Service Platform for Robotic Disassembly Planning in Remanufacturing

Remanufacturing is being paid much attention due to environmental protection and resource re-utilization. Disassembly is an inevitable step of remanufacturing and it is always finished by the manual labor. Robotic disassembly helps to improve the automation of disassembly process, while robotic disassembly planning helps to improve the efficiency of robotic disassembly. However, the existing research on robotic disassembly planning seldom integrates the physical industrial robots and then provides convenient services for the users. In this paper, the framework of service platform for robotic disassembly planning in remanufacturing is studied. The database of the service platform is designed, and the data communication method between the software system and KUKA industrial robots is studied. After that, based on the physical facilities, this service platform is implemented using MyEclipse software. The developed service platform is convenient for the users to access the methods of robotic disassembly planning and the connected industrial robots can also execute the optimal disassembly solutions through the data communication method after the optimal disassembly solutions are obtained. Finally, case study based on an idler shaft is conducted to verify the developed service platform for robotic disassembly planning.     

Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing✰

Remanufacturing helps to reduce manufacturing cost and environmental pollution by reusing end-of-life products. Disassembly is an inevitable process of remanufacturing and it is always finished by manual labor which is high cost and low efficiency while robotic disassembly helps to cover these shortages. Before the execution of disassembly, well-designed disassembly sequence and disassembly line balancing solution help to improve disassembly efficiency. However, most of the research used for disassembly sequence planning and disassembly line balancing problem is only applicable to manual disassembly. Also, disassembly sequence planning and disassembly line balancing problem are separately studied. In this paper, an improved discrete Bees algorithm is developed to solve the collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem. Robotic workstation assignment method is used to generate robotic disassembly line solutions based on feasible disassembly solutions obtained by the space interference matrices. Optimization objectives of the collaborative optimization problem are described, and the analytic network process is used to assign suitable weights to different indicators. With the help of variable neighborhood search, an improved discrete Bees algorithm is developed to find the optimal solution. Finally, based on a gear pump and a camera, case studies are used to verify the effectiveness of the proposed method. The results under different cycle time of robotic disassembly line are analyzed. Under the best cycle time, the performance of the improved discrete Bees algorithm under different populations and iterations are analyzed and compared with the other three optimization algorithms. The results under different assessment methods and scenarios are also analyzed.     

A Decision Maker-Centered End-of-Life Product Recovery System for Robot Task Sequencing

End-of-life (EOL) disassembly focuses on regaining the value embodied in products which are considered to have completed their useful lives due to a variety of reasons such as lack of technical functionality and/or lack of demand. Disassembly is known to possess unique characteristics due to possible changes in the EOL product structure and hence, cannot be considered as the reverse of assembly operations. With similar reasoning, obtaining a near-optimal/optimal disassembly sequence requires intelligent decision making during the disassembly when the sequence needs to be regenerated to accommodate these unforeseeable changes. That is, if one or more components which were included in the original bill-of-material (BOM) of the product is missing or if one or more joint types are different than the ones that are listed in the original BOM, the sequence needs to be able to adapt and generate a new and accurate alternative for disassembly. These considerations require disassembly sequencing to be solved by more powerful and versatile methodologies justifying the utilization of image detection technologies for online real-time disassembly while imposing search techniques which would provide more efficient solutions than their exhaustive search counterparts. Therefore, EOL disassembly sequencing literature offers a variety of heuristic techniques. As with any data driven technique, the performance of the proposed methodologies is heavily reliant on the accuracy and the flexibility of the algorithms and their abilities to accommodate several special considerations such as preserving the precedence relationships during disassembly while obtaining near-optimal or optimal solutions. This study builds on previous disassembly sequencing research and introduces an automated robotic disassembly framework for EOL electronic products. The model incorporates decision makers’ (DMs’) preferences into the problem environment for efficient material and component recovery. A numerical example is provided to demonstrate the functionality of the proposed approach.     

