Computers in Industry最新文献_第10页

Survey of automated methods for design and assessment of smart products 智能产品设计和评估的自动化方法综述

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-05-24 DOI: 10.1016/j.compind.2025.104316

Anoop Kumar Sinha , Youngmi Christina Choi , David W. Rosen

User centric smart products prioritize the needs and preferences of users, enhancing their experience and satisfaction. Involving users in the design and assessment of smart products ensures that they meet real-world requirements, leading to more intuitive product design, user interface, and functionalities that truly resonate with users. Further, the capability of generating and evaluating many alternative designs early in product development is beneficial. However, the need to construct physical prototypes for user testing limits the number of designs that can be evaluated during early design stages. As such, our interest is in automated methods that support user centered design and usability and user experience assessment. In this review article, we look at at two decades of automation methods that have been employed in the design and development of user centric smart products. The focus of these automation methods is to incorporate user voice in early design stages rather than replacing the users. We have identified five key activities of the design cycle in which automated methods have been employed: design thinking, design ideation, prototype creation, user data collection for usability study, and user data analysis. Overall, 154 articles were identified across engineering, human-computer interaction, human factors, inclusive design, industrial design, and other disciplines that have incorporated automation methods to include the user’s voice in the design of user centric smart products. This review examines the effectiveness and limitations of different automation methods compared to conventional methods, offering valuable insights and suggestions to enhance the design processes of smart products with a focus on widespread usability issues. Our specific interest lies in developing assistive mobility and rehabilitation devices, where constraints such as limited development time and resources persist, yet the usability and user experience profoundly influence significant outcomes like perceived functionality, stigma, and device acceptance.

以用户为中心的智能产品优先考虑用户的需求和偏好，提高用户的体验和满意度。让用户参与智能产品的设计和评估，确保产品符合现实需求，从而使产品设计、用户界面和功能更加直观，真正与用户产生共鸣。此外，在产品开发早期生成和评估许多备选设计的能力是有益的。然而，为用户测试构建物理原型的需要限制了在早期设计阶段可以评估的设计的数量。因此，我们的兴趣在于支持以用户为中心的设计、可用性和用户体验评估的自动化方法。在这篇综述文章中，我们研究了二十年来在以用户为中心的智能产品的设计和开发中采用的自动化方法。这些自动化方法的重点是在早期设计阶段纳入用户的声音，而不是取代用户。我们已经确定了采用自动化方法的设计周期的五个关键活动：设计思维、设计构思、原型创建、可用性研究的用户数据收集和用户数据分析。总体而言，在工程、人机交互、人为因素、包容性设计、工业设计和其他学科中确定了154篇文章，这些学科已将自动化方法纳入以用户为中心的智能产品设计中，包括用户的声音。本文考察了与传统方法相比，不同自动化方法的有效性和局限性，为提高智能产品的设计过程提供了有价值的见解和建议，重点关注广泛的可用性问题。我们的具体兴趣在于开发辅助移动和康复设备，其中限制，如有限的开发时间和资源持续存在，但可用性和用户体验深刻地影响重大结果，如感知功能，污名和设备接受度。

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引用次数: 0

A novel paradigm for predicting and interpreting uneven roll wear in the hot rolling steel industry 预测和解释热轧钢行业轧辊不均匀磨损的新范式

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-05-21 DOI: 10.1016/j.compind.2025.104318

Wen Peng , Cheng-yan Ding , Yu Liu , Jia-nan Sun , Zhen Wei , Wen-bo Wang , Dian-hua Zhang , Jie Sun

