Robotics and Computer-integrated Manufacturing最新文献

{"title":"Enhancing remanufacturing efficiency: a genetic teaching-learning-based optimisation algorithm for human-robot shared-workstation disassembly line balancing problem","authors":"Qiyao Duan ,&nbsp;Zeqiang Zhang ,&nbsp;Junyi Hu ,&nbsp;Lei Guo ,&nbsp;Wei Liang","doi":"10.1016/j.rcim.2025.103094","DOIUrl":"10.1016/j.rcim.2025.103094","url":null,"abstract":"<div><div>Human-robot collaborative technology leverages the complementary capabilities of both agents, offering diversified operational scenarios for the remanufacturing industry. In this study, the human-robot shared-workstation disassembly line balancing problem (HRSW-DLBP) is addressed, where humans and robots operate concurrently. The HRSW-DLBP facilitates the rapid release of precedence constraints on components while processing both hazardous and complex components. Recognising the prevalence of sequence-dependent setup times (SDSTs) in practical applications, this study extends the HRSW-DLBP with SDST to more accurately model real-world scenarios. The HRSW-DLBP-SDST presents a more complex challenge than its predecessors, which do not consider such setup times. Therefore, devising an effective method for solving this problem is crucial. Given the NP-hard nature of the HRSW-DLBP-SDST, this study introduces a genetic teaching-learning-based optimisation (GTLBO) algorithm tailored for large-scale problem solving, incorporating a double-layer encoding and decoding strategy informed by the characteristics of the problem and enhancing local search operator to better align with the GTLBO structure. The performance of the proposed GTLBO algorithm was benchmarked against established optimisation algorithms across four cases, demonstrating its superiority. Finally, the HRSW-DLBP-SDST was applied to a liquid crystal display TV disassembly scenario, yielding multiple optimal allocation schemes. These case studies confirm the efficacy of the proposed method in resolving the HRSW-DLBP-SDST.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103094"},"PeriodicalIF":9.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on learning an autonomous dynamic system for human–robot skills transfer from demonstration","authors":"Jiayun Fu ,&nbsp;Haotian Huang ,&nbsp;Zhehao Jin ,&nbsp;Andong Liu ,&nbsp;Wen-An Zhang ,&nbsp;Li Yu ,&nbsp;Weiyong Si ,&nbsp;Chenguang Yang","doi":"10.1016/j.rcim.2025.103092","DOIUrl":"10.1016/j.rcim.2025.103092","url":null,"abstract":"<div><div>Autonomous dynamic systems (ADS) have become a key area of research in the field of robotics, aiming to enable robots to acquire human-like operational skills and perform complex tasks in dynamic environments without external intervention. Despite significant progress, current technologies have yet to enable robots to fully achieve autonomous skill transfer in real-world applications. The prevailing approach to bridge this gap is Learning from Demonstration (LfD), where robots learn by observing and imitating expert demonstrations. Dynamic systems-based methods, particularly those utilizing Lyapunov stability theory, have shown great potential in effectively encoding human motor skills, ensuring the stability, accuracy, and generalization of learned behaviors during the learning process. This survey provides an overview of the recent advancements in dynamic systems for skill transfer, focusing on methods that enable robots to replicate human actions, as demonstrated by experts. We present a classification of existing dynamic systems approaches, highlight landmark studies, and discuss their key features, advantages, and limitations. This paper also explores the applications of these methods and identifies major challenges that remain in both theoretical and practical aspects of robot skill learning.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103092"},"PeriodicalIF":9.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable scheduling for job shops with joint maintenance, machine speed scaling, and uncertain processing times","authors":"M.N. Darghouth ,&nbsp;A.M. Attia","doi":"10.1016/j.rcim.2025.103091","DOIUrl":"10.1016/j.rcim.2025.103091","url":null,"abstract":"<div><div>This paper addresses the job shop scheduling problem by integrating preventive and corrective maintenance, uncertain processing times, and machine speed scaling under an environmental constraint. A comprehensive mixed-integer nonlinear programming (MINLP) model is formulated to optimize job scheduling, considering machine reliability, energy consumption, and carbon emissions. The model captures uncertainty in processing times, influenced by variations in machine speed, and integrates machine degradation using a Weibull distribution. A global carbon footprint constraint ensures compliance with environmental targets. This study examines the trade-offs between minimizing makespan, scheduling maintenance, and achieving sustainability objectives. Given the NP-hard nature of the job shop scheduling problem, a two-fold approach is applied for efficient solving. First, the Relax-and-Fix heuristic generates a high-quality initial solution by iteratively relaxing and fixing subsets of variables, thereby significantly accelerating the optimization process. The second step employs a branch-and-bound algorithm, which systematically explores the solution space by solving relaxed subproblems and refining integer variables. Numerical