IET Collaborative Intelligent Manufacturing最新文献_第4页

From Digital Twins to Immersive Manufacturing: XR and Gesture-Based Control for Enhancing Human–Robot Collaboration 从数字孪生到沉浸式制造：增强人机协作的XR和基于手势的控制

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-02-24 DOI: 10.1049/cim2.70055

Elena Manoli, Giulio Mattera, Alessandra Caggiano, Adelaide Marzano, Luigi Nele

{"title":"From Digital Twins to Immersive Manufacturing: XR and Gesture-Based Control for Enhancing Human–Robot Collaboration","authors":"Elena Manoli, Giulio Mattera, Alessandra Caggiano, Adelaide Marzano, Luigi Nele","doi":"10.1049/cim2.70055","DOIUrl":"10.1049/cim2.70055","url":null,"abstract":"Digital twins (DTs) and immersive extended reality (XR) interfaces offer new opportunities for intuitive, human-centred interaction in smart manufacturing. However, implementations often lack rigorous validation, quantitative performance analysis and assessment of scalability and robustness, especially in small-scale or resource-constrained manufacturing settings. This work proposes a modular metaverse framework integrating ROS2, Unity and Meta Quest devices to develop interactive, bidirectional DTs enhanced with gesture-based and mixed reality (MR) control. The framework is demonstrated through a lab-scale case study combining a robotic Wire Arc Additive Manufacturing (WAAM) system and a collaborative robot-based laser-cleaning station, showing broad applicability across industrial robotics. To evaluate usability, a preliminary user study with 17 participants was conducted, comparing a standard teach pendant with the proposed XR interface for a tool-inspection task. Results show an 80% reduction in programming time and significant decreases in perceived workload. Measured end-to-end gesture-to-action latency ranged from 430 to 450 ms, representing suitable timeframe for high-level interaction and task initiation. The study provides empirical user-centred evidence for metaverse-enabled interaction and discusses scalability, latency and industrial constraints. Aligned with the human-centric intelligence principles of Industry 5.0, the proposed approach improves accessibility, operator well-being and adaptability, contributing towards more inclusive and robust smart manufacturing systems.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-02-17

引用次数: 0

Development of a Digital Twin for a Bakery Line With Predictive Analytics and Adaptive Control Functions 具有预测分析和自适应控制功能的烘焙生产线数字孪生体的开发

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-02-17 DOI: 10.1049/cim2.70056

Bauyrzhan Amirkhanov, Murat Kunelbayev, Gulshat Amirkhanova, Tomiris Nurgazy, Gulnur Tyulepberdinova, Sholpan Tletay

{"title":"Development of a Digital Twin for a Bakery Line With Predictive Analytics and Adaptive Control Functions","authors":"Bauyrzhan Amirkhanov, Murat Kunelbayev, Gulshat Amirkhanova, Tomiris Nurgazy, Gulnur Tyulepberdinova, Sholpan Tletay","doi":"10.1049/cim2.70056","DOIUrl":"10.1049/cim2.70056","url":null,"abstract":"This paper presents the development and validation of a digital twin system for a bakery production line, integrating real-time sensor data, physics-based process models and advanced predictive analytics with CNN + LSTM neural networks. The proposed architecture combines logistic growth, moisture evaporation and heat transfer equations with deep learning for accurate prediction and early detection of baking defects. Simulation and pilot implementation results demonstrate that the digital twin reproduces dough volume dynamics with an error below 3%, predicts humidity within ± 2% and stabilises oven temperature in a narrow range (± 1.2°C). The intelligent system enabled a 77% reduction in unplanned equipment downtime, decreased alarm events by over 60% and reduced the share of defective products from 8% to 2%. These outcomes highlight the practical impact and scalability of the hybrid digital twin framework for improving product quality, minimising losses and enhancing process reliability in food manufacturing.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146224352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-02-13

引用次数: 0

Reinforcement Learning-Assisted Meta-Heuristics for Scheduling Job Shops With Material Handling Robots 物料搬运机器人作业车间调度的强化学习辅助元启发式算法

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-02-13 DOI: 10.1049/cim2.70054

Qi Jia, Kaizhou Gao, Naiqi Wu, Ponnuthurai Nagaratnam Suganthan

引用次数: 0

Generative AI for Sustainable Product Design: A Technology Convergence Framework Integrating Multi-Objective Optimisation and Smart Manufacturing 可持续产品设计的生成人工智能：多目标优化与智能制造的技术融合框架

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-01-21 DOI: 10.1049/cim2.70051