In the hot rolling industry, uneven roll wear significantly influences schedule free rolling and product quality, necessitating more precise wear prediction to improve the capabilities of hot rolling production. However, existing methods, laden with limitations, struggle to predict uneven roll wear precisely and transparently. To address these challenges, we present a novel paradigm that combines a computer simulation technique, classical wear theory and a data-driven approach for predicting uneven work roll wear in the hot rolling industry. Initially, a finite element model is constructed to simulate hot rolling processing. Subsequently, an Archard-theory-based work roll wear model is derived to calculate the theoretical wear loss using the simulation results. Following this, based on the theoretical wear loss, a deep ensemble model containing three base predictors is established. Notably, Shapley additive explanations (SHAP) and ensemble mechanism analysis are implemented to explain the predictive process of the wear loss. The comparative experimental results demonstrate the deep ensemble method achieves a 2 % accuracy improvement over other machine learning models. Additionally, the wear prediction results for a real case of a roll change period prove that, at the peak position of wear profile, the proposed paradigm surpasses the existing model by 7.2 %. Significantly, the feature contributions and process interpretable analysis based on SHAP make the proposed paradigm both transparent and reliable.

在热轧行业中，轧辊磨损不均匀严重影响无进度轧制和产品质量，需要更精确的磨损预测来提高热轧生产能力。然而，现有的方法，充满了局限性，难以准确和透明地预测轧辊不均匀磨损。为了应对这些挑战，我们提出了一种新的范例，将计算机模拟技术、经典磨损理论和数据驱动方法相结合，用于预测热轧工业中工作辊的不均匀磨损。首先，建立了模拟热轧过程的有限元模型。在此基础上，建立了基于archard理论的工作辊磨损模型，利用仿真结果计算理论磨损损失。在此基础上，以理论磨损量为基础，建立了包含三个基本预测量的深度系综模型。值得注意的是，采用Shapley加性解释（SHAP）和系综机理分析来解释磨损的预测过程。对比实验结果表明，与其他机器学习模型相比，深度集成方法的准确率提高了2 %。此外，对实际轧辊换期的磨损预测结果表明，在磨损曲线的峰值位置，所提出的模型比现有模型高出7. %。值得注意的是，基于SHAP的特征贡献和过程可解释性分析使所提出的范式既透明又可靠。

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引用次数: 0

A novel and scalable multimodal large language model architecture Tool-MMGPT for future tool wear prediction in titanium alloy high-speed milling processes 面向钛合金高速铣削过程刀具磨损预测的新型多模态大语言模型体系结构tool - mmgpt

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-30 DOI: 10.1016/j.compind.2025.104302

Caihua Hao , Zhaoyu Wang , Xinyong Mao , Songping He , Bin Li , Hongqi Liu , Fangyu Peng , Weiye Li

Accurately predicting the future wear of cutting tools with variable geometric parameters remains a significant challenge. Existing methods lack the capability to model long-term temporal dependencies and predict future wear values—a key characteristic of world models. To address this challenge, we introduce the Tool-Multimodal Generative Pre-trained Transformer (Tool-MMGPT), a novel and scalable multimodal large language model (MLLM) architecture specifically designed for tool wear prediction. Tool-MMGPT pioneers the first tool wear world model by uniquely unifying multimodal data, extending beyond conventional static dimensions to incorporate dynamic temporal dimensions. This approach extracts modality-specific information and achieves shared spatiotemporal feature fusion through a cross-modal Transformer. Subsequently, alignment and joint interpretation occur within a unified representation space via a multimodal-language projector, which effectively accommodates the comprehensive input characteristics required by world models. This article proposes an effective cross-modal fusion module for vibration signals and images, aiming to fully leverage the advantages of multimodal information. Crucially, Tool-MMGPT transcends the limitations of traditional Large Language Models (LLMs) through an innovative yet generalizable method. By fundamentally reconstructing the output layer and redefining training objectives, we repurpose LLMs for numerical regression tasks, thereby establishing a novel bridge that connects textual representations to continuous numerical predictions. This enables the direct and accurate long-term forecasting of future wear time series. Extensive experiments conducted on a newly developed multimodal dataset for variable geometry tools demonstrate that Tool-MMGPT significantly outperforms state-of-the-art (SOTA) baseline methods. These results highlight the model's superior long-context modeling capabilities and illustrate its potential for effective deployment in environments with limited computational resources.

