IET Collaborative Intelligent Manufacturing最新文献_第3页

Dual-Strategy Collaborative Reinforcement Learning for Path Planning and Optimisation in Concrete 3D Printing 混凝土3D打印路径规划与优化的双策略协同强化学习

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-04-30 DOI: 10.1049/cim2.70061

Dan Xu, Jiaxin Yuan, Meiting Xin, Zongfang Ma

{"title":"Dual-Strategy Collaborative Reinforcement Learning for Path Planning and Optimisation in Concrete 3D Printing","authors":"Dan Xu, Jiaxin Yuan, Meiting Xin, Zongfang Ma","doi":"10.1049/cim2.70061","DOIUrl":"https://doi.org/10.1049/cim2.70061","url":null,"abstract":"As concrete 3D printing emerges as a transformative technology in construction, optimising printing path planning has become crucial for enhancing manufacturing efficiency and structural integrity. Conventional path planning methods often struggle with complex geometries, leading to discontinuous paths, excessive directional changes and inefficient start–stop cycles. To address these limitations, this study introduces a novel reinforcement learning (RL)–based framework incorporating a dual-strategy coordination mechanism. The proposed approach employs two specialised deep Q-network (DQN) models: one for continuous path optimisation and the other for start–stop sequence refinement. The state and action spaces are designed based on sliced geometric features of concrete components, while a multi-objective reward function penalises nonprintable movements, abrupt turns and unnecessary interruptions. In addition, a path merging strategy and a G-code generation module are incorporated to ensure path continuity and operational feasibility. Experimental evaluations across diverse complex topological structures demonstrate that the proposed approach achieves complete edge coverage, minimises idle motions and path oscillations, and significantly improves printing efficiency and geometric fidelity. The results underscore the potential of the dual-strategy RL method as an intelligent and viable solution for advanced additive manufacturing path planning.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148100292","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

Development of Tool Wear Detection System Using Smartphone and CNN With Kalman-Based Noise Reduction 基于智能手机和CNN的刀具磨损检测系统的开发

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-04-23 DOI: 10.1049/cim2.70060

Mustafa H. Hadi, Hussam L. Alwan, Atiya Al-Zuheri

{"title":"Development of Tool Wear Detection System Using Smartphone and CNN With Kalman-Based Noise Reduction","authors":"Mustafa H. Hadi, Hussam L. Alwan, Atiya Al-Zuheri","doi":"10.1049/cim2.70060","DOIUrl":"https://doi.org/10.1049/cim2.70060","url":null,"abstract":"The development of a reliable and cost-effective detection system of tool wear is essential for monitoring the machining process. Researchers in recent years have proposed many methods to implement tool condition monitoring (TCM) with the use of timeseries signals acquired by high resolution sensors. However, the high installation and operation costs of these sensors incurs high difficulties for small and medium sized enterprises, whereas using a low-cost alternative like the smartphone is a viable option. This work introduces a smartphone-enabled approach that utilises the smartphone vibration sensor with deep learning approach to detect the tool wear in two categories: Fresh and Worn. An extended Kalman filter noise reduction technique is applied to reduce undesired noise effects. Then, the Euclidean norm (l2-norm) is applied to determine the overall magnitude of vibration and the resulted data is z-normalisation and transformed by using the sliding window method into a suitable input shape for the 1D CNN model. The implemented method is validated with real-world datasets from the milling machining of Al 6061.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148090678","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

Challenges to Industry 4.0 Implementation: Identification and Modelling for Smart and Sustainable Production 工业4.0实施的挑战：智能和可持续生产的识别和建模

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-04-23 DOI: 10.1049/cim2.70059

Rupen Trehan, Kuldip Singh Sangwan, Perminderjit Singh, Narpat Ram Sangwa, Shubham Sharma, Krishna Prakash Arunachalam, Mohamed Abbas, Krishnaraj Ramaswamy

