Procedia CIRP最新文献

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From connectivity via intelligence toward sustainability? Maturity of shopfloor automation technology in the manufacturing industry 从连通性到智能化，再到可持续性？制造业车间自动化技术的成熟度

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.03.024

The manufacturing industry is experiencing a significant transformation driven by digital technologies, Industry 4.0 concepts, and the potential for sustainable practices. This research presents an overview of the manufacturing industry, focusing on the design of manufacturing systems, digital transformation, and sustainability, identifying the gap between technological advancements and their application in industrial settings. In order to examine this, a panel survey of 899 industrial participants was conducted. This analysis assesses the potential for automation and digital factory implementation alongside the dedication for sustainability in the manufacturing industry. The findings indicate that while research has advanced in these areas, practical applications in the manufacturing industry still lag behind, particularly in terms of technology readiness levels and data implementation for automation. This paper serves as a foundation for future research, providing insights into technologies ready for industrial implementation and identifying areas requiring further optimization and study. Therefore, a matrix is presented that aids in selecting or strategizing technology utilization during the design of manufacturing systems. However, this research primarily focused on technological aspects, recommending future research to explore the interplay between organizational, human related factors, and technology to design manufacturing systems successfully with respect to sustainability.

在数字技术、工业 4.0 概念和可持续实践潜力的推动下，制造业正在经历一场重大变革。本研究对制造业进行了概述，重点关注制造系统的设计、数字化转型和可持续发展，找出技术进步与其在工业环境中的应用之间的差距。为此，我们对 899 名工业参与者进行了小组调查。这项分析评估了自动化和数字化工厂实施的潜力，以及对制造业可持续发展的贡献。研究结果表明，虽然这些领域的研究取得了进展，但制造业的实际应用仍然滞后，尤其是在自动化的技术准备水平和数据实施方面。本文为今后的研究奠定了基础，深入探讨了可用于工业实施的技术，并确定了需要进一步优化和研究的领域。因此，本文提出了一个矩阵，有助于在设计制造系统时选择技术或制定技术利用战略。不过，本研究主要侧重于技术方面，建议今后的研究探索组织、与人相关的因素和技术之间的相互作用，以成功设计出具有可持续性的制造系统。

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引用次数: 0

Development of Standardization Concepts for Packaging Machines 开发包装机标准化概念

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.03.028

Packaging machines are designed to meet specific customer requirements and are usually produced on a one-off basis. To cater to consumer demands, packaging machines have become more and more complex. This extends the time taken for development and amplifies expenses. Therefore, novel approaches for standardization have to be explored beyond the established applications of mass production.

To achieve this, an interdisciplinary value stream analysis is carried out within the relevant departments of a packaging machine company to identify specific constraints and requirements. These requirements are structured and documented to provide a foundation for targeted development of standardization concepts. Additionally, new possibilities are created to evaluate the effectiveness of standardization measures in the future and to establish a continual improvement process.

The aim of this project is to establish a comprehensive strategy for standardization, enhanced communication, and data management that will be applied at all operational levels of the company. New communication methods, along with participatory and reflective methods, are expected to enhance acceptance by the employees and optimize business processes.

包装机是为满足客户的特定要求而设计的，通常是一次性生产。为了满足消费者的需求，包装机变得越来越复杂。这就延长了研发时间，增加了开支。为此，包装机械公司的相关部门要进行跨学科的价值流分析，以确定具体的制约因素和要求。对这些要求进行整理和记录，为有针对性地开发标准化概念奠定基础。此外，还为评估未来标准化措施的有效性和建立持续改进流程创造了新的可能性。该项目的目的是建立一个标准化、加强沟通和数据管理的综合战略，该战略将应用于公司的所有运营层面。新的沟通方法以及参与和反思方法有望提高员工的接受程度并优化业务流程。

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引用次数: 0

Active Learning Processes for Smart Inspection, Verification Operations and Modelling of Surfaces with Geometrical Deviations 用于有几何偏差表面的智能检测、验证操作和建模的主动学习过程

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.034

The ISO 17450-2:2012 defines the verification operation. The ISO 17450-1:2011 defines the extracted features and the extraction operations on the “non-ideal surface model or on the real surface of the workpiece”. The ISO 12781-2:2011 and 12180-2:2011 provide dedicated extraction strategies for the flatness and cylindricity. But no real measurement process is explicit here. However, one of the key factors in an inspection plan is the choice of the measurement strategy. Some sampling strategies, such as Hammersley sequence, offer robust and efficient technics to ensure good distribution of measurement points. But measurements are always defined with a “blind sampling strategy”. So, in this paper, the author proposed to use adaptative sampling strategies, based on the Active learning method based on Kriging for the Inspection of Large Surfaces (AK-ILS) and developed an original Active Learning method based on Multi-Layer-Perceptron (AL-MLP), to classify the conform / non-conform parts and to ensure the reconstruction of the surfaces, with smart Skin Models Shapes and a minimal sampling size.

