Currently, the disassembly of retired electric vehicle battery packs relies on manpower and results in high cost, low efficiency, and poor stability. With the development of artificial intelligence, automated disassembly is an efficient method to largely reduce even completely replace human disassembly. However, the various kinds of battery packs and the uncertainty on their retired numbers and types lead to frequent changes of their disassembly processes. It is necessary to provide a method that can integrate valuable disassembly knowledge to enable automated disassembly. Thus, this study proposes an automated disassembly-oriented knowledge graph for retired battery packs which considers the properties of subassemblies (entities) and explicit physical connections/implicit associations among subassemblies (relations). A large amount of unstructured data exists regarding battery packs, such as product manuals and maintenance records, whereas the knowledge that can be available to guide the disassembly process is dispersed and sparse. To solve this, a candidate entity-based relational triple joint extraction method is developed to efficiently extract the disassembly knowledge, which consists of semantic feature learning, candidate entity recognition, and explicit/implicit relational triple identification. Finally, more than 10,000 sentences collected from multi-source unstructured texts are adopted to verify the proposed method. The experimental results demonstrate that our proposed method achieves an F1-score of 93.99% in candidate entity recognition and an F1-score of 95.6% in triple extraction. Also, the information of disassembly operations, disassembly tools, and subassembly properties can be recommended by the automated disassembly-oriented knowledge graph for retired battery packs.

This study addresses the challenges of hybrid production lines, reconfigurable characteristics, frequent disturbances, and multi-resource constraints in complex aerospace product assembly and testing workshops. We propose a Dynamic Integrated Process Planning and Scheduling under Multi-Resource Constraints in Workshops with Reconfigurable Manufacturing Cells (MRC-DIPPS-RMC). By establishing an integrated mathematical model that combines process planning, cell reconfiguration, task scheduling, and resource allocation, we designed a Genetic Programming Hyper-Heuristic with Bloat Control Mechanism (GPHH-BC) based on multi-heuristic co-evolution. The algorithm employs population segmentation to co-evolve four types of heuristic rules, effectively solving five critical subproblems in dynamic environments while successfully suppressing efficiency degradation caused by rule bloating. Experimental results demonstrate that the proposed method demonstrates a 52.67 % improvement in computational efficiency compared to conventional baseline approaches while ensuring solution feasibility; when compared to state-of-the-art algorithms, it achieves a further 7.40 % improvement in computational efficiency.

Manufacturing scheduling research has often overlooked the complexities of dynamic product assembly and testing scenarios, particularly those involving reconfigurable manufacturing cells (RMCs) and the integration of process planning and scheduling. This article addresses the problem of Dynamic Integrated Process Planning and Scheduling with RMCs, a novel and complex challenge in modern manufacturing systems. A variable-fidelity surrogate-assisted hyper-heuristic algorithm is proposed, which strategically integrates process planning and scheduling tasks to reduce computation time while improving solution quality. Unlike existing methods, our approach uses surrogate models to approximate expensive evaluations, significantly enhancing computational efficiency. In experiments, our method outperformed the second-best approach by 42.4% and the least effective method by 56.6% in terms of computational efficiency, demonstrating its capability to manage dynamic scheduling and cell reconfiguration challenges in large-scale, real-world manufacturing environments.

Use of robots is rising in process applications where robots need to interact with parts using tools. Representative examples can be cleaning, polishing, grinding, etc. These tasks can be non-repetitive in nature and the physics-based models of the task performances are unknown for new materials and tools. In order to reduce operation cost and time, the robot needs to identify and optimize the trajectory parameters. The trajectory parameters that influence the performance can be speed, force, torque, stiffness, etc. Building physics-based models may not be feasible for every new task, material, and tool profile as it will require conducting a large number of experiments. We have developed a method that identifies the right set of parameters to optimize the task objective and meet performance constraints. The algorithm makes decisions based on uncertainty in the surrogate model of the task performance. It intelligently samples the parameter space and selects a point for experimentation from the sampled set by determining its probability to be optimum among the set. The iterative process leads to rapid convergence to the optimal point with a small number of experiments. We benchmarked our method against other optimization methods on synthetic problems. The method has been validated by conducting physical experiments on a robotic cleaning problem. The algorithm is general enough to be applied to any optimization problem involving black box constraints.