准确预测具有可变几何参数的刀具的未来磨损仍然是一个重大挑战。现有方法缺乏模拟长期时间依赖性和预测未来磨损值的能力——这是世界模型的一个关键特征。为了应对这一挑战，我们引入了工具-多模态生成预训练变压器（tool - mmgpt），这是一种专门为工具磨损预测设计的新颖且可扩展的多模态大语言模型（MLLM）架构。tool - mmgpt通过独特的统一多模态数据开创了第一个工具磨损世界模型，超越了传统的静态维度，纳入了动态时间维度。该方法提取特定于模态的信息，并通过跨模态转换器实现共享的时空特征融合。随后，通过多模态语言投影仪在统一的表示空间内进行对齐和联合解释，有效地适应了世界模型所需的综合输入特征。本文提出了一种有效的振动信号与图像的跨模态融合模块，旨在充分发挥多模态信息的优势。最重要的是，Tool-MMGPT通过一种创新且可推广的方法超越了传统大型语言模型（llm）的局限性。通过从根本上重构输出层和重新定义训练目标，我们将llm重新用于数值回归任务，从而建立了连接文本表示和连续数值预测的新桥梁。这使得对未来磨损时间序列的直接和准确的长期预测成为可能。在新开发的可变几何工具多模态数据集上进行的大量实验表明，Tool-MMGPT明显优于最先进的（SOTA）基线方法。这些结果突出了该模型优越的长上下文建模能力，并说明了它在计算资源有限的环境中有效部署的潜力。

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引用次数: 0

A simple and reliable semi-supervised anomaly detection network for detecting crack in stamped parts 一种用于冲压件裂纹检测的简单可靠的半监督异常检测网络

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-26 DOI: 10.1016/j.compind.2025.104301

Xingjun Dong , Changsheng Zhang , Shuaitong Liu , Dawei Wang

Stamped parts play a crucial role in industrial manufacturing, and it is particularly important to automatically inspect their surface cracks. Since crack is rare and diverse, supervised defect detection methods lack sufficient data and cannot achieve ideal results. Unsupervised anomaly detection algorithms, which do not require crack data, can identify unknown cracks. However, they tend to have high rates of missed detections and false positives when dealing with complex backgrounds in stamped parts. To address these problems, this paper proposes a network called simple and reliable semi-supervised anomaly detection, considering the presence of a small number of anomalous data in actual production. This network uses a large number of normal samples and a small number of anomalous samples to detect surface cracks in stamped parts. Firstly, a pre-trained feature extractor is used for feature extraction, coupled with a designed feature adaptation network to reduce domain bias. Secondly, by extracting normal features from normal images, adding noise to these normal features to generate abnormal features, and extracting abnormal features from abnormal images at multiple scales, a feature space is constructed. Finally, by training a simplified discriminator based on the constructed feature space, computational efficiency is enhanced, and the deployment process is simplified. In the experiments, we collaborated with a multinational company, using an actual production dataset for verification. The proposed algorithm can achieve the score of area under the receiver operating characteristic curve of 98.2% for detection and 97.9% for localization at a processing speed of 19 frames per second.

冲压件在工业制造中起着至关重要的作用，其表面裂纹的自动检测尤为重要。由于裂纹的罕见性和多样性，监督缺陷检测方法缺乏足够的数据，不能达到理想的结果。无监督异常检测算法不需要裂纹数据，可以识别出未知的裂纹。然而，当处理冲压件的复杂背景时，它们往往有很高的漏检率和误报率。为了解决这些问题，本文提出了一种简单可靠的半监督异常检测网络，考虑到实际生产中存在少量异常数据。该网络使用大量的正常样本和少量的异常样本来检测冲压件的表面裂纹。首先，使用预训练的特征提取器进行特征提取，并结合设计的特征自适应网络减少域偏差；其次，从正常图像中提取正常特征，在正常特征上加入噪声生成异常特征，在多尺度上从异常图像中提取异常特征，构建特征空间；最后，基于构造的特征空间训练一个简化的判别器，提高了计算效率，简化了部署过程。在实验中，我们与一家跨国公司合作，使用实际生产数据集进行验证。该算法在处理速度为19帧/秒的情况下，检测面积得分为98.2%，定位面积得分为97.9%。