experiments validate the model, offering insights into balancing maintenance strategies, speed scaling, and energy efficiency in complex manufacturing environments. For a maximum permissible carbon footprint of 550 kg CO2, the model achieves a makespan of 18.98 h with a total carbon footprint of 514.56 kg CO2 when processing time variability is set at 5%. Results show that preventive maintenance (PM) reduces repair times, mitigates the impact of failures on makespan, and enhances machine speed adjustments, thereby optimizing energy consumption to meet environmental constraints. Sensitivity analysis reveals that higher failure rates significantly increase repair times and overall makespan. The model dynamically adjusts machine speeds to balance environmental targets with operational reliability under varying conditions. This study also derives key managerial insights for a sustainable scheduling model for job shop operating environments, emphasizing the integration of maintenance, machine speed adjustments, and the effective handling of processing time variability.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103091"},"PeriodicalIF":9.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved deep Lagragian network-enabled momentum observer for collision detection during human-robot collaboration","authors":"Yudie Hu ,&nbsp;Weidong Li ,&nbsp;Yong Zhou ,&nbsp;Duc Truong Pham","doi":"10.1016/j.rcim.2025.103093","DOIUrl":"10.1016/j.rcim.2025.103093","url":null,"abstract":"<div><div>During human-robot collaboration (HRC), robots share workplaces with humans, and there may be frequent contact between them. It is crucial to be able to detect unexpected collisions in real-time so that appropriate safety measures should be taken to avoid injuries to humans and damage to robots. However, there are challenges with existing collision detection strategies, such as the additional costs incurred in deploying sensors in robots to implement pre-collision safety surveillance solutions or conducting complicated experiments to develop post-collision compliance solutions. To address these challenges, this paper presents a new momentum observer-based collision detection approach in which the external torques caused by collisions on robots can be efficiently identified. The approach involves integrating an improved deep Lagrangian network (DeLaN) to model robot dynamics without dynamic parameter identification experiments and prior knowledge of the robot’s physical and structural parameters. Another innovation of this approach is that a compensatory safety threshold is designed to enhance collision detection accuracy. Three robot datasets were used to train the improved DeLaN model. Simulation and real-world experiments were further carried out on the proposed approach to validate the effectiveness of the approach. Comparative experiments showed that the proposed approach outperformed other momentum observers in terms of both speed and efficiency. Moreover, experiments showed that the compensatory safety threshold proposed in this approach mitigated false positives caused by friction errors in robot joints to prevent the misdetection of collisions.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103093"},"PeriodicalIF":9.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision-based extraction of industrial information from legacy Programmable Logic Controllers","authors":"Zhengyang Ling,&nbsp;Sam Brooks,&nbsp;Duncan McFarlane,&nbsp;Alan Thorne,&nbsp;Gregory Hawkridge","doi":"10.1016/j.rcim.2025.103088","DOIUrl":"10.1016/j.rcim.2025.103088","url":null,"abstract":"<div><div>Technological advancements in manufacturing are increasingly driven by connectivity and information that can be collected about manufacturing processes. Programmable Logic Controllers (PLCs) are a valuable source of process information which can help inform operations. However, many factories use legacy PLCs with restricted connection and data extraction capabilities. This paper presents a novel vision-based PLC monitoring method for extracting the input and output (I/O) states of a PLC in real time. Four case studies in industry and laboratory settings are presented; in each case study, vision-based PLC monitoring was used to extract I/O data successfully and provide data for applications such as operation monitoring, process monitoring, production counting and fault detection. Vision-based monitoring is evaluated and compared to other PLC monitoring methods using a set of key requirements. The vision-based monitoring method showed several improvements over existing PLC data extraction methods; these include no PLC control system interference, minimal disruption during installation, system security, and cost-effective design. This new vision-based PLC monitoring method has the potential to provide manufacturers with a method to retrofit PLCs to access new valuable sources of information that can be used to improve their operation or create a smart factory at a lower cost.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103088"},"PeriodicalIF":9.