Laminator trust in human–robot collaboration for manufacturing fibre-reinforced composites

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2024-09-27 DOI:10.1049/cim2.12123
Laura Rhian Pickard, Michael Elkington
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Abstract

Fibre-reinforced composites manufacturing is a large and growing industry, with much of the work carried out manually by skilled human laminators. The physical nature of the work can be significantly deleterious to these workers' health, while growing demand requires increased rates of manufacture. Human–robot collaborative manufacturing offers a potential solution but requires the human to feel confident working with the robot and trust that they will be safe. Successful human trials of two different approaches to collaborative lay-up of fibre-reinforced plastic composites are presented, with tasks representative of manufacturing challenges in industry. Volunteer responses are measured by questionnaires, with users reporting the processes to be safe, simple to use and allowing greater ease of manufacturing than manual-only lay-up.

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人机协作制造纤维增强复合材料的层压机信任度
纤维增强复合材料制造是一个庞大且不断发展的行业,其中大部分工作都是由熟练的人工层压工完成的。工作的体力性质可能会严重损害这些工人的健康,而不断增长的需求又要求提高生产率。人机协作制造提供了一个潜在的解决方案,但需要人类对与机器人合作充满信心,并相信机器人会保证他们的安全。本文介绍了两种不同的纤维增强塑料复合材料协同铺层方法的成功人体试验,其任务代表了工业制造中的挑战。通过问卷调查对志愿者的反应进行了测量,结果表明,与纯手工铺层相比,协作铺层过程安全、简单易用,而且更易于制造。
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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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