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引用次数: 0

Toward laser-assisted cutting: A real-time segmentation method for reinforcing particles in particle-reinforced metal matrix composites 走向激光辅助切割：颗粒增强金属基复合材料中增强颗粒的实时分割方法

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-25 DOI: 10.1016/j.compind.2025.104305

Jixiang Ding , Zhengding Zheng , Shayu Song , Long Bai , Jianfeng Xu , Jianguo Zhang , Wenjie Chen

Particle-reinforced metal matrix composites (PRMMCs) are widely used because of their exceptional material properties. Online control of the laser field to soften and modify the reinforcing particles on the machined surface of the composites is an effective way to improve the machinability and machining quality of PRMMCs. A real-time segmentation method for reinforcing particles in PRMMCs is proposed. First, real-time acquisition of reinforcing particle images along the processing path is achieved using machine vision, and cutting region images are determined. Next, to improve the model’s ability to effectively segment the reinforcing particles in low-resolution images of the machining region, a reinforcing particle segmentation network (RPSNet) is proposed, incorporating a multimodal fusion and space-to-depth convolution module. Subsequently, position signals along the cutting direction are obtained by using a sliding window method. The effectiveness of each module and the performance of the model are analyzed and verified through comparative and ablation experiments. The results demonstrated that the proposed RPSNet achieved a mean average precision (mAP) of 95.4 % in segmenting reinforcing particles, with an inference time of 5.8 ms. In comparison to other methods, it demonstrated better real-time performance and accuracy. Additionally, the proposed method can convert image information into position signals, thus enabling real-time control of the laser for softening and modifying the reinforcing particles.

颗粒增强金属基复合材料（PRMMCs）因其优异的材料性能而得到广泛应用。在线控制激光场对复合材料加工表面的增强颗粒进行软化和改性是提高复合材料可加工性和加工质量的有效途径。提出了一种prmmc中增强颗粒的实时分割方法。首先，利用机器视觉实现沿加工路径实时获取增强粒子图像，确定切割区域图像；其次，为了提高模型在加工区域低分辨率图像中有效分割增强粒子的能力，提出了一种结合多模态融合和空间到深度卷积模块的增强粒子分割网络（RPSNet）。随后，采用滑动窗口法获得沿切割方向的位置信号。通过对比实验和烧蚀实验对各模块的有效性和模型的性能进行了分析和验证。结果表明，RPSNet分割增强粒子的平均精度（mAP）为95.4 %，推理时间为5.8 ms。与其他方法相比，该方法具有更好的实时性和准确性。此外，该方法可以将图像信息转换为位置信号，从而实现对激光的实时控制，以软化和修饰增强颗粒。

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引用次数: 0

A task-oriented physical collaborative network for pipeline defect diagnosis in a magnetic flux leakage detection system 漏磁检测系统中管道缺陷诊断的任务导向物理协同网络

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-25 DOI: 10.1016/j.compind.2025.104290

Xiangkai Shen , Jinhai Liu , Yifu Ren , Lin Jiang , Lei Wang , He Zhao , Rui Li

Defect diagnosis based on magnetic flux leakage (MFL) signals is an important process for assessing pipeline health, including defect detection and size quantification. However, existing studies suffer from poor consistency of results, because they regard defect detection and size quantification as separate tasks, lacking paradigm harmonization and interaction. In addition, the calibration of experts is required to achieve harmonization between the two, which increases the time cost of data analysis. To address the above challenges, our motivation is to synergistically learn two tasks within a unified framework and utilize their task properties for mutual benefit. Therefore, a novel defect diagnosis method based on a task-oriented physical collaborative network (TOPC-Net) is proposed, which is the first attempt at joint defect detection and size quantification in MFL inspection. First, a feature extraction subnetwork with a heterogeneous focus module is proposed to decompose initial task-specific features from shared spaces. Second, considering the strong correlation between the two tasks, a cross-task information awareness method is proposed to realize the information interaction between the two tasks, so that the task-specific features can be enhanced. Finally, a physical information-guided collaborative decision subnetwork is proposed to jointly optimize two tasks, where MFL domain knowledge is embedded into the subnetwork to provide expert guidance, ensuring the accuracy and stability of predictions. Experimental results show that the proposed method outperforms existing methods, with a detection accuracy of 96.0% and an average improvement of 7.5% in quantification accuracy, which makes it promising for industrial applications.