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grid-to-Robot: Deep Wasserstein generative modeling of robot/power grid interaction using hybrid adversarial Residual Networks","authors":"Ashkan Safari ,&nbsp;Hamed Kharrati ,&nbsp;Afshin Rahimi ,&nbsp;M. Ali Tavallaei","doi":"10.1016/j.rcim.2025.103086","DOIUrl":"10.1016/j.rcim.2025.103086","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Smart Manufacturing (SM) is an important factor for driving innovation, enhancing operational efficiency, and increasing sustainable industrial growth in an increasingly competitive and resource-constrained world. However, it faces several challenges related to increasing energy consumption and climate change. The high energy demands of connected devices and robotic manipulators increase the carbon footprint. To resolve this issue, most enterprises are now transitioning to use Renewable Energy Sources (RES), and optimizing their power and energy usage, while holding the process efficient. To fully achieve this transition, a detailed power modeling of the robotic manufacturing system is crucial and, therefore, it is important to investigate this power modeling of the robotic manipulators’ consumption in a Smart Sustainable Manufacturing (SSM) to achieve the best power modeling results and better integrability analytics in optimal power planning of the robotic systems power supply. To this end, this paper presents a deep Generative Artificial Intelligence (GAI)-based modeling of robotic manipulators’ power supply interaction with the power grid, and RES. In the proposed system, which is powered by solar energy and the power grid, a SSM equipped with ten 6-Degrees of Freedom (DoF) robotic manipulators is considered in the presence of Battery Energy Storage Systems (BESSs). Subsequently, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is employed to generate synthetic data for the system alongside the real data, thereby expanding the analytical horizons across varying operational characteristics of the system. Following this, a Residual Networks (ResNet) is developed to comprehensively analyze and predictively model the power consumption of the manipulators and their interactions with the power supply resources. Finally, the proposed hybrid GAI modeling strategy is numerically evaluated across a broad spectrum of Key Performance Indicators (KPIs) (MSE= &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, MAE= &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;mo&gt;×&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, R&lt;sup&gt;2&lt;/sup&gt;= 99.98%, MARE= &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;97&lt;/mn&gt;&lt;mo&gt;×&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, RMSPE= &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;83&lt;/mn&gt;&lt;mo&gt;×&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mtext&gt;%&lt;/mtext&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, MSRE= &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;7&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;mo&gt;×&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, RMSRE= &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;84&lt;/mn&gt;&lt;mo&gt;×&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103086"},"PeriodicalIF":9.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing industrial human action recognition framework integrating skeleton data acquisition, data repair and optimized graph convolutional networks","authors":"Bojian Liu ,&nbsp;Yufeng Yao,&nbsp;Honggang Wang ,&nbsp;Zengmin He,&nbsp;Anyang Dong","doi":"10.1016/j.rcim.2025.103089","DOIUrl":"10.1016/j.rcim.2025.103089","url":null,"abstract":"<div><div>The precise interpretation of human actions is crucial for seamless interaction and operational efficiency for industrial human-robot collaboration. However, existing skeleton-based action recognition methods focus on algorithmic applications while overlooking key challenges such as robust data acquisition, validation, and repair. Additionally, the scarcity of high-quality industrial datasets and the challenges in distinguishing similar actions further limit the capability to infer operators' intentions accurately. This paper presents a novel framework to address challenges utilizing integrating skeleton data acquisition, effective data augmentation method, and an optimized graph convolutional network. Specifically, the proposed framework employs a pose estimation method for 2D (two-dimensional) joint estimation and a 2D-to-3D (three-dimensional) lifting technique, supplemented with a robust method for repairing invalid skeleton data and a skeletal feature-based data augmentation strategy. To enhance action recognition, this paper introduces the Channel-Topology Refinement Graph Convolutional Network Plus (CTR-GCN-Plus), which incorporates dynamic topology learning and multi-channel feature aggregation, augmented with hand motion integration for finer differentiation of similar actions. The proposed framework is evaluated on an industrial assembly dataset incorporating challenging scenarios, such as occlusions and similar actions. Experimental results demonstrate that the proposed methods significantly improve accuracy, enhance recognition of similar actions, and effectively account for individual variations, outperforming existing approaches in industrial human-robot collaboration environments.