Huma Sikandar, Nohman Khan, Mohammad Falahat, Muhammad Imran Qureshi

{"title":"Generative AI for Sustainable Product Design: A Technology Convergence Framework Integrating Multi-Objective Optimisation and Smart Manufacturing","authors":"Huma Sikandar, Nohman Khan, Mohammad Falahat, Muhammad Imran Qureshi","doi":"10.1049/cim2.70051","DOIUrl":"10.1049/cim2.70051","url":null,"abstract":"The accelerating adoption of generative artificial intelligence (AI) is reshaping sustainable product design, yet current research remains fragmented across computational design, multi-objective optimisation, and smart manufacturing. This systematic review addresses this fragmentation by analysing 59 peer-reviewed studies (2010–2025) using PRISMA guidelines, advanced bibliometric mapping, and structural topic modelling to uncover how these domains converge to create superior sustainability outcomes. The study develops the Technology Convergence Framework, a unified theoretical model that integrates Advanced Computational Methods, Multi-Objective Optimisation, and Smart Manufacturing into an interconnected system capable of delivering emergent performance improvements. Findings show that when these domains operate synergistically—supported by mechanisms such as infrastructural maturation, empirical validation feedback loops, and standardisation-driven diffusion—manufacturers achieve 30%–65% gains in energy efficiency, waste reduction, and material optimisation, far exceeding improvements achieved through isolated technological efforts. The framework further incorporates human-AI collaboration principles aligned with Industry 5.0, emphasising the critical role of human judgement, contextual reasoning, and ethical oversight in complementing AI-driven decision systems. By bridging methodological, technological, and operational gaps, this review provides a holistic roadmap for transitioning from fragmented innovation to integrated sustainable product realisation, offering both scholars and industry leaders a coherent foundation for advancing next-generation sustainable manufacturing ecosystems.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-01-21

引用次数: 0

Mechatronics as Design Philosophy to Inspire Engineering Innovations 机电一体化作为激发工程创新的设计理念

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-01-01 DOI: 10.1049/cim2.70053

Zhuming Bi, Jianning Chi, Wenjun Zhang, Chaomin Luo

{"title":"Mechatronics as Design Philosophy to Inspire Engineering Innovations","authors":"Zhuming Bi, Jianning Chi, Wenjun Zhang, Chaomin Luo","doi":"10.1049/cim2.70053","DOIUrl":"10.1049/cim2.70053","url":null,"abstract":"The historical development of mechatronics is discussed to identify the challenges in applying existing mechatronic principles to accelerate engineering innovations in the digital era. There are emerging needs in advancing the theory of mechatronics to (1) expand design principles to promote innovations and knowledge transfer, (2) integrate rapidly developed artificial intelligence (AI), human robots interactions (HRIs) and digital technologies to design and operate complex systems and (3) develop systematic approaches to enhance a system's scalability, adaptability and sustainability. Mechatronic systems are characterised in terms of their novelties and innovations, the importance of innovative thinking in mechatronic designs is thoroughly examined and the theory of inventive problem solving (TRIZ) is incorporated to facilitate mechatronic innovations. Five system engineering (SE) methods are introduced to stimulate innovations at different development phases of mechatronic systems. Finally, project-based mechatronic design (PBMD) is proposed as a methodological framework to integrate these design methods in promoting innovations in a mechatronic design.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-01-01

引用次数: 0

Combining Simulation and Gradient Boosted Trees for the Stochastic Permutation Flowshop Scheduling Problem 结合仿真和梯度提升树的随机置换流水车间调度问题

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-12-18 DOI: 10.1049/cim2.70049

Jose M. Framinan

{"title":"Combining Simulation and Gradient Boosted Trees for the Stochastic Permutation Flowshop Scheduling Problem","authors":"Jose M. Framinan","doi":"10.1049/cim2.70049","DOIUrl":"10.1049/cim2.70049","url":null,"abstract":"In this paper, we address a stochastic variant of the well-known permutation flowshop scheduling problem, where the processing times of the jobs in the machines are assumed to be random variables. The objective considered is to minimise the expected makespan. This problem is substantially harder than its deterministic counterpart, as—except for a few special cases involving rather restrictive hypotheses—there is no closed formula to obtain the expected makespan of a given solution. Therefore, for most cases, it is necessary to estimate its expected value by sampling and averaging the results across a large number of replications. Furthermore, it has been shown that the number of replications required to obtain a reasonable estimate increases with the variability of the instance. In practice, this imposes extremely high computational costs for evaluating a solution, making it difficult to apply local search methods to instances of realistic size. Our proposal is to embed a machine learning technique (more specifically, gradient boosted trees or GBT) into a GRASP (greedy randomised adaptive search procedure) to compute a probabilistic threshold so it is possible to discard solutions with low probability of improving the actual best solution. The computational experience carried out shows that (1) the GBT is able to provide rather accurate estimates of the expected makespan even with a modest training effort and that its accuracy is not essentially influenced by the variability of the scenario and (2) the proposed procedure is able to produce the same quality of results as using the full sample of each solution, reducing the number of evaluated solutions by roughly 15%.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0