首页期刊导航|Robotics and computer-integrated manufacturing
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Robotics and computer-integrated manufacturing
Elsevier Science
Robotics and computer-integrated manufacturing

Elsevier Science

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0736-5845

Robotics and computer-integrated manufacturing/Journal Robotics and computer-integrated manufacturingSCIISTPEIAHCI
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    A tool wear monitoring method based on data-driven and physical output

    Yiyuan QinXianli LiuCaixu YueLihui Wang...
    102820.1-102820.15页
    查看更多>>摘要:In the process of metal cutting, realizing effective monitoring of tool wear is of great significance to ensure the quality of parts machining. To address the tool wear monitoring (TWM) problem, a tool wear monitoring method based on data-driven and physical output is proposed. The method divides two Physical models (PM) into multiple stages according to the tool wear in real machining scenarios, making the coefficients of PM variable. Meanwhile, by analyzing the monitoring capabilities of different PMs at each stage and fusing them, the PM's ability to deal with complex nonlinear relationships, which is difficult to handle, is improved, and the flexibility of the model is improved; The pre-processed signal data features were extracted, and the original features were fused and downscaled using Stacked Sparse Auto-Encoder (SSAE) networker to build a data-driven model (DDM). At the same time, the DDM is used as a guidance layer to guide the fused PM for the prediction of wear amount at each stage of the tool, which enhances the interpretability of the monitoring model. The experimental results show that the proposed method can realize the accurate monitoring of tool wear, which has a certain reference value for the flexible tool change in the actual metal-cutting process.

    Station-viewpoint joint coverage path planning towards mobile visual inspection

    Feifei KongFuzhou DuDelong Zhao
    102821.1-102821.15页
    查看更多>>摘要:Coverage path planning (CPP) has been widely studied due to its significant impact on the efficiency of automated surface quality inspection. However, these researches mostly concentrate on fixed-base visual robotic schemes, with limited focus on the widely utilized mobile-base schemes which require considerations of inherent constraints between stations (base positions) and viewpoints. Therefore, this article models a station-viewpoint joint coverage path planning problem and proposes a workflow to solve it. Within this workflow, firstly, a viewpoint selection genetic algorithm based on alternating evolution strategy is presented to optimize both the viewpoint quantity and view quality; secondly, a novel genetic algorithm is devised to accomplish joint assignment and sequence planning for stations and viewpoints. Several experimental studies are conducted to validate the effectiveness and efficiency of the proposed methods, and the proposed genetic algorithms exhibit notable superiorities compared to the benchmark methods in terms of viewpoint quantity, mean view quality, motion cost, and computational efficiency.

    Learning accurate and efficient three-finger grasp generation in clutters with an auto-annotated large-scale dataset

    Zhenning ZhouHan SunXi Vincent WangZhinan Zhang...
    102822.1-102822.13页
    查看更多>>摘要:With the development of intelligent manufacturing and robotic technologies, the capability of grasping unknown objects in unstructured environments is becoming more prominent for robots with extensive applications. However, current robotic three-finger grasping studies only focus on grasp generation for single objects or scattered scenes, and suffer from high time expenditure to label grasp ground truth, making them incapable of predicting grasp poses for cluttered objects or generating large-scale datasets. To address such limitations, we first introduce a novel three-finger grasp representation with fewer prediction dimensions, which balances the training difficulty and representation accuracy to obtain efficient grasp performance. Based on this representation, we develop an auto-annotation pipeline and contribute a large-scale three-finger grasp dataset (TF-Grasp Dataset). Our dataset contains 222,720 RGB-D images with over 2 billion grasp annotations in cluttered scenes. In addition, we also propose a three-finger grasp pose detection network (TF-GPD), which detects globally while fine-tuning locally to predict high-quality collision-free grasps from a single-view point cloud. In sum, our work addresses the issue of high-quality collision-free three-finger grasp generation in cluttered scenes based on the proposed pipeline. Extensive comparative experiments show that our proposed methodology outperforms previous methods and improves the grasp quality and efficiency in clutters. The superior results in real-world robot grasping experiments not only prove the reliability of our grasp model but also pave the way for practical applications of three-finger grasping. Our dataset and source code will be released.

