Connector-link-part-based disassembly sequence planning

Under the trend of concurrent engineering, the correspondence between functions and physical structures in product design is gaining importance. Between the functions and parts, connectors are the basic unit for engineers to consider. Moreover, the relationship between connector-liaison-part will help accomplish the integration of information. Such efforts will help the development of the Knowledge Intensive CAD (KICAD) system. Therefore, we proposed a Connector-liaison-part-based disassembly sequence planning (DSP) in this study. First, the authors construct a release diagram through an interference relationship to express the priority of disassembly between parts. The release diagram will allow designers to review the rationality of product disassembly planning. Then, the cost calculation method and disassembly time matrix are established. Last, the greedy algorithm is used to find an appropriate disassembly sequence and seek suggestions for design improvement. Through the reference information, the function and corresponding modules are improved, from which the disassembly value of a product can be reviewed from a functional perspective. In this study, a fixed support holder is used as an example to validate the proposed method. The discussion of the connector-liaison-part will help the integration of the DSP and the functional connector approach.

Optimization of Disassembly Strategies for Electric Vehicle Batteries

Various studies show that electrification, integrated into a circular economy, is crucial to reach sustainable mobility solutions. In this context, the circular use of electric vehicle batteries (EVBs) is particularly relevant because of the resource intensity during manufacturing. After reaching the end-of-life phase, EVBs can be subjected to various circular economy strategies, all of which require the previous disassembly. Today, disassembly is carried out manually and represents a bottleneck process. At the same time, extremely high return volumes have been forecast for the next few years, and manual disassembly is associated with safety risks. That is why automated disassembly is identified as being a key enabler of highly efficient circularity. However, several challenges need to be addressed to ensure secure, economic, and ecological disassembly processes. One of these is ensuring that optimal disassembly strategies are determined, considering the uncertainties during disassembly. This paper introduces our design for an adaptive disassembly planner with an integrated disassembly strategy optimizer. Furthermore, we present our optimization method for obtaining optimal disassembly strategies as a combination of three decisions: (1) the optimal disassembly sequence, (2) the optimal disassembly depth, and (3) the optimal circular economy strategy at the component level. Finally, we apply the proposed method to derive optimal disassembly strategies for one selected battery system for two condition scenarios. The results show that the optimization of disassembly strategies must also be used as a tool in the design phase of battery systems to boost the disassembly automation and thus contribute to achieving profitable circular economy solutions for EVBs.

Landing gear disassembly sequence planning using multi-level constraint matrix ant colony algorithm

The life cycle of most complex engineering systems is greatly a function of maintenance. Generally, most maintenance operation usually requires the removal of failed part. Disassembly sequence planning is an optimization program that seeks to identify the optimal sequence for the removal of the failed part. Most studies in this area usually, use single constraint matrix while implementing varied complex algorithm to identify the optimal sequence that saves time associated with carrying out maintenance operation. The used of single constraint matrix typically has the drawback of computer higher storage requirement as well as time consumption. To address this problem, this study proposes Multi-Level Constraint Matrix Ant Colony Algorithm (MLCMACA). MLCMACA efficiency was validated using complex aircraft landing gear systems in comparison with genetic algorithms. The result shows MLCMACA superior performance from the perspective of reduced search time and faster tracking of optimal disassembly sequence. Hence is recommended for handling of disassembly sequence planning problems.

An Improved GA with Matrix-Coding for Optimizing a Complex Disassembly Sequence Problem on ELV

Optimal disassembly sequencing is an NP-hard problem and has always been an ambition for industry production. In the context of increasing public concerns over environmental impacts, in addition to the feasibility of a disassembly sequence, dismantling enterprises have to consider the relationship between potential profits and the impacts. Thus, an ideal disassembly sequence should weight these three factors comprehensively. Up to now, an appropriate ELV disassembly sequence still mainly relies on people’s intuitive experience and seeking an optimal disassembly sequencing method assumes enormous importance. This paper aims to address the optimal disassembly sequencing problem of ELVs by means of an improved genetic algorithm, in which a matrix coding mechanism and an elite strategy are employed. The weight of different factors can be adjusted according to the actual conditions of factories. The paper gives a case and a series of Pareto fronts are obtained. The effects of population size and maximum evolutionary time on the Pareto solutions were investigated. Ultimately, the optimal Pareto disassembly sequence corresponding to balanced profit and environmental impact is achieved, thereby providing an appropriate disassembly depth defined by the aforementioned disassembly sequence. This can contribute to timely disassembly decisions for end-of-life vehicle (ELV) dismantling enterprises, achieving a cost-effective disassembly process for survival in the context of growing environmental concerns. This paper seeks to offer a viable decision-making approach prior to real disassembly of ELVs by detailing a Pareto disassembly depth and sequence.

Objective Disassembly Sequence Planning of Used Mobile Phones Based on Improved Hybrid Graph Disassembly Model

To solve the problems of environmental pollution and waste of resources caused by used mobile phones, the study of objective disassembly sequence planning is carried out for used mobile phones. In view of the connection of mobile phone parts with multiple parts and the need to disassemble components, the concepts of containment, exclusion, and components are integrated into the hybrid graph. An improved hybrid graph is proposed and the improved hybrid graph disassembly model suitable for mobile phone disassembly is established. The ant colony algorithm is used to search for the optimal disassembly sequence, with the objective of minimum disassembly time. Finally, the improved hybrid graph disassembly model is applied to obtain the disassembly solution of HUAWEI Honor 6. The experimental results demonstrate that the disassembly sequence generated by the improved hybrid graph disassembly model can describe the actual disassembly process of disassembling components with less disassembly time.

A systematic approach in remanufacturing for high efficiency and low cost: The selective parallel disassembly sequence planning

With the growth of environmental awareness, remanufacturing and sustainable manufacturing have become hot issues. Disassembly is the first step and critical activity in remanufacturing. Traditional disassembly sequence planning (DSP) focusses on sequential disassembly. However, it is inefficient for complicated products because only one manipulator is employed to execute disassembly operations. Thus, this work focusses on parallel DSP (PDSP) and proposes a selective parallel disassembly sequence planning (SPDSP) methodology, which performs disassembly compared to sequential DSP and PDSP. In this paper, a mathematical model is used to describe the constraint and precedence relationships, and a parallel sequence model is designed for parallel disassembly. A novel hybrid genetic algorithm (NHGA) based-multi-objective model of SPDSP is proposed for optimisation. In this model, two indicators are integrated: disassembly time (including basic disassembly time, tool exchange time and direction change time) and disassembly costs. A transmission box is used as an instance, and a comparison with conventional genetic algorithm (GA), simulated annealing (SA) and tabu search (TS) is made to validate the practicality of the proposed methodology.