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103089"},"PeriodicalIF":9.1,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A modeling and adaptive evolution method for simulation parameters of digital twin shop floor","authors":"Litong Zhang ,&nbsp;Yu Guo ,&nbsp;Shengbo Wang ,&nbsp;Guanguan Zheng ,&nbsp;Weiwei Qian ,&nbsp;Shaohua Huang ,&nbsp;Weiguang Fang","doi":"10.1016/j.rcim.2025.103090","DOIUrl":"10.1016/j.rcim.2025.103090","url":null,"abstract":"<div><div>Digital twin (DT) model can accurately predict the future state of the shop floor and promptly identify potential problems, abnormal situations, or optimization opportunities. However, traditional production simulation method without considering the temporal characteristics of entities’ attributes. In the life cycle of physical entities, its attributes’ change will increase the DT simulation parameters’ error. Therefore, deep learning algorithms are used to model and evolve the simulation parameters of the digital twin shop floor (DTSF) to improve simulation accuracy. Firstly, the interaction mechanism between deep learning and discrete event simulation is designed. Then, a sequential regression variational autoencoder (SRVAE) is proposed to model the DT temporal parameters. Furthermore, the online instructor algorithm is proposed to update SRVAE through online data. This approach improves the simulation accuracy of DTSF while allowing its parameters to be self-maintained. And the effectiveness of the proposed method is verified by a case study.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103090"},"PeriodicalIF":9.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic abrasive cloth flap wheel polishing system and multivariate parameter decision-making method for blade leading and trailing edges","authors":"Dongbo Wu ,&nbsp;Huilin Li ,&nbsp;Hui Wang ,&nbsp;Suet To","doi":"10.1016/j.rcim.2025.103075","DOIUrl":"10.1016/j.rcim.2025.103075","url":null,"abstract":"<div><div>The surface quality of the blade leading and trailing edges (LTE) impacts jet-engine performance. This study constructs a robotic abrasive cloth flap wheel (ACFW) polishing system and proposes a multivariate parameter decision-making method for the optimal polishing surface quality of the blade LTE. The robotic polishing system, including machine vision, offline programming, and constant force control, is first developed, and the blade polishing process, including blade clamping, on-machine measurement, position compensation, and polishing strategies, is then analyzed. Finally, a multivariate parameter decision-making method is proposed based on the surface roughness regression model (RM) and adaptive genetic algorithm-backpropagation (AGA-BP) network. The surface roughness RM, influencing factors, and the response curve are determined through a full factorial design (FFD) and the response surface methodology (RSM). Meanwhile, the AGA-BP network, which integrates the adaptive genetic algorithm (AGA) and backpropagation neural network (BPNN), is proposed to model and predict the roughness of the blade surface. Based on the optimal parameters, the surface roughness of the blade LTE will reach Ra = 0.142μm, which illustrates that the developed robotic polishing system is highly efficient and feasible. Furthermore, the mean error percentages of the RM, BPNN, and AGA-BP predictions are 17.946%, 9.633%, and 1.495%, respectively, for four random test datasets. The maximum error for the AGA-BP network is 1.995%, while the minimum is 0.758%. This network model can accurately predict surface roughness for the robotic polishing system of the Ti-6Al-4V blade LTE.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103075"},"PeriodicalIF":9.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient trochoidal toolpath for freeform surface polishing with uniform material removal and isotropic texture","authors":"Hai-Long Xie ,&nbsp;Qing-Hui Wang ,&nbsp;Yue-Feng Li ,&nbsp;Jing-Rong Li ,&nbsp;A.Y.C. Nee ,&nbsp;S.K. Ong","doi":"10.1016/j.rcim.2025.103087","DOIUrl":"10.1016/j.rcim.2025.103087","url":null,"abstract":"<div><div>Isotropic polished surface topography (PST) and uniform material removal (MR) are the keys to achieving good polished surface quality and favorable surface functional properties. However, few methods reported thus far can ensure obtaining uniform MR, isotropic PST, and high polishing efficiency simultaneously for freeform surfaces. Therefore, this paper presents a novel trochoidal toolpath enabling simultaneous optimization of polishing efficiency, PST isotropy, and MR uniformity for freeform surfaces. With this approach, a new polishing toolpath model is proposed by combining an adaptive trochoid and an improved Hilbert curve, which has more adjustable parameters, better multi-directionality, higher randomness, and smoothness. Then, a polishing performance index, namely “PSD (Power Spectral Density) roundness”, is proposed to quantitatively characterize the PST isotropy. Based on this, a toolpath optimization algorithm with the comprehensive consideration of polishing efficiency, MR uniformity, and PST isotropy is further proposed. Finally, the proposed method was compared with existing toolpath planning methods through simulation and physic polishing experiments to demonstrate its effectiveness and superiority. The result shows that the polishing efficiency, MR uniformity, and PST isotropy are comprehensively improved by nearly 30 %, 49 %, and 20 times respectively when compared to the existing methods, which is of great significance in improving the polished surface quality for freeform surfaces.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"97 ","pages":"Article 103087"},"PeriodicalIF":9.1,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}