Selective disassembly for virtual prototyping as applied to de-manufacturing

Selective disassembly involves separating a selected set of components from an assembly. Applications for selective disassembly include de-manufacturing (maintenance and recycling), and assembling. This paper presents a new methodology for performing design for selective disassembly analysis on the CAD model of an assembly. The methodology involves the following three steps: (i) identifying the components to be selectively disassembled for de-manufacturing by a software program or designer, (ii) determining an optimal (e.g. minimal cost) disassembly sequence for the selected components that involves a computationally efficient two-level reduction procedure: (a) the determination of a set of sequences with an objective of minimal component removals via a wave propagation approach that topologically order components in an assembly for selective disassembly, and (b) the evaluation of resulting sequences based on an objective function (e.g. minimal cost) to identify an optimal sequence, and (iii) Performing disassembly design decisions based on the evaluated optimal sequence. Preliminary implementation results of the selective disassembly methodology in sequencing and disassembly cost evaluation, and application of the selective disassembly technique for de-manufacturing assessment are presented.     

Disassembly Sequencing Using Tabu Search

End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included     

基于蚁群优化算法的目标拆卸序列规划

为了能够以较高的效率求解出产品中目标零件的拆卸方案,基于产品中零件间的拆卸优先约束关系,提出并建立目标零件的拆卸层次信息图模型,将目标零件的拆卸序列规划问题转化为对该图模型中具备最优值的路径的搜索和寻优问题.同时,提出一种改进蚁群优化算法,以实现对目标零件拆卸层次信息图的构建和对拆卸方案的搜索与寻优.最后通过实例验证了该方法的可行性和计算效率.     

Predictive exposure control for vision-based robotic disassembly using deep learning and predictive learning

Lighting conditions can affect the performance of vision-based robots in manufacturing. This paper presents a predictive exposure control method that allows the acquisition of high-quality images in real time under poor lighting conditions. This technique is particularly useful in robotic disassembly where a fixed and optimised lighting environment is difficult to construct due to the uncertain conditions of used components, and the optimal exposure conditions for each used component are different. We first develop a region-of-interest (ROI) extraction module capable of identifying ROIs under poor light exposure, in which the states of captured images under various lighting conditions are hypothesised to enhance the extraction ability of a deep learning-based object detector. The extraction results can help a robot obtain an optimal capture position and are incorporated with information about entropy to assess the image quality of ROIs in the proposed ROI quality assessment module. We further design an exposure-entropy prediction model based on predictive learning. This lightweight model is crucial in assisting the exposure time prediction module to achieve real-time searching for the optimal exposure time. The performance of the proposed exposure control method is validated using a screw-removal case study in the application to end-of-life electric vehicle battery disassembly. Together with the ROI extraction module and the ROI quality assessment module, the exposure time prediction module enables the accurate and efficient estimation of optimal exposure time and delivers high-quality images under poor lighting conditions. With our exposure control method, the robot vision system achieves satisfactory performance in the robotic disassembly of electric vehicle batteries.     

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete Bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.     

Integrated multi-layer representation and ant colony search for product selective disassembly planning

Selective disassembly plays an important role in product life cycle to meet requirements of the product repairing, reusing and recycling. An efficient disassembly plan is essential to minimize processing time in product maintenance for cost saving. This paper introduces a method for integration of the multi-layer product representation and the optimal search in product selective disassembly planning. The multi-layer representation is based on the product structure formed in product design. The method enables an efficient search for the disassembly sequence. Unlike the existing product representation methods, the multi-layer representation is a dynamic product data model integrated with an ant colony search process for a near optimal solution. Industrial applications have proven the method effectiveness.     

Optimisation of compressed air and electricity consumption in a complex robotic cell

Energy conservation of a complex robotic cell can be achieved in many different ways. Parameters related to different utilities that are used in complex robotic cell could be easily varied. Various utilities and various parameters should be observed as a part of an integral approach. Naturally, it is required to have in mind the productivity and efficiency of the entire robotic cell. This study presents the optimisation of a complex robotic cell with installed electric and pneumatic devices. The presented methodology involves three key steps: (1) an identification of possible approaches in complex robotic cell optimisation; (2) an identification of parameters of influence on electricity and compressed air consumption; and (3) experimental evaluation of the proposed approaches with a recommendation for the optimal one, meeting the requirements of maximal productivity and energy efficiency.     