ISO 17450-2:2012 规定了验证操作。ISO 17450-1:2011 规定了 "非理想表面模型或工件真实表面 "的提取特征和提取操作。ISO 12781-2:2011 和 12180-2:2011 为平面度和圆柱度提供了专门的提取策略。但这里没有明确说明实际测量过程。不过，检测计划的关键因素之一是测量策略的选择。一些采样策略，如哈默斯利序列，提供了强大而高效的技术，以确保测量点的良好分布。但是，测量总是以 "盲目采样策略 "来定义的。因此，在本文中，作者建议使用基于克里金法的大表面检测主动学习方法（AK-ILS）的适应性取样策略，并开发了一种基于多层感知器（AL-MLP）的原始主动学习方法，以对符合/不符合部件进行分类，并确保通过智能皮肤模型形状和最小取样尺寸重建表面。

引用次数: 0

Quality control in manufacturing through temperature profile analysis of metal bars: A steel parts use case 通过金属棒的温度曲线分析进行生产质量控制：钢铁部件使用案例

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.036

Non-uniform heating during metal bar hot forming may impact its straightness. In this study, an infrared non-destructive inspection system is proposed to acquire steel temperature profiles in runtime which should correlate to straightness deviations. Additionally, a machine learning algorithm detects outliers to identify oxides on the metal, which in turn is correlated to process parameters. This allows for proactive temperature adjustment to mitigate the risk based on historical profiles. The proposed approach has been tested in a use case coming from the steel industry.

金属棒热成型过程中的不均匀加热可能会影响其直线度。本研究提出了一种红外无损检测系统，用于在运行时获取钢材温度曲线，该曲线应与直线度偏差相关联。此外，机器学习算法可检测异常值，识别金属上的氧化物，进而与工艺参数相关联。这样就可以根据历史曲线主动调整温度，降低风险。所提出的方法已在钢铁行业的一个使用案例中进行了测试。

引用次数: 0

An Improved PointNet++ Based Method for 3D Point Cloud Geometric Features Segmentation in Mechanical Parts 基于 PointNet++ 的改进型三维点云机械零件几何特征分割方法

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.006

The extraction of geometric features such as holes, arcs, and surfaces of mechanical parts is crucial for quality control. The existing methods for geometrical feature segmentations on 3D point clouds still have limitations, especially for simultaneously extracting multiple types of geometric features from comprehensive workpieces. To this end, this study investigates segmentation methods that take 3D point cloud datasets of mechanical parts as inputs, and employs an improved PointNet++ deep learning model to solve this extraction difficulty. Firstly, the Set Abstraction module in PointNet++ was modified by incorporating Self-Attention mechanisms to increase interactivity and global correlation among data points. Then, the local feature extraction Multilayer Perceptron (MLP) from PointNet-Transformer was integrated to enhance the feature extraction accuracy. Due to the inherent class imbalance issue, the Focal Tversky Loss is employed as the loss function to ensure that geometric features with relatively lower proportions can be fully trained. Finally, the Statistical filtering algorithm is utilized to mitigate noise and attenuate subtle irregularities, such that the smoothness of geometric features can be substantially enhanced. The experimental results demonstrate that the proposed model achieves an accuracy of 86.6% on geometric feature segmentations and a mean Intersection over Union (mIoU) of 0.84. The comparison with the original PointNet++ proves that the proposed method can improve accuracy and mIoU by 3.7% and 0.03 respectively.

提取机械零件的孔、弧和表面等几何特征对于质量控制至关重要。现有的三维点云几何特征分割方法仍然存在局限性，尤其是在同时提取综合工件的多种几何特征时。为此，本研究探讨了以机械零件三维点云数据集为输入的分割方法，并采用改进的 PointNet++ 深度学习模型来解决这一提取难题。首先，对 PointNet++ 中的集合抽象模块进行了修改，加入了自我关注机制，以提高交互性和数据点之间的全局相关性。然后，集成了 PointNet-Transformer 中的局部特征提取多层感知器（MLP），以提高特征提取的准确性。由于存在固有的类不平衡问题，因此采用了 Focal Tversky Loss 作为损失函数，以确保比例相对较低的几何特征能够得到充分训练。最后，利用统计滤波算法来减少噪音和削弱细微的不规则性，从而大大提高几何特征的平滑度。实验结果表明，所提出的模型在几何特征分割上达到了 86.6% 的准确率，平均交集大于联合（mIoU）为 0.84。与原始 PointNet++ 的比较证明，所提出的方法能将准确率和 mIoU 分别提高 3.7% 和 0.03。

