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Synthetic data generation in manufacturing: a review of methods, domains, and emerging trends 制造中的合成数据生成：方法、领域和新兴趋势的回顾

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-18 DOI: 10.1016/j.procir.2025.08.199

Sara Shafiee

Data scarcity remains a major barrier to the effective deployment of AI in manufacturing, where labeled data is often limited, costly, or difficult to obtain. This review investigates how synthetic data generation techniques are being applied to address this challenge in manufacturing AI applications. Eighteen recent papers (Jan 2024- May 2025) were analyzed and categorized based on generation methods, application domains, and data modalities. Techniques covered include GAN (Generative Adversarial Networks), VAEs (Variational Autoencoders), diffusion models, simulation-based approaches, SMOTE (Synthetic Minority Oversampling Technique), and hybrid combinations. Their use spans tasks such as defect detection, predictive maintenance, process modeling, material design, and human–robot collaboration. The review highlights emerging trends, methodological trade-offs, and practical challenges shaping the future of synthetic data in intelligent manufacturing systems. In addition to consolidating recent work, the review identifies underexplored research gaps and methodological challenges that shape future directions in synthetic data use for manufacturing AI.

数据稀缺仍然是人工智能在制造业中有效部署的主要障碍，在制造业中，标记数据通常有限、昂贵或难以获得。这篇综述探讨了如何应用合成数据生成技术来解决制造业人工智能应用中的这一挑战。根据生成方法、应用领域和数据模式对最近发表的18篇论文（2024年1月- 2025年5月）进行了分析和分类。涵盖的技术包括GAN（生成对抗网络）、VAEs（变分自编码器）、扩散模型、基于仿真的方法、SMOTE（合成少数过采样技术）和混合组合。它们的使用范围包括缺陷检测、预测性维护、过程建模、材料设计和人机协作等任务。该综述强调了智能制造系统中合成数据未来的新兴趋势、方法权衡和实际挑战。除了巩固最近的工作外，该审查还确定了未充分开发的研究差距和方法挑战，这些差距和挑战将影响制造业人工智能合成数据使用的未来方向。

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引用次数: 0

An Explainable AI-Guided Feature Refinement Framework for Surface Roughness Prediction in Robotic Drilling 机器人钻孔表面粗糙度预测的可解释ai引导特征细化框架

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-18 DOI: 10.1016/j.procir.2025.09.041

Dongpeng Li , Fang Hao , Zheng Ma , Yugi Ji , Pai Zheng , Weihua Li , Liang Liu , Shuo Chen

In aircraft assembly, the low structural stiffness of industrial robots complicates surface quality control, and the opacity of conventional machine learning models hinders their adoption for process optimization. To address this, this paper presents a systematic Explainable AI-Guided Feature Refinement Framework to develop a minimal, yet robust, and physically interpretable model for surface roughness prediction in robotic drilling. The framework utilizes SHapley Additive exPlanations (SHAP) as an active component in an iterative feature selection process to refine a Random Forest model. The experimental validation on an integrated industrial platform demonstrates that this approach successfully identifies a minimal set of critical features from a high-dimensional dataset, including process parameters and specific vibration characteristics. The resulting model achieves superior predictive performance and stability compared to conventional feature selection methods. Furthermore, the analysis uncovers key non-linear relationships and feature interactions, providing interpretable insights into how operational parameters and dynamic responses collectively influence surface quality, which facilitates process optimization in robotic aircraft assembly.

在飞机装配中，工业机器人的低结构刚度使表面质量控制变得复杂，而传统机器学习模型的不透明性也阻碍了它们在工艺优化中的应用。为了解决这个问题，本文提出了一个系统的可解释的人工智能引导特征细化框架，为机器人钻井中的表面粗糙度预测开发一个最小的、鲁棒的、物理可解释的模型。该框架利用SHapley加性解释（SHAP）作为迭代特征选择过程中的主动组件来改进随机森林模型。在集成工业平台上的实验验证表明，该方法成功地从高维数据集中识别出最小的关键特征集，包括工艺参数和特定振动特性。与传统的特征选择方法相比，所得模型具有更好的预测性能和稳定性。此外，该分析还揭示了关键的非线性关系和特征相互作用，为操作参数和动态响应如何共同影响表面质量提供了可解释的见解，从而促进了机器人飞机装配过程的优化。