基于漏磁信号的缺陷诊断是管道健康评估的重要环节，包括缺陷检测和缺陷尺寸量化。然而，现有的研究结果一致性差，因为它们将缺陷检测和尺寸量化视为独立的任务，缺乏范式协调和相互作用。此外，需要专家的校准来实现两者之间的协调，这增加了数据分析的时间成本。为了应对上述挑战，我们的动机是在一个统一的框架内协同学习两个任务，并利用它们的任务属性实现互利。为此，提出了一种基于任务导向物理协同网络（TOPC-Net）的缺陷诊断新方法，首次尝试了MFL检测中缺陷的联合检测和尺寸量化。首先，提出了具有异构焦点模块的特征提取子网络，从共享空间中分解初始任务特征；其次，考虑到两个任务之间的强相关性，提出了一种跨任务信息感知方法，实现两个任务之间的信息交互，从而增强任务特有的特征。最后，提出了一个物理信息引导的协同决策子网，将MFL领域知识嵌入到协同决策子网中，提供专家指导，保证了预测的准确性和稳定性。实验结果表明，该方法的检测精度达到96.0%，定量精度平均提高7.5%，具有较好的工业应用前景。

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引用次数: 0

Gradient-free physics-informed neural networks (GF-PINNs) for vortex shedding prediction in flow past square cylinders 无梯度物理信息神经网络（gf - pinn）用于方形圆柱体流动中旋涡脱落的预测

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-24 DOI: 10.1016/j.compind.2025.104304

Chunhao Jiang , Nian-Zhong Chen

Physics-informed neural networks (PINNs) face significant challenges to predict the vortex shedding in the flow past a two-dimensional cylinder, mainly due to complex loss landscapes, spectral bias, and a lack of inductive bias towards periodic functions. To overcome these challenges, a novel gradient-free PINN (GF-PINN) coupled with a U-Net+ + architecture is proposed. For optimizing the complex loss landscape, the skip pathways in U-Net+ + are redesigned to reduce the semantic gap between encoder and decoder feature maps. Then, the stream function instead of velocity, is used as the input and output for the neural network, ensuring flow incompressibility and reducing output dimensionality. This approach aims to overcome the inherent problems of spectral bias and the lack of inductive bias towards periodic functions in PINNs. Furthermore, gradient-free convolutional filters are employed to approximate the derivative terms in the loss function to further optimize the complex loss landscape. A series of numerical experiments and dynamic mode analyses are conducted and the results show that the vortex shedding in the wake of a square cylinder is successfully captured by the proposed model and the estimated drag coefficients and Strouhal numbers are in a good agreement with those predicted by traditional methods. In addition, numerical experiments also show that the model exhibits great capabilities of generalization and extrapolation. This work demonstrates the potential of PINN-based models to effectively solve complex fluid dynamics problems.