Disassembly information model incorporating dynamic capabilities for disassembly sequence generation

Industrial recycling and reusing is becoming more and more important due to the environmental and economic pressures. It involves disassembly activities to retrieve all the parts or selected parts. An information modeling for the disassembly and optimal disassembly sequence generation based on the information model becomes critical. Unlike the traditional graph based representation of product structure, this paper introduces an efficient and machine readable disassembly information model and then discusses a linear programming based optimization model for obtaining the optimal disassembly sequence from the proposed disassembly information model. A key feature of this approach is the incorporation and use of dynamic capabilities in its information model processing technique. Dynamic capabilities are added into the information model to handle state-dependent information such as parts' disassembly directions which may change after each disassembly operation. The overall information model is built in UML, and dynamic capabilities are represented as events in UML. The proposed method has been illustrated using an electrical–mechanical device.     

Automated generation and execution of disassembly actions

Product variation and the unavailability of end-of-life product and condition information makes automation of disassembly a significant challenge. Several research attempts have been made employing different techniques to approach uncertainties in the disassembly process automatically, but it has made little influence on industrial applications. This research presents a robotic disassembly system aimed at addressing these issues. The system is tested on disassembly of various models of LCD screens with promising results, limited by sensing and model inaccuracies which were able to be resolved by user intervention. Specific product information obtained from user demonstration is supplemented with background knowledge relating actions to the fasteners to be disestablished. This goal-driven approach allows the substitution of dissimilar tools––namely a screwdriver, hole saw, and angle grinder––along with flexible reactive planning in the case of operation failures. A method of assessing and selecting desirable disassembly actions based on geometrical estimations of destructiveness is implemented. Finally, an improved method for the evaluation of systems employing (semi-) destructive disassembly is proposed.     

Automatic cooperative disassembly robotic system: Task planner to distribute tasks among robots

This paper presents a task planner based on decision trees. Two different types of cooperative tasks are described: common task and parallel task. In the first type of task two or more robots are required to accomplish the task. In the second type, several tasks can be performed in parallel by different robots to reduce the total disassembly time. The planner presented is based on a hierarchical representation of the product and performs the distribution of the tasks among robots using decision trees. The system takes into consideration the work area of each robot and its own characteristics. The work cell can be composed of j robotic manipulators. Finally, a practical application of a PC disassembly system is shown.     

Vision-based control of an autonomous disassembly station

In the EU about 10 millions of used cars have to be wrecked per year. So the disposal of these cars is of increasing importance in the next years. In a disassembly process for used cars one has to cope with the objects in an undefined state in contrast to the car production. For this purpose the use of vision-based control is motivated and a vision guided robotic system for an autonomous disassembly process is presented in this paper. An active stereo camera system is used as vision sensor. A combination of gray value and contour-based object recognition and a position measurement approach with implicit detection of occlusions is described. A system for the autonomous disassembly of wheels was successfully tested and shown in a final presentation. In this demonstrator a stereo vision system, a force–torque sensor, a task planning module, and a special unscrewing tool were integrated.     

Learning and revision in cognitive robotics disassembly automation

Disassembly is a key step for an efficient treatment of end-of-life (EOL) products. A principle of cognitive robotics is implemented to address the problem regarding uncertainties and variations in the automatic disassembly process. In this article, advanced behaviour control based on two cognitive abilities, namely learning and revision, are proposed. The knowledge related to the disassembly process of a particular model of product is learned by the cognitive robotic agent (CRA) and will be implemented when the same model has been seen again. This knowledge is able to be used as a disassembly sequence plan (DSP) and disassembly process plan (DPP). The agent autonomously learns by reasoning throughout the process. In case of an unresolved condition, human assistance is given and the corresponding knowledge will be learned by demonstration. The process can be performed more efficiently by applying a revision strategy that optimises the operation plans. As a result, the performance of the process regarding time and level of autonomy are improved. The validation was done on various models of a case-study product, Liquid Crystal Display (LCD) screen.     

