Robotic disassembly technology as a key enabler of autonomous remanufacturing
机器人拆卸技术是自主再制造的关键推动者
基本信息
- 批准号:EP/N018524/1
- 负责人:
- 金额:$ 247.86万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2016
- 资助国家:英国
- 起止时间:2016 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Remanufacturing is "the process of returning a used product to at least OEM original performance specification from the customers' perspective and giving the resultant product warranty that is at least equal to that of a newly manufactured equivalent". Remanufacturing can be more sustainable than manufacturing de novo "because it can be profitable and less harmful to the environment ...". Remanufacturing is a sizable industry. For example, in the USA, there are more than 73,000 companies engaged in remanufacturing. They employ 350,000 people and have turnovers totalling $53 billion. A key step in remanufacturing is disassembly of the returned product to be remanufactured. As it is complex, disassembly tends to be manually executed and is labour intensive. We propose to develop robotic technology allowing disassembly to be carried out with minimal human intervention or in a collaborative fashion by man and machine. We aim to facilitate the cost-effective robotisation of this critical step in remanufacturing to unlock the potential of remanufacturing and make it feasible for many more companies to adopt, thus helping to expand the UK's £2.35 Billion remanufacturing industry. Our research will start with a detailed investigation of disassembly processes aimed at fundamentally understanding them. Such a fundamental understanding does not currently exist but is necessary to support the development of robust disassembly strategies and systems that can autonomously handle variability in the product. We will study basic common tasks such as unscrewing, removal of pins from holes with small clearances, separation of press-fit components, extraction of elastic parts (e.g. O-rings and circlips) and breaking up of 'permanently' assembled components. We will analyse those generic disassembly tasks for feedback information that can be obtained while a robot is performing them. We will employ different types of sensors to provide feedback appropriate to a given task. In addition to visual sensing, we will focus on using contact forces and moments as a means to gauge the state of the disassembly operation. To counteract uncertainties, such feedback will be helpful in guiding the robot and avoiding damage to the components being taken apart. We will apply the acquired basic process knowledge methodically to create models, scheduling algorithms and learning tools to enable autonomous or semi-autonomous disassembly by robotic systems. We will develop strategies for planning and implementing multi-robot operation when the disassembly task is too complex for one machine. We will devise techniques for effective collaboration between humans and robots in cases where the work is too difficult for people or for machines on their own. We will validate these plans, strategies and techniques experimentally and will give public demonstrations of collaborative robotic disassembly using real products as examples. Our multi-disciplinary project team, with experience in robotic assembly, intelligent systems, CAD/CAM and process modelling, will be supported by three industrial partners (Caterpillar, Meritor and MG Motor). These user companies will supply case studies for evaluating the research results. Two technology translators (the Manufacturing Technology Centre and the High Speed Sustainable Manufacturing Institute) will contribute to converting laboratory-based technology into solutions ready for deployment on an industrial scale.