基于物理信息的神经网络（pinn）在预测流过二维圆柱体的流体中的涡落方面面临着重大挑战，这主要是由于复杂的损失、光谱偏倚和缺乏对周期函数的归纳偏倚。为了克服这些挑战，提出了一种新型的无梯度pin - n （GF-PINN）结合U-Net+ +架构。为了优化复杂的损失情况，重新设计了U-Net+ +中的跳过路径，以减少编码器和解码器特征映射之间的语义差距。然后，用流函数代替速度作为神经网络的输入和输出，保证了流不可压缩性，降低了输出维数。该方法旨在克服pinn中固有的频谱偏置和对周期函数缺乏归纳偏置的问题。此外，采用无梯度卷积滤波器对损失函数中的导数项进行近似，进一步优化复杂损失格局。通过一系列的数值实验和动力模态分析，结果表明，该模型成功地捕获了方形圆柱体尾迹的涡脱落，所估计的阻力系数和Strouhal数与传统方法预测的结果吻合较好。此外，数值实验还表明，该模型具有良好的泛化和外推能力。这项工作证明了基于pup模型有效解决复杂流体动力学问题的潜力。

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引用次数: 0

3D modeling from a single image via a novel dual-decoder framework for Agile design 通过敏捷设计的新型双解码器框架从单个图像进行3D建模

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-24 DOI: 10.1016/j.compind.2025.104303

Jieyang Peng , Andreas Kimmig , Simon Kreuzwieser , Zhibin Niu , Xiaoming Tao , Jivka Ovtcharova

In the fast-paced manufacturing industry, rapid and efficient product design is essential for meeting customer demands and maintaining a competitive edge. Despite advancements, transforming 2D design concepts into accurate 3D models remains a complex challenge, primarily due to the non-differentiability of traditional rendering processes that hinder gradient-based optimizations. To address this limitation, this paper introduces an innovative dual-decoder architecture that effectively separates the shape and color components of 3D models. By assigning separate decoders for vertex positions and color assignment, our proposed model enables targeted optimization of each, leading to more refined and authentic 3D reconstructions. Moreover, we have overcome the non-differentiability issue, enabling gradient-based learning through the incorporation of differentiable rendering techniques. These techniques facilitate gradient-based optimization, paving the way for data-driven enhancements in the design process. Our empirical research has demonstrated the effectiveness of our approach in generating high-fidelity 3D models from 2D inputs. Additionally, we have shed light on the sensitivity of hyperparameters within our framework, offering valuable insights for future model refinement and optimization. In summary, our research provides valuable insights into enhancing 3D modeling frameworks, thereby contributing to incremental progress in the field of computer-aided design and manufacturing.

在快节奏的制造业中，快速高效的产品设计对于满足客户需求和保持竞争优势至关重要。尽管取得了进步，但将2D设计概念转换为精确的3D模型仍然是一项复杂的挑战，主要原因是传统渲染过程的不可微分性阻碍了基于梯度的优化。为了解决这一限制，本文引入了一种创新的双解码器架构，可以有效地分离3D模型的形状和颜色组件。通过为顶点位置和颜色分配分配单独的解码器，我们提出的模型可以有针对性地优化每个解码器，从而实现更精细和真实的3D重建。此外，我们克服了不可微性问题，通过结合可微渲染技术实现基于梯度的学习。这些技术促进了基于梯度的优化，为设计过程中数据驱动的增强铺平了道路。我们的实证研究证明了我们的方法在从2D输入生成高保真3D模型方面的有效性。此外，我们还阐明了我们框架中超参数的敏感性，为未来的模型改进和优化提供了有价值的见解。总之，我们的研究为增强3D建模框架提供了有价值的见解，从而有助于计算机辅助设计和制造领域的渐进式进展。

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引用次数: 0

Interactions between BIM and robotics: Towards intelligent construction engineering and management BIM与机器人的交互：走向智能化的建筑工程与管理

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-22 DOI: 10.1016/j.compind.2025.104299

Hongzhe Yue , Qian Wang , Zixuan Zhao , Sha Lai , Guanying Huang

The interactions between robotics and Building Information Modeling (BIM) are revolutionizing the construction industry by fostering smarter, more adaptive, and efficient workflows. BIM provides robots with geometric and semantic information for precise task execution, while robots contribute real-time as-built data to update and refine BIM models. Despite its significant potential, research on BIM-robotics interactions is still in the early stages and lacks comprehensive reviews. This paper presents a detailed review of the BIM-robotics interactions in the construction industry. A two-fold literature search was conducted, resulting in the collection of 92 research papers published since 2015. Four key applications are identified: task planning, intelligent assembly, 3D printing, and inspection. Additionally, the role of robotics in facilitating BIM model generation is discussed. To address challenges such as data interoperability and the absence of standardized frameworks, this study proposes a four-layer interaction framework: Foundation Layer, Application Layer, Communication Layer, and Intelligence Layer. This framework aims to enhance BIM-robotics synergy, enabling seamless data exchange and advancing intelligent construction and management.

