Human-Robot Collaborative Disassembly in A Cyber-Physical Remanufacturing System

Abstract

Remanufacturing is increasingly recognised as a pivotal technology for enhancing the lifespan and residual value of end-of-life (EoL) products. Contrasting with conventional manufacturing systems, which are highly integrated and automated, remanufacturing processes must navigate a multitude of uncertainties, including small-batch and customised production demands. Presently, the intelligence and autonomy levels within remanufacturing systems are rudimentary, offering limited support for autonomous decisionmaking and optimisation of production strategies. Thus, this dissertation aims to elevate the intelligence and dependability of the remanufacturing system, with a particular emphasis on the disassembly process as the primary area of study.

Initially, drawing inspiration from the broad application of Digital Twins (DT) and Cyber-Physical Systems (CPS) within the realm of intelligent manufacturing, this work proposes a systemic conceptual framework for a Cyber-Physical Remanufacturing System (CPRS). This framework seeks to enhance the automation, intelligence, and operational capabilities of remanufacturing systems. Subsequently, at the workshop level, to efficiently manage the disassembly of vast quantities and diverse types of EoL products, disassembly lines are introduced to boost the cost-effectiveness and productivity of these operations. This thesis introduces a novel simulated annealing-based hyper-heuristic algorithm (HH) designed for the multi-objective optimisation of the stochastic parallel complete disassembly line balancing problem. Furthermore, human-robot collaborative disassembly (HRCD), an innovative semi-automatic disassembly approach, is explored to increase flexibility and efficiency by offering multiple disassembly methods. An individual-level general ontology model for modelling EoL products is proposed, along with a rule-based reasoning method to autonomously generate optimal disassembly sequences and schemes. In addition, an analysis of disassembly sequence reliability,
leveraging a large-language model (LLM), is conducted to assess the efficacy of these disassembly sequences. The practical applicability of these case studies is demonstrated through experimental validation.

Date of Award	Apr 2024
Original language	English
Awarding Institution	Aston University
Supervisor	Yuchun Xu (Supervisor), Yu Jia (Supervisor) & Chao Liu (Supervisor)