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引用次数: 0

Multi-sensor data fusion framework and validation of algorithms with reference datasets 多传感器数据融合框架和利用参考数据集验证算法

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.024

Spatial data fusion algorithms are widely applied in dimensional metrology for quality assessment or surface reconstruction. Multi-sensor point cloud fusion combines the advantages of multiple sensors by merging their measurements into a single coordinate system and reducing the prediction uncertainty and systematic errors. Algorithms designed for these tasks employ many methods that require thorough evaluations through a common framework. To address this need, this paper proposes a framework for simultaneous registration and approximation, and introduces a reference data generator for unbiased evaluations of data fusion algorithms with heterogeneous and anisotropic noise assumptions for applications involving multiple sensors. The bias for the generated reference data is evaluated close to floating point accuracy, which validates the generation method, and uncertainty evaluation on ICP variants reveals that reference data is more suitable to evaluate point cloud fusion algorithms. The proposed framework and data generator allows developing and validating more accurate data fusion algorithms.

空间数据融合算法被广泛应用于质量评估或表面重建的尺寸计量中。多传感器点云融合结合了多个传感器的优势，将它们的测量结果合并到一个坐标系中，减少了预测的不确定性和系统误差。针对这些任务设计的算法采用了多种方法，需要通过一个通用框架进行全面评估。为了满足这一需求，本文提出了一个同步注册和近似的框架，并引入了一个参考数据生成器，用于对涉及多个传感器应用的具有异构和各向异性噪声假设的数据融合算法进行无偏评估。对生成的参考数据进行的偏差评估接近浮点精度，这验证了生成方法，而对 ICP 变体进行的不确定性评估显示，参考数据更适合评估点云融合算法。建议的框架和数据生成器允许开发和验证更精确的数据融合算法。

引用次数: 0

A prediction method of surface geometric deviation for additive manufacturing parts based on knowledge-integrated deep learning algorithm 基于知识集成深度学习算法的增材制造零件表面几何偏差预测方法

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.005

Compared with traditional machining processes, additive manufacturing (AM) has received widespread attention in recent years because of its high degree of modeling freedom. However, due to the multiple manufacturing errors and complex physical state changes involved in the process, the geometric deviation on the AM part surface is a challenge for controlling product geometrical quality. To address this problem, data-driven machine learning (ML) techniques have been widely studied in product quality controlling. However, traditional ML greatly depends on the training sample data, and suffers the risk of violating physical mechanisms due to the lack of domain knowledge. In order to take the best advantage of domain knowledge, prior information and deep learning algorithm, this paper proposes a knowledge-integrated deep learning algorithm and constructs the geometric deviation prediction model of the AM part surface. After that, the method was verified with design of experiments. The results show that compared with the data-driven neural network (DDNN), the knowledge-integrated neural network (KINN) has fewer iterations during the training process, less sample data requirement and more accurate prediction results.

与传统加工工艺相比，快速成型制造（AM）因其建模自由度高，近年来受到广泛关注。然而，由于加工过程中存在多种制造误差和复杂的物理状态变化，AM 零件表面的几何偏差成为控制产品几何质量的难题。为解决这一问题，数据驱动的机器学习（ML）技术已在产品质量控制领域得到广泛研究。然而，传统的 ML 在很大程度上依赖于训练样本数据，并且由于缺乏领域知识而存在违反物理机制的风险。为了充分利用领域知识、先验信息和深度学习算法，本文提出了一种知识集成深度学习算法，并构建了 AM 零件表面几何偏差预测模型。随后，通过实验设计对该方法进行了验证。结果表明，与数据驱动神经网络（DDNN）相比，知识集成神经网络（KINN）在训练过程中的迭代次数更少，样本数据要求更低，预测结果更准确。

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引用次数: 0

A Bayesian Approach For The Consideration Of Measurement Errors 考虑测量误差的贝叶斯方法

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.031

Metrology is a key tool for tolerancing as it is used to determine whether dimensions are within their tolerance intervals. However, measurement errors cannot be avoided and need being accounted for. The probabilistic approach is applied to both the dimensions and their measurement errors; they are modelled as random variables and characterized by their probability density function. The probability density function of the measurement error is assumed to be known; this work is included in a research project in collaboration with a metrology company, where the engineers are able to provide us with this information. This paper describes a strategy to account for such measurement errors and (partially) correct or mitigate their effects. Through Bayesian inference, the likelihood of true values given measured values is estimated, allowing for a probabilistic correction. The proposed method is applied to numerical examples with simulated data and its relevance is discussed.

