Disassembly Process Planning Research Articles

Disassembly Process Planning and Its Lines Balancing Prediction

Disassembly Process Planning and Its Lines Balancing Prediction

Disassembly assessment from CAD-based collision evaluation for sequence planning

Disassembly sequence planning is the foundation for disassembly process planning. Furthermore, integrating efficient geometrical disassembly feasibility of non-destructive operations can contribute to designing robotic disassembly and remanufacturing systems. Non-destructive operations increase the chance of repurposing parts with less cost but require collision-free trajectories between components. Thus, generating geometrically feasible disassembly sequences is critical for determining the optimal disassembly sequencing and disassembly trajectories. In this work, geometric collision data has generated to develop the disassembly precedence matrix that provides fundamental information for disassembly planning methods. Current methods often require significant manual resources to locate geometrically feasible sequences for disassembly of products. This paper describes the results of utilizing disassembly collision tests to automatically extract interference data from STEP CAD assembly files to determine feasible disassembly operations. A method to determine a precedence matrix that describes all geometrically feasible disassembly operations for disassembly of a selected part or a whole assembly to its components has been developed and tested. Extracted data from STEP files is used to characterize the spatial properties of a component in an assembly and is the basis for the collision test to determine the geometrically feasible disassembly operations. The precedence matrix also identifies the feasible disassembly directions for each component based on the outcomes of the collision tests.

A Hybrid Approach Combining Improved Shuffled Frog-Leaping Algorithm With Dynamic Programming for Disassembly Process Planning

This paper proposes a hybrid approach which combines Improved Shuffled Frog-Leaping Algorithm (ISFLA) with Dynamic Programming (DP) for disassembly process planning (DPP). The DPP is a new frontier to the end-of-life product management which can help protect our environment. In the DPP, two tasks are to be accomplished. One is the determination of a disassembly sequence for components and another is the assignment of each component with a responsible operator. In the hybrid approach, the Shuffled Frog-Leaping Algorithm is used to accomplish the first task while the dynamic programming is used to complete the second task. Preliminary experimental results show the proposed hybrid approach outperforms the standard Shuffled Frog-Leaping Algorithm and improved Shuffled Frog-Leaping Algorithm. The comparison to a MILP has further confirmed that the proposed hybrid approach can find the optimal/near-optimal solution in terms of disassembly time. Further experiments show that the hybrid approach outperforms a genetic GA and MA. The results show the potential of using hybrid approaches for disassembly process planning.

Linguistic Petri Nets Based on Cloud Model Theory for Knowledge Representation and Reasoning

Fuzzy Petri nets (FPNs) are a vital modeling technique for the construction of knowledge-based systems, which have been commonly used in many fields, such as fault diagnosis, risk assessment, workflow management, and disassembly process planning. However, the conventional FPNs have been blamed for the following reasons: 1) the representation parameters in FPNs cannot precisely model experts' experience since it is difficult to manage the fuzziness and randomness of knowledge assessments simultaneously, and 2) the weight coefficients in the existing approximate reasoning algorithms are hardly enough to reflect the associated weights of reordered places. In response, we propose a new type of FPNs, called cloud reasoning Petri nets (CRPNs) based on the concept of interval clouds and the hybrid averaging operator. The cloud production rules in a knowledge-based system are modeled by CRPNs, where the truth degrees of places, the certainty factors of rules, and the thresholds of transitions are represented by interval clouds. Moreover, a matrix operation-based reasoning algorithm is proposed to improve the efficiency of calculating final truth degrees, in which both local and ordered weight coefficients are taken into consideration. Finally, a practical example concerning a power system is provided to demonstrate the usefulness and advantages of the proposed CRPN model.

A Fuzzy Knowledge-Based Disassembly Process Planning System Based on Fuzzy Attributed and Timed Predicate/Transition Net

This paper focuses on graphic-based approaches and proposes a fuzzy knowledge-based disassembly process planning (FDPP) system for DPP. This FDPP system is based on a novel high-level Petri net (PN), termed fuzzy attributed and timed predicate/transition net (FATP/T net). An FATP/T net model for DPP is developed and the model is found very compact and general to any products, due to the comprehensive modeling capability and the inclusion of a bill of material component. It avoids issues such as unmanageable big model size and specific to a product, which are often found in the DPP models based on ordinary PNs. Another feature of the FATP/T net model is that it also includes a functional test operation to discern the usability of detached subassemblies/components. In addition, we have proposed a systemic approach to transform the FATP/T net model into an FDPP system to support DPP at a system level. A telephone kit is used as an example to illustrate the applicability of the FDPP system. The FDPP system is found flexible and expandable due to the independence of its components.

Prise en compte de l’état des produits pour la planification de leur désassemblage

This paper adresses the problem of disassembly process planning taking into account the quality or states of the product to be disassembled. We propose an approach which is able to return the best disassembly level for a product considering the disassembly cost and the state of the product and/or the states of its subassemblies or components. The state of the product is represented using the concept of « Potentiel d’Utilisation Residuel (PUR) » which is assumed to be a Gaussian random variable with known truncated distribution. A stochastic program is proposed to model the problem with the objective of maximizing the disassembly process profit. The latter is calculated as the difference between the positive revenue generated by recovered parts and the costs of the disassembly tasks. The revenue of a recovered part is a function of PUR. The developed approach is tested on two example case studies from the literature to analyze the impact of uncertain product quality on its disassembly process planning.