再制造是“从客户的角度将旧产品至少恢复到原始设备制造商的原始性能规格,并给予最终产品至少相当于新制造的同等产品的保修的过程”。再制造可以比从头制造更可持续,因为它可以盈利,对环境的危害较小。".再制造是一个规模庞大的产业。例如,在美国,有超过73,000家公司从事再制造。它们雇用了35万人,营业额总计530亿美元。再制造过程中的一个关键步骤是对返回产品进行拆卸再制造。由于拆卸过程复杂,往往需要人工完成,而且劳动强度大。我们建议开发机器人技术,使拆卸进行最少的人为干预或在一个合作的方式由人和机器。我们的目标是促进再制造这一关键步骤的成本效益机器人化,以释放再制造的潜力,并使更多的公司能够采用,从而帮助扩大英国23.5亿英镑的再制造产业。我们的研究将从一个详细的调查,旨在从根本上了解他们的拆卸过程。这种基本的理解目前还不存在,但有必要支持强大的拆卸策略和系统,可以自主处理产品的变化的发展。我们将学习基本的常见任务,如拧松、从小间隙孔中取出销、分离压配合部件、取出弹性部件(如O形圈和弹性挡圈)以及分解“永久”组装部件。 我们将分析这些通用的拆卸任务的反馈信息,可以获得,而机器人正在执行他们。我们将采用不同类型的传感器来提供适合于给定任务的反馈。除了视觉传感,我们将重点关注使用接触力和力矩作为衡量拆卸操作状态的一种手段。为了抵消不确定性,这种反馈将有助于引导机器人并避免损坏正在拆卸的组件。我们将应用所获得的基本工艺知识,有条不紊地创建模型,调度算法和学习工具,使自主或半自主拆卸机器人系统。 当拆卸任务对于一台机器来说过于复杂时,我们将制定规划和实施多机器人操作的策略。我们将设计出人类和机器人之间有效协作的技术,以应对人类或机器本身难以完成的工作。我们将通过实验验证这些计划、策略和技术,并以真实的产品为例,公开演示协作机器人拆卸。我们的多学科项目团队在机器人装配、智能系统、CAD/CAM和工艺建模方面拥有丰富的经验,将得到三个工业合作伙伴(卡特彼勒、Meritor和MG Motor)的支持。这些用户公司将提供案例研究,以评估研究结果。两个技术翻译机构(制造技术中心和高速可持续制造研究所)将致力于将实验室技术转化为可在工业规模上部署的解决方案。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Constraint Handling Methods for Resource-Constrained Robotic Disassembly Line Balancing Problem
- DOI:10.1088/1742-6596/1576/1/012039
- 发表时间:2020-06
- 期刊:
- 影响因子:0
- 作者:Yilin Fang;Hongliang Xu
- 通讯作者:Yilin Fang;Hongliang Xu
Predictive exposure control for vision-based robotic disassembly using deep learning and predictive learning
- DOI:10.1016/j.rcim.2023.102619
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:Wupeng Deng;QUAN LIU;D. Pham;Jiwei Hu;Kin-Man Lam;Yongjing Wang;Zude Zhou
- 通讯作者:Wupeng Deng;QUAN LIU;D. Pham;Jiwei Hu;Kin-Man Lam;Yongjing Wang;Zude Zhou
Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time
- DOI:10.1080/00207543.2019.1602290
- 发表时间:2019-04
- 期刊:
- 影响因子:9.2
- 作者:Yilin Fang;Hao Ming;Miqing Li;Quan Liu;D. Pham
- 通讯作者:Yilin Fang;Hao Ming;Miqing Li;Quan Liu;D. Pham
Robotic Task Oriented Knowledge Graph for Human-Robot Collaboration in Disassembly
- DOI:10.1016/j.procir.2019.03.121
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Ding Yiwen;Wenjun Xu;Zhihao Liu;Zude Zhou;D. Pham
- 通讯作者:Ding Yiwen;Wenjun Xu;Zhihao Liu;Zude Zhou;D. Pham
Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations
- DOI:10.1016/j.ejor.2018.12.035
- 发表时间:2019-07-01
- 期刊:
- 影响因子:6.4
- 作者:Fang, Yilin;Liu, Quan;Duc Truong Pham
- 通讯作者:Duc Truong Pham
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Duc Pham其他文献
State-of-Matter-Dependent Charge-Transfer Interactions between Planar Molecules for Doping Applications
- DOI:
10.1021/acs.chemmater.8b01447 - 发表时间:
2019-02-26 - 期刊:
- 影响因子:8.6
- 作者:
Beyer, Paul;Duc Pham;Opitz, Andreas - 通讯作者:
Opitz, Andreas
Prediction of springback in multi-point forming
- DOI:
10.1080/23311916.2017.1400507 - 发表时间:
2017-11-13 - 期刊:
- 影响因子:1.9
- 作者:
Elghawail, Ali;Essa, Khamis;Duc Pham - 通讯作者:
Duc Pham
COMPARISON OF MONITORED ANESTHESIA CARE AND GENERAL ANESTHESIA FOR TRANSCATHETER AORTIC VALVE REPLACEMENT (TAVR)
- DOI:
10.1016/s0735-1097(18)31764-9 - 发表时间:
2018-03-10 - 期刊:
- 影响因子:
- 作者:
Olga Kislitsina;Danielle Smith;Duc Pham;Andrei Churyla;Mark Ricciardi;Ranya Sweis;Charles Davidson;James Flaherty;Jane Kruse;Patrick McCarthy;S. Chris Malaisrie - 通讯作者:
S. Chris Malaisrie
RIGHT VENTRICULAR FUNCTION AND COMORBIDITIES LIMIT IMPROVEMENTS IN QUALITY OF LIFE WITH LEFT VENTRICULAR ASSIST DEVICE THERAPY
- DOI:
10.1016/s0735-1097(12)61022-5 - 发表时间:
2012-03-27 - 期刊:
- 影响因子:
- 作者:
Michael S. Kiernan;Duc Pham;Navin Kapur;Naveen Pereira;Kartik Sundareswaran;David Farrar;Martin Stueber;David DeNofrio;Joseph Rogers - 通讯作者:
Joseph Rogers
HSD26 Cancer Survivors and Opioid Use During Survivorship - A Scoping Review
- DOI:
10.1016/j.jval.2025.04.922 - 发表时间:
2025-07-01 - 期刊:
- 影响因子:6.000
- 作者:
Albert M. Truong;Duc Pham;Trevor Sproule;Erica R. Brody;Vasco M. Pontinha - 通讯作者:
Vasco M. Pontinha
Duc Pham的其他文献
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{{ truncateString('Duc Pham', 18)}}的其他基金
Automated Manufacturing Process Integrated with Intelligent Tooling Systems (AUTOMAN)
自动化制造流程与智能工装系统集成 (AUTOMAN)
- 批准号:
EP/L505225/1 - 财政年份:2014
- 资助金额:
$ 247.86万 - 项目类别:
Research Grant
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