计量是公差的关键工具，因为它用于确定尺寸是否在公差范围内。然而，测量误差是无法避免的，需要加以考虑。概率方法适用于尺寸及其测量误差；它们被模拟为随机变量，并以其概率密度函数为特征。测量误差的概率密度函数假定是已知的；这项工作包含在与一家计量公司合作的研究项目中，该公司的工程师可以向我们提供这方面的信息。本文介绍了一种考虑此类测量误差并（部分）纠正或减轻其影响的策略。通过贝叶斯推理，可以估算出测量值的真实值的可能性，从而进行概率修正。所提出的方法应用于模拟数据的数值示例，并讨论了其相关性。

引用次数: 0

Automatic diagnosis and thickness determination for white etching layers in deep drilled steels based on thresholding and machine learning algorithms 基于阈值和机器学习算法的深钻钢中白色蚀刻层的自动诊断和厚度测定

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.05.008

Simon Strodick , Robert Schmidt , Andreas Zabel , Dirk Biermann , Frank Walther

The reliable detection and precise assessment of white etching layers (WEL) are key challenges in the investigation of a component’s surface integrity. This paper proposes an innovative methodology for evaluating the extent of WEL in quenched and tempered steels, machined by Boring and Trepanning Association (BTA) deep hole drilling. Micrographs obtained by light microscopy were partitioned into classes by three methods, separating the WEL from the base material and the embedding resin. Traditional manual segmentation was performed as a benchmark for automatic segmentation methods. A gray level thresholding-based method served for the segmentation of micrographs partitioned into subsets. In addition to conventional manual and thresholding-based segmentation, a machine learning-based approach for image segmentation was applied. The segmented images were further analyzed by a newly developed set of algorithms, implemented to obtain detailed information on the WEL, e.g. their average thickness as well as the area covered by WEL in the micrographs. Results indicate that both, gray level thresholding, as well as machine learning-based image segmentation, show potential for the automated diagnosis and assessment of WEL. They both yield quantitatively similar, but less biased results compared to manual segmentation.

可靠检测和精确评估白色蚀刻层（WEL）是调查部件表面完整性的关键挑战。本文提出了一种创新方法，用于评估通过镗孔和穿孔协会（BTA）深孔钻加工的淬火和回火钢中的白蚀层程度。通过三种方法将光学显微镜获得的显微照片分成三类，将 WEL 从基体材料和包埋树脂中分离出来。传统的人工分割是自动分割方法的基准。基于灰度阈值的方法用于分割被划分为子集的显微照片。除了传统的人工分割和基于阈值的分割外，还采用了基于机器学习的图像分割方法。新开发的一套算法对分割后的图像进行了进一步分析，以获取 WEL 的详细信息，例如它们的平均厚度以及 WEL 在显微照片中的覆盖面积。结果表明，灰度阈值法和基于机器学习的图像分割法都显示出自动诊断和评估 WEL 的潜力。与人工分割相比，它们都能得到数量上相似但偏差较小的结果。

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引用次数: 0

The influence of an ambient energetic field on precision cutting 环境能量场对精密切割的影响

Procedia CIRP

Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.05.010

Tjarden Zielinski , Oltmann Riemer

Ultrasonic-assisted machining processes with diamond tools are regarded as a key technology for reducing the catastrophic tool wear occurring while machining steel workpieces with surface roughness in optical quality. Economical machining of various steel materials with significantly reduced wear of the diamond tool further improved since elliptical vibration cutting with a superimposed ultrasonic tool motion was introduced. How far the result of the machining process is influenced by the changing process kinematics of the ultrasonic motion alone or by the energy introduced into the material by the energy of an ambient ultrasonic field has not been investigated yet. The presented work is dedicated to superimposing an ultrasonic field into the workpiece during machining using an ultrasonic bath. Machining experiments with cutting grooves and particular surfaces with monocrystalline diamond tools are carried out on brass, copper and aluminum. The process forces show a decrease with the increase of the ultrasonic energy of up to 50 percent, while the surface roughness remains uninfluenced by the ultrasonic energy. The results indicate that the ultrasonic induced softening has an influence on the cutting process, which could improve the machining of brittle hard materials in future investigations.

使用金刚石刀具的超声波辅助加工工艺被认为是一项关键技术，可在加工表面粗糙度达到光学质量的钢制工件时减少刀具的灾难性磨损。自从引入了叠加超声波刀具运动的椭圆振动切削后，各种钢材料的经济型加工和金刚石刀具磨损的显著减少得到了进一步改善。至于加工过程的结果在多大程度上仅受超声波运动的过程运动学变化的影响，或受环境超声波场能量引入材料的能量的影响，尚未进行研究。本研究致力于利用超声波槽在加工过程中向工件叠加超声波场。使用单晶金刚石工具对黄铜、铜和铝进行了切槽和特殊表面的加工实验。加工力随着超声波能量的增加而降低，降幅高达 50%，而表面粗糙度则不受超声波能量的影响。结果表明，超声波诱导的软化对切削过程有影响，这可以在未来的研究中改善脆性硬质材料的加工。

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