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引用次数: 0

Redesigning supply chains – An industrial case study 重新设计供应链——一个工业案例研究

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-12 DOI: 10.1016/j.procir.2026.01.011

Judit Monostori

In these turbulent times, companies are forced to rethink their operations every now and again. Naturally, their supply chains represent one of the main focuses of this process. The results of an industrial project on the analysis and potential restructuring of the European distribution network of a global manufacturing company are presented in the paper. In addition to providing the cost-optimal solution, it is demonstrated how the optimal network structure depends on the parameters of the considered cost factors. The consequences of the possible restructuring of the network on the transportation-related CO2 emission are also treated.

在这个动荡的时代，公司不得不时不时地重新思考他们的运营。当然，他们的供应链是这一过程的主要焦点之一。本文介绍了一个工业项目对一家全球制造公司的欧洲分销网络的分析和潜在重组的结果。除了提供成本最优的解决方案外，还演示了最优网络结构如何取决于所考虑的成本因素的参数。还讨论了路网可能改组对与运输有关的二氧化碳排放的影响。

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Innovative Approaches to High-Level End-to-End Supply Chain Planning: Large Bucket Lot Sizing Strategies for Multifaceted Integration and Optimization 创新方法高层次的端到端供应链规划：大桶批量战略的多方面整合和优化

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-12 DOI: 10.1016/j.procir.2026.01.013

Flavio Tonelli , Massimo Paolucci , Antonio Giovannetti

High level end-to-end supply chain production and logistics planning capable to consider a multifaceted integration of production, inventory management, supply, and transportation subject to multiple constraints is becoming feasible because of algorithms evolution and computational availability. The primary objective of this study is to devise an innovative approach based on large bucket lot sizing planning entailing the need for a streamlined process that meets the market demand and optimizes aggregate resources, as well as economic and financial aspects before tactical master production scheduling. The study comprises end-to-end supply chain dynamics analysis and provides algorithmically approach and experimental application domain.

由于算法的发展和计算的可用性，能够考虑生产、库存管理、供应和运输等多方面集成的高层次端到端供应链生产和物流规划正变得可行。本研究的主要目标是设计一种基于大桶批量计划的创新方法，该计划需要一个简化的过程，以满足市场需求并优化总资源，以及在战术主生产调度之前的经济和财务方面。该研究包括端到端供应链动态分析，并提供算法方法和实验应用领域。

引用次数: 0

An integrated re-planning ILP approach on the production capacity and sequence in the automotive industry. 汽车工业产能和生产顺序的综合再规划ILP方法。

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-12 DOI: 10.1016/j.procir.2026.01.022

Simon Gruber , Patricia Freyler , Clemens Gutschi , Nikolaus Furian , Ziga Letonja , Siegfried Vössner

Automotive contract manufacturers in Europe encounter significant market challenges due to the evolving market landscape towards e-mobility. This change involves a shift to smaller batch sizes and an expansion of the product portfolio. Further, the increased variety also raises the probability of production idleness caused by supply chain disruptions, which challenge production planning and control. This paper presents an integrated re-planning approach on the production capacity and sequence. We consider capacity and sequence restrictions, as well as the utilization of the production itself. An ILP is formulated for solving the problem and enabling further sensitivity analysis and risk reduction.