    Multi-layer cutting path planning for composite enclosed cavity in additive and subtractive hybrid manufacturing

    Yin WangYukai ChenYu LuJunyao Wang...
    102823.1-102823.17页
    查看更多>>摘要:Additive and subtractive hybrid manufacturing (ASHM) refers to the hybrid manufacturing process where in-situ subtractive machining (SM) is introduced during additive manufacturing (AM). Its process characteristics dictate the necessity of planning multi-layer cutting paths in ASHM. Currently, the slice-based planning method cannot plan multi-axis cutting paths, and the machining accuracy is difficult to directly control. Meanwhile, the manual layering planning method is inefficient when dealing with complex models. Consequently, this paper presents an innovative automatic planning method for multi-layer, multi-axis, interference-free cutting paths with controllable precision in ASHM of composite enclosed cavity parts. To enhance the ASHM efficiency, criteria for the recognition of hybrid machining features (HMFs) have been defined to identify HMFs within the model. The identification of interference planes during cavity conversion has been achieved, and these interference planes are then utilized as the conversion planes for the ASHM process. Furthermore, a boundary-guided method is employed to automatically plan the overall cutting path for HMFs. According to the G-code standard, the overall cutting paths are then output to the corresponding cutting path file within the height interval of the conversion planes. Through practical machining, it has been demonstrated that the proposed method can significantly enhance the efficiency and automation of the data preparation process in ASHM, while also improving the surface quality and dimensional accuracy of the AM part.

    Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing

    Fengyi LuGuanghui ZhouChao ZhangYang Liu...
    102824.1-102824.19页
    查看更多>>摘要:Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this paper proposes an energy-efficient integration optimisation of cutting parameters and tool path with hierarchical reinforcement learning (HRL). Firstly, a novel multi-pass machining energy consumption model is developed with cutting and path parameters, based on which the integrated optimisation problem is modelled considering a dynamic workpiece deformation constraint. Secondly, HRL with a Soft Actor Critic agent (HSAC) decouples the model into two Markov Decision Processes at different timescales. The higher-layer plans cutting parameters for each pass on a macro timescale, while the micro-timescale lower-layer performs multiple tool path expansions with the planned cutting parameters, and provides feedback to the higher layer. By hierarchical optimisation and non-hierarchical interaction, the model is efficiently solved. Moreover, curriculum transfer learning is applied to expedite task completion of the lower layer, enhancing interaction efficiency between the two layers. Experiments show that, compared with two benchmarks, the proposed method improves machining energy consumption by 35.02 % and 30.92 %, and reduces machining time by 38.57 % and 27.17%, providing a promising paradigm of green practices for thin-walled freeform parts and the broader manufacturing industry.

    Hand-eye calibration method for a line structured light robot vision system based on a single planar constraint

    Kaifan ZhongJingxin LinTao GongXianmin Zhang...
    102825.1-102825.11页
    查看更多>>摘要:Hand-eye calibration is a prerequisite for robot vision system applications. However, due to the lack of image features, implementing hand-eye calibration with a line-structured light sensor is limited by complex procedures and special reference objects. The aim of this paper is to develop a stable and undemanding eye-in-hand calibration method with a 2D laser sensor that can be adapted to random measurement strategies in most common scenes. The proposed method can use an easily accessible plane from machined workpieces, except for specialized calibration objects, which facilitates the automation of industrial robots. Moreover, in this method, a two-step iterative method is combined with fast simulated annealing based on a single planar constraint to overcome large initial deviations and sensor errors. Simulations and calibration experiments are conducted to assess the method performance and demonstrate the feasibility and accuracy of the proposed eye-in-hand calibration method.

    Continuous stiffness optimization of mobile robot in automated fiber placement

    Lei MiaoWeidong ZhuYingjie GuoXiaokang Xu...
    102833.1-102833.20页
    查看更多>>摘要:The low stiffness of series robots limits their application in high-load precision manufacturing, such as automated fiber placement (AFP). This paper presents a stiffness optimization method to enhance the stiffness of plane-mobile robots in continuous fiber placement by simultaneously adjusting the robot's posture and the base position. A stiffness performance index suitable for evaluating the comprehensive stiffness of the robot during the AFP process is proposed, which is based on the fluctuation characteristics of the contact force in fiber placement. To maximize this index and the normal stiffness, the multi-objective particle swarm optimization algorithm (MOPSO) is used to solve the two-objective optimization model under multiple constraints. The constrained area of the mobile robot base corresponding to a given path point is determined by the fixed-height slice of the robot's reachable point cloud. A novel method combining global discrete solution and local continuous solution (GD-LC) is proposed to solve the model efficiently, which reduces the search dimension of the MOPSO algorithm. Experimental results from fiber placement on an aircraft mold show that the proposed method can significantly improve the stiffness performance of the AFP robot, and the force-induced deformation after continuous stiffness optimization is reduced by 70.01 % on average. The optimized laying quality further validates the engineering value of the proposed method.

    Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation

    Yuxin LiXinyu LiLiang GaoZhibing Lu...
    102834.1-102834.20页
    查看更多>>摘要:Reconfigurable manufacturing system is considered as a promising next-generation manufacturing paradigm. However, limited equipment and complex product processes add additional coupled scheduling problems, including resource allocation, batch processing and worker cooperation. Meanwhile, dynamic events bring uncertainty. Traditional scheduling methods are difficult to obtain good solutions quickly. To this end, this paper proposes a multi-agent deep reinforcement learning (DRL) based method for dynamic reconfigurable shop scheduling problem considering batch processing and worker cooperation to minimize the total tardiness cost. Specifically, a dual-agent DRL-based scheduling framework is first designed. Then, a multi-agent DRL-based training algorithm is developed, where two high-quality end-to-end action spaces are designed using rule adjustment, and an estimated tardiness cost driven reward function is proposed for order-level scheduling problem. Moreover, a multi-resource allocation heuristics is designed for the reasonable assignment of equipment and workers, and a batch processing rule is designed to determine the action of manufacturing cell based on workshop state. Finally, a strategy is proposed for handling new order arrivals, equipment breakdown and job reworks. Experimental results on 140 instances show that the proposed method is superior to scheduling rules, genetic programming, and two popular DRL-based methods, and can effectively deal with various disturbance events. Furthermore, a real-world assembly and debugging workshop case is studied to show that the proposed method is applicable to solve the complex reconfigurable shop scheduling problems.

    A resilient scheduling framework for multi-robot multi-station welding flow shop scheduling against robot failures

    Ming WangPeng ZhangGuoqing ZhangKexin Sun...
    102835.1-102835.16页
    查看更多>>摘要:With the development of intelligent manufacturing, robots are being increasingly applied in manufacturing systems due to their high flexibility. To avoid production disruptions caused by robot failures, higher requirements are imposed on the resilience of systems, specifically in terms of resistance, response, and recovery capabilities. In response to this, this paper investigates the resilient scheduling framework for multi-robot multistation welding flow shop, thereby endowing and enhancing the resilience of the system. Within the resilient scheduling framework, a proactive scheduling method maximizing resistance capability is firstly proposed based on an improved NSGA-Ⅲ with variable neighborhood search. Secondly, to improve the response and recovery capabilities of the system, a recovery scheduling method is presented. Therein, an adaptive trigger policy based on deep reinforcement learning is introduced to enhance the rapid response capability for disturbances, while the recovery optimization grants the system the ability to recover its performance that has been degraded due to the impact of disturbances. Finally, through simulation experiments and case study, it is verified that the proposed algorithms and framework possess superior performance of multi-objective optimization, which can endow the multi-robot multi-station welding flow shop with resilience to against uncertain robot failures.

    Smart and user-centric manufacturing information recommendation using multimodal learning to support human-robot collaboration in mixed reality environments

    Sung Ho ChoiMinseok KimJae Yeol Lee
    102836.1-102836.14页
    查看更多>>摘要:The future manufacturing system must be capable of supporting customized mass production while reducing cost and must be flexible enough to accommodate market demands. Additionally, workers must possess the knowledge and skills to adapt to the evolving manufacturing environment. Previous studies have been conducted to provide customized manufacturing information to the worker. However, most have not considered the worker's situation or region of interest (ROI), so they had difficulty providing information tailored to the worker. Thus, a manufacturing information recommendation system should utilize not only manufacturing data but also the worker's situational information and intent to assist the worker in adjusting to the evolving working environment. This study presents a smart and user-centric manufacturing information recommendation system that harnesses the vision and text dual encoder-based multimodal deep learning model to offer the most relevant information based on the worker's vision and query, which can support human-robot collaboration (HRC) in a mixed reality (MR) environment. The proposed recommendation model can assist the worker by analyzing the manufacturing environment image acquired from smart glasses, the worker's specific question, and the related manufacturing document. By establishing correlations between the MR-based visual information and the worker's query using the multimodal deep learning model, the proposed approach identifies the most suitable information to be recommended. Furthermore, the recommended information can be visualized through MR smart glasses to support HRC. For quantitative and qualitative evaluation, we compared the proposed model with existing vision-text dual models, and the results demonstrated that the proposed approach outperformed previous studies. Thus, the proposed approach has the potential to assist workers more effectively in MR-based manufacturing environments, enhancing their overall productivity and adaptability.