基于目标拆卸的虚拟拆装过程建模

针对目前基于层次关系模型和关联关系模型的虚拟拆装系统中未考虑目标拆卸的 问题,在原有层次关系模型基础上引入跳跃拆卸路径的概念,对基于层次关系模型和关联关系 模型的拆装结构模型进行了完善和优化。对引入跳跃拆卸路径后的拆卸模型进行序列规划,并 针对拆装决策过程中拆装单元的组合与还原问题进行论述,最后建立基于关联关系模型的目标 拆卸算法。以船用分油机进行实例验证,验证结果表明该模型能够很好地解决目标拆卸的问题, 对相关应用系统开发具有一定的参考价值。     

Modelling and control of an assembly/disassembly mechatronics line served by mobile robot with manipulator

The aim of this paper is to reverse an assembly line using a mobile platform equipped with a manipulator. By reversibility we mean that the line is able to perform disassembly. For this purpose, an assembly/disassembly line balancing (A/DLB) and a synchronised hybrid Petri nets (SHPN) model will be used to model and control an assembly/disassembly mechatronics line (A/DML), with a fixed number of workstations, served by a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM). The SHPN model is a hybrid type, where A/DML is the discrete part, and WMR with RM is the continuous part. Moreover, the model operates in synchronised mode with signals from sensors. Disassembly starts after the assembly process and after the assembled piece fails the quality test, in order to recover the parts. The WMR with RM is used only during disassembly, to transport the parts from the disassembling locations to the storage locations. Using these models and a LabView platform, a real-time control structure has been designed and implemented, allowing automated assembly and disassembly, where the latter is assisted by a mobile platform equipped with a manipulator.     

Disassembly sequence structure graphs: An optimal approach for multiple-target selective disassembly sequence planning

Modern green products must be easy to disassemble. Specific target components must be accessed and removed for repair, reuse, recycling, or remanufacturing. Prior studies describe various methods for removing selective targets from a product. However, solution quality, model complexity, and searching time have not been considered thoroughly. The goal of this study is to improve solution quality, minimize model complexity, and reduce searching time. To achieve the goal, this study introduces a new ‘disassembly sequence structure graph’ (DSSG) model for multiple-target selective disassembly sequence planning, an approach for creating DSSGs, and methods for searching DSSGs. The DSSG model contains a minimum set of parts that must be removed to remove selected targets, with an order and direction for removing each part. The approach uses expert rules to choose parts, part order, and part disassembly directions, based upon physical constraints. The searching methods use rules to remove all parts, in order, from the DSSG. The DSSG approach is an optimal approach. The approach creates a high quality minimum-size model, in minimum time. The approach finds high quality, practical, realistic, physically feasible solutions, in minimum time. The solutions are optimized for number of removed parts, part order, part disassembly directions, and reorientations. The solutions remove parts in practical order. The solutions remove parts in realistic directions. The solutions consider contact, motion, and fastener constraints. The study also presents eight new design rules. The study results can be used to improve the product design process, increase product life-cycle quality, and reduce product environmental impact.     

Disassembly sequence planning in a disassembly cell context

In this paper a two-phase approach is proposed for determining the optimal disassembly sequence when the disassembly system has a cellular configuration. Operations are first grouped into cells based on the resources they require with the goal of minimizing machine acquisition costs. The aim is to group together those operations that use similar equipment in order to achieve good utilization levels of such equipment. A maximum cell size may be imposed. Once the cells have been formed and the operations have been assigned to them, a metaheuristic algorithm (namely GRASP) is used to search for the disassembly sequence for each product that leads to the minimum number of intercellular movements. To account for uncertainty regarding the condition in which the product may arrive, each disassembly task is assumed to be required with a certain probability, regardless of the other tasks. AND/OR precedence relations among the disassembly tasks are also considered. The proposed approach is illustrated on a randomly generated disassembly problem.