由于电动汽车的市场格局不断变化，欧洲的汽车合同制造商面临着重大的市场挑战。这一变化包括转向较小的批量大小和产品组合的扩展。此外，品种的增加也增加了由供应链中断引起的生产闲置的可能性，这对生产计划和控制提出了挑战。提出了一种基于生产能力和生产顺序的综合再规划方法。我们考虑产能和顺序限制，以及产品本身的利用率。制定ILP是为了解决问题，并进一步进行敏感性分析和降低风险。

引用次数: 0

Workforce and Logistics Centered Lineside Stock Optimization: A Case Study in automotive Industry. 以劳动力和物流为中心的一线库存优化：汽车行业的案例研究。

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-12 DOI: 10.1016/j.procir.2026.01.020

Benedikt Honeder , Clemens Gutschi , Simon Gruber , Nikolaus Furian , Siegfried Vössner

In general assembly in the automotive industry, material handling is crucial in terms of system efficiency and overall costs. Line balancing and optimized cycle times are heavily dependent on the line-side stock layout and assignment. This paper presents a hybrid approach for line-side stock area layout optimization under consideration of worker cost and logistics accessibility. While certain layouts are evaluated by a single-agent production simulation of different product variations, a genetic algorithm is proposed to optimize the layout with respect to multiple criteria. The resulting approach considers line-side stock dimensions as well as accessibility restrictions to enable automated line-balancing.

在汽车工业的总装中，物料处理在系统效率和总体成本方面至关重要。生产线平衡和优化周期时间在很大程度上取决于生产线侧的库存布局和分配。提出了一种考虑人力成本和物流可达性的线侧库存区布局优化的混合方法。通过对不同产品变化的单智能体生产模拟来评估某些布局，提出了一种基于多准则的遗传算法来优化布局。由此产生的方法考虑了线侧库存尺寸以及可访问性限制，以实现自动线平衡。

引用次数: 0

Towards a conceptual guideline for an economical assessment of manufacturing process data 为制造过程数据的经济评估提供一个概念性的指导方针

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-12 DOI: 10.1016/j.procir.2026.01.052

Johannes Mayer , Tobias Kaufmann , Philipp Niemietz , Thomas Bergs

Data will have an exponentially increasing economic value in the coming years. To harness the economic potential of data e.g. due to machine learning, data sharing is helpful to create the large amount of data needed to train models. For sharing data, the definition of prices for datasets is crucial. While current approaches exclusively take the data science perspective to evaluate the value of data based on quality dimensions, the consideration of a multidimensional assessability for transparent pricing is missing. The contribution of this paper is to sketch a conceptual guideline for assessing sensory-captured manufacturing process data from multidimensional perspectives.

未来几年，数据的经济价值将呈指数级增长。为了利用数据的经济潜力，例如由于机器学习，数据共享有助于创建训练模型所需的大量数据。为了共享数据，数据集价格的定义至关重要。虽然目前的方法完全采用数据科学的角度来评估基于质量维度的数据价值，但缺乏对透明定价的多维可评估性的考虑。本文的贡献是概述了从多维角度评估感官捕获的制造过程数据的概念指南。

引用次数: 0

A data-driven approach to determine tangential cutting force in turning operations using CNC control data 利用数控控制数据确定车削操作中切向切削力的数据驱动方法

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-12 DOI: 10.1016/j.procir.2026.01.074

Michal Demko , Jozef Brindza , István Sztankovics , Marek Vrabeľ , Ján Kušnír

This research investigates leveraging internal CNC data for tangential cutting force estimation in turning operations to detect tool wear. The experiment involved recording torque and power of the CNC lathe spindle drive while turning various cutting lengths every time with a new insert. The tangential cutting force was computed based on the recorded data and compared with measurements from external sensors. The analysis revealed a close correlation between the values obtained from both sources, affirming the feasibility of monitoring tool wear through internal CNC data. This approach demonstrates potential for real-time tool condition assessment in machining processes.