{"title":"Challenges to Industry 4.0 Implementation: Identification and Modelling for Smart and Sustainable Production","authors":"Rupen Trehan, Kuldip Singh Sangwan, Perminderjit Singh, Narpat Ram Sangwa, Shubham Sharma, Krishna Prakash Arunachalam, Mohamed Abbas, Krishnaraj Ramaswamy","doi":"10.1049/cim2.70059","DOIUrl":"https://doi.org/10.1049/cim2.70059","url":null,"abstract":"This study identifies and models the key challenges that hinder Industry 4.0 (I4.0) adoption, supporting organisations in prioritising mitigation efforts while advancing sustainable production goals. A review of the literature led to the selection of 13 major challenges and 57 related sub-challenges. Using Interpretive Structural Modelling (ISM), an eight-level hierarchical structure was developed to map the interrelationships among these challenges. MICMAC analysis indicates that most challenges are linkage variables, characterised by strong interdependence and high system sensitivity. The Interpretive Ranking Process (IRP) was employed to rank the challenges based on their dominance across performance dimensions. Results from both ISM and IRP reveal that insufficient advanced technologies and supporting processes constitute the most critical barrier to I4.0 implementation. The combined ISM–IRP framework offers a practical, decision-oriented tool that enables managers to identify priority areas, allocate resources effectively, and design more coherent strategies for I4.0 adoption in alignment with organisational needs.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148090677","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 : 2026-04-23

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-04-23

引用次数: 0

The Concept of Multi-Level Dynamic Employee Value in Agile Project-Oriented Maritime Logistics Enterprises 面向敏捷项目的海运物流企业多层次动态员工价值概念

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-03-30 DOI: 10.1049/cim2.70058

Svitlana Onyshchenko, Oleksiy Melnyk, Aditya Rio Prabowo, Vira Andriievska, Olena Korobkova, Inna Khodikova, Mykyta Yurganov

{"title":"The Concept of Multi-Level Dynamic Employee Value in Agile Project-Oriented Maritime Logistics Enterprises","authors":"Svitlana Onyshchenko, Oleksiy Melnyk, Aditya Rio Prabowo, Vira Andriievska, Olena Korobkova, Inna Khodikova, Mykyta Yurganov","doi":"10.1049/cim2.70058","DOIUrl":"10.1049/cim2.70058","url":null,"abstract":"The article presents the concept of multi-level dynamic employee value (MLDEV) as an innovative approach to human resource management in flexible project-oriented maritime logistics enterprises. The purpose of this study is to develop a methodological framework for integrating human capital into digital management systems operating within the Industry 5.0 paradigm, with a focus on human-centred development. The paper proposes a model that combines three levels of assessment—individual, team and organisational, allowing to track the dynamics of employee performance over time. The key parameters (competence, motivation, adaptability, synergy of interaction and innovation) and relevant indicators that form an integrated assessment of the value of personnel are identified. Particular attention is paid to the digital implementation of the model through artificial intelligence tools, HR analytical dashboards and management decision support systems that provide forecasting of personnel risks and increase the efficiency of organisational interaction. The practical significance of the study lies in the formation of a scientific basis for the creation of intelligent human resource management systems in maritime logistics companies focused on adaptability, innovation and sustainable development.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666365","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 : 2026-03-30

引用次数: 0

Role of Machine Learning and Big Data Analytics in Enhancing the Efficiency of Gas Metal Arc Welding Process 机器学习和大数据分析在提高气体金属弧焊工艺效率中的作用

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-03-27 DOI: 10.1049/cim2.70057

Ahmed Sharaf, Noha M. Hassan

{"title":"Role of Machine Learning and Big Data Analytics in Enhancing the Efficiency of Gas Metal Arc Welding Process","authors":"Ahmed Sharaf, Noha M. Hassan","doi":"10.1049/cim2.70057","DOIUrl":"10.1049/cim2.70057","url":null,"abstract":"Gas Metal Arc Welding is a widely used process in manufacturing due to its versatility, speed, and cost-effectiveness. However, variations in process parameters can lead to quality issues affecting production efficiency and increasing rework costs. Optimising these parameters is essential to ensure high-quality welds, reduce material waste and improve overall manufacturing productivity. Traditional optimisation models are often limited by specific welding conditions and constrained experimental data. This research employs machine learning and big data analytics to develop a generalised optimisation model for Gas Metal Arc Welding, leveraging literature data to overcome experimental limitations. Data preprocessing, including imputation for missing values, was applied to enhance data quality. Supervised machine learning algorithms were compared for predicting key weld characteristics, including tensile strength, penetration, and weld width. A Medium Gaussian SVM model predicted weld tensile strength with 85.60% accuracy, while an interactions linear model achieved 80.40% accuracy for weld penetration. A stepwise linear regression model provided 95.80% accuracy for weld width prediction. By optimising machine learning models for different manufacturing scenarios, this study offers a data-driven approach to parameter selection, improving weld quality and operational efficiency. The findings bridge machine learning-based welding optimisation and industrial applications, supporting data-driven decision-making for enhanced production performance.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666248","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 : 2026-03-27

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-02-24

引用次数: 0