机器人和建筑信息模型（BIM）之间的相互作用通过培养更智能、更适应性和更高效的工作流程，正在彻底改变建筑行业。BIM为机器人提供精确执行任务的几何和语义信息，而机器人则提供实时建成数据来更新和完善BIM模型。尽管具有巨大的潜力，但对bim -机器人交互的研究仍处于早期阶段，缺乏全面的评论。本文详细介绍了bim -机器人在建筑行业中的相互作用。进行双重文献检索，收集2015年以来发表的研究论文92篇。确定了四个关键应用：任务规划、智能装配、3D打印和检测。此外，还讨论了机器人在促进BIM模型生成中的作用。为了解决诸如数据互操作性和缺乏标准化框架等挑战，本研究提出了一个四层交互框架：基础层、应用层、通信层和智能层。该框架旨在增强bim与机器人的协同作用，实现无缝数据交换，推进智能建筑和管理。

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引用次数: 0

Deformation-aware positioning optimization in aircraft assembly using surrogate model-assisted deep reinforcement learning 基于代理模型辅助深度强化学习的飞机装配变形感知定位优化

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry

Pub Date : 2025-04-21 DOI: 10.1016/j.compind.2025.104300

Yifan Zhang , Ye hu , Wenxu Luo , Qing Wang , Liang Cheng , Yinglin Ke

Assembly positioning processes play a crucial role in determining the final manufacturing precision of aircraft components. Traditional methods typically treat components as rigid bodies, focusing on adjusting their position and orientation while overlooking the complexities associated with deformable structures. This paper proposes an innovative methodology to optimize the positioning process of aircraft components by incorporating deformation considerations. A two-stage surrogate model, enhanced by machine learning techniques, is introduced to approximate the deformation of structures under various locator configurations. Deep Reinforcement Learning (DRL) is subsequently applied to leverage the surrogate model-based simulation environment. The high-dimensional stress field, compressed by the surrogate model, is used as the state input for the DRL agent, significantly reducing training complexity and enhancing stability. The agent's action corresponds to adjusting the locator's end effector position, while the reward function is designed to minimize the deformation indicator. Upon training, the resulting policy demonstrates strong generalization on the test dataset, achieving a median structural deformation reduction of 99.3 %, with 95 % of the test samples showing a reduction of over 92 %. This approach not only improves the precision but also increases the productivity of aircraft assembly, establishing a new benchmark for intelligent assembly systems that involve deformable components.

装配定位工艺在决定飞机部件的最终制造精度方面起着至关重要的作用。传统方法通常将部件视为刚体，侧重于调整其位置和方向，而忽略了与可变形结构相关的复杂性。本文提出了一种创新方法，通过考虑变形因素来优化飞机部件的定位过程。通过机器学习技术强化的两阶段代用模型被引入到各种定位器配置下结构变形的近似值中。随后，深度强化学习（DRL）被应用到基于代用模型的仿真环境中。由代理模型压缩的高维应力场被用作 DRL 代理的状态输入，从而大大降低了训练的复杂性并提高了稳定性。代理的行动对应于调整定位器的末端效应器位置，而奖励函数则旨在使变形指标最小化。经过训练后，所产生的策略在测试数据集上表现出很强的泛化能力，结构变形减少的中位数达到 99.3%，其中 95% 的测试样本的变形减少率超过 92%。这种方法不仅提高了飞机装配的精度，还提高了生产率，为涉及可变形部件的智能装配系统树立了新的标杆。

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