本研究探讨了在车削操作中利用内部CNC数据进行切向切削力估计以检测刀具磨损。实验记录了每次用新刀片切削不同切削长度时数控车床主轴驱动的扭矩和功率。根据记录数据计算切向切削力，并与外部传感器测量值进行比较。分析揭示了从两个来源获得的值之间的密切相关性，肯定了通过内部CNC数据监测刀具磨损的可行性。这种方法显示了在加工过程中实时评估刀具状态的潜力。

引用次数: 0

Analysis of the deflection characteristics of straight-fluted milling tools with direction-dependent compliance behavior. 具有方向相关柔度行为的直槽铣刀的偏转特性分析。

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-12 DOI: 10.1016/j.procir.2026.01.085

Rafael Garcia Carballo , Melina Wenzel , Jonas Baumann , Dirk Biermann , Petra Wiederkehr

Dynamically instable milling processes make it difficult to realize efficient and competitive manufacturing applications. Previous investigations have shown that tools with a direction-dependent compliance behavior can increase the process stability by disturbing the regenerative effect. In this work, milling tools with design features that allow simultaneous measurement of the deflection at the tool shank and at the TCP lead to a precise reconstruction of the deflection behavior. Straight-fluted milling tools are suitable for modeling milling processes due to the simplified process kinematics, especially in combination with the asymmetric dynamic properties, and should therefore be used for further modeling tasks.

动态不稳定的铣削过程使得实现高效和有竞争力的制造应用变得困难。先前的研究表明，具有方向依赖柔度行为的刀具可以通过干扰再生效应来提高工艺稳定性。在这项工作中，铣削工具的设计特点是可以同时测量刀柄和TCP的挠度，从而精确地重建挠度行为。由于简化了铣削过程的运动学，特别是结合非对称动力学特性，直槽铣削工具适合于铣削过程的建模，因此应用于进一步的建模任务。

引用次数: 0

A Collaborative Bayesian Optimization Dashboard for Manufacturing Process Optimization 面向制造过程优化的协同贝叶斯优化仪表板

Procedia CIRP

Pub Date : 2026-01-01 Epub Date: 2026-02-18 DOI: 10.1016/j.procir.2025.09.003

Lars Leyendecker , Mohamed Amine Kooli , Christian Wergers , Dennis Grunert , Robert H. Schmitt

A central task in production engineering is the parameterization of manufacturing processes and machinery. The parameterization has a significant impact on product quality, process efficiency, and profitability of the production. Bayesian optimization (BO) - an adaptive black-box optimization algorithm for efficient and performance-optimal parameterization - has emerged in recent years as a promising alternative to conventional experimental design methods such as design of experiments, one factor at a time, or trial and error. Because optimization of manufacturing processes falls under the responsibility of human process experts, close collaboration between BO and human experts is key to successful optimization. Although first approaches to collaborative BO exist, intuitive dashboards that communicate and explain parameter suggestions and optimization progress to process experts are missing. In this paper, we propose a three-phase pipeline for collaborative BO, motivate the need for a collaborative Bayesian process optimization dashboard and define a total of 15 requirements for the dashboard design. Based on this, we propose a design concept for the BO-dashboard comprising multiple metrics and visualizations to explain parameter suggestions, create transparency in the optimization process, and promote the accumulation of process knowledge. We showcase the implementation of the dashboard at the example of optimizing an ultra-short pulsed laser ablation process. By enhancing human-BO collaboration, we aim to promote the adoption of BO within the conservative industry of production engineering.

生产工程的中心任务是制造过程和机械的参数化。参数化对产品质量、工艺效率和生产盈利能力有重要影响。贝叶斯优化（BO）是一种用于高效和性能最优参数化的自适应黑盒优化算法，近年来作为传统实验设计方法（如实验设计、一次一个因素或试错法）的一种有前途的替代方法而出现。由于制造过程的优化是人类过程专家的责任，因此BO和人类专家之间的密切协作是成功优化的关键。虽然协作BO的第一种方法已经存在，但是向流程专家沟通和解释参数建议和优化进度的直观仪表板仍然缺失。在本文中，我们提出了一个用于协作BO的三阶段管道，激发了对协作贝叶斯流程优化仪表板的需求，并定义了仪表板设计的总共15个需求。基于此，我们提出了一种包含多个指标和可视化的BO-dashboard的设计理念，以解释参数建议，在优化过程中创造透明度，促进过程知识的积累。我们以优化超短脉冲激光烧蚀工艺为例，展示了仪表板的实现。通过加强人与人之间的BO协作，我们的目标是在保守的生产工程行业中促进BO的采用。

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