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