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. Actual implication of the learning and revision strategy in cognitive robotic disassembly operation.Improvement of the process performance using learning and revision strategy.Disassembly sequence and process plans can be automatically generated by the CRA during the disassembly.

Flexible disassembly planning considering product conditions

To increase the economic benefit of product recovery at the end of life of a consumer product, the profit margins should be augmented. This can be realised by utilising the existing flexibility of today’s mostly manually-conducted disassembly processes. Moreover, with increased economic benefits, the recovery becomes even more attractive, which is also beneficial to the environment. A key component of product recovery is disassembly. Allowing different disassembly states (or levels) per core (i.e. recovered product) increases flexibility in planning. This should result in higher profits, as long as the flexibility is still manageable for the companies. This study focuses on flexible disassembly planning, i.e. the integration of sequencing aspects into disassembly (process) planning. In addition, we further incorporate the condition of items in the core, item damaging, purity requirements, special treatment of hazardous items and several limitations like core availability, item, module and material distribution, disposal and labour time limit. We base our developed mixed integer linear programme on graphs, such as the disassembly state graph for sequencing, and a hypergraph to model the core condition. Lastly, our considerations are illustrated by a numerical example.

Using constraint-satisfaction to optimise disassembly sequences generated from AND/OR information

This paper applies a constraint-based method, shown to successfully find the optimal order of component removal using precedence information, to finding the optimal sequence via AND/OR data. Development of disassembly theory is vital for practically all product end-of-life strategies. In particular, Disassembly Process Plans (DPPs) allow the careful selection and removal of components for recycling and reuse. While AND/OR graphs explicitly embody all of the feasible DPPs of an assembly, generating the best plan is non-trivial due to the rapid increase in the number of solution paths. In contrast, defining the physical restrictions between the components using precedence relationships is implicit and hence compact. By converting AND/OR representations into precedence ones, not only is the size of the problem reduced, modified techniques developed previously are applicable. Dependent upon the structure of the subassemblies, two different approaches are used to achieve this: the introduction of dummy components, and by means of the set of operations upon the subassemblies. Thus an all-encompassing methodology is created that can deal with any DPP representation such that the optimal sequences may be found.

Subassembly identification based on grey clustering

Nowadays discarded electromechanical products are more and more common, and have done much harm to the ecological environment, human health and natural resources. In order to recycle discarded products effectively, it is necessary to disassemble them properly in an integrated consideration of economic returns and environmental protection. Therefore Disassembly Process Planning has become a key part in Environmentally Conscious Design. Because there may be a combination explosion when the disassembly process of a product with a large number of parts is planned, subassembly identification is often adopted to divide the product into some reasonable subassemblies. This paper uses a method of grey clustering based on grey system theory to perform subassembly identification. The clustered objects are part pairs with adjacency relation in a product, and the clustering indices consist of energy consumption of disassembly, disassembly time, disassemblable direction and diameter of part pair. The indices can be obtained by detailed estimate or direct input from a CAD system via secondary development. After five grey clusters are set up, their whitening weight functions are presented in detail. Several other key problems in subassembly identification based on grey clustering are also expounded, such as obtaining the nondimensional matrix of sample values, determining the weight of clustering index relative to grey cluster. A heat-sealing machine with 80 components is selected as an example to validate the method of subassembly identification based on grey clustering. The identification result of the example is feasible according to experiences, and at the same time it satisfies the requirement of Disassembly Process Planning.

Disassembly planning of mechanical systems for service and recovery: a genetic algorithms based approach

In a perspective of improving the behavior of a product in its whole life cycle, the efficient planning of the disassembly processes acquires strategic importance, as it can improve both the product’s use phase, by facilitating service operations (maintenance and repairs), and the end-oflife phase, by favoring the recycling ofmaterials and the reuse of components. The present paper proposes an approach to disassembly process planning that supports the search for the disassembly sequence best suited for both aspects, service of the product and recovery at the end of its useful life, developing two different algorithms. Notwithstanding their different purposes, the two algorithms share the typology of modeling on which they operate, and the logical structure according to which the genetic search procedure is developed. The choice of implementing genetic algorithms was prompted by the intrinsic complexity of the complete mathematical solution to the problem of generating the disassembly sequences, which suggests the use of a non-exhaustive approach. As is shown in the results of a set of simulations, both algorithms may be used not only for the purposes related to disassembly process planning but also as supporting tools during the product design phases. This is especially so for the second algorithm, that deals with the problem of a recovery-oriented disassembly through an all-encompassing approach, combining economical and environmental considerations, and extending the evaluations to the whole life cycle of the product. This formulation gives this algorithm and autonomous decisional capacity on both the disassembly level to be reached, and the definition of the optimum recovery plan (i.e., the best destination for the disassembled components, based on some significant properties of them).

Disassembly sequence generation in recycling based on parts accessibility and end-of-life strategy

Nowadays, global sustainability is the central issue in recycling and, increasingly, in manufacturing. Recycling used products can save energy, natural resources, and landfill space, and can reduce air pollution. It can give used products new lives. The study of disassembly is needed in order to make recycling economical, and disassembly sequence generation (DSG) plays an important role. An appropriate disassembly process plan can minimize the cost spent on the disassembly processes and maximize the benefits coming from the reused components. In the current paper, a new approach using Petri net modelling to generate an optimal disassembly sequence (ODS), based on accessibility and end-of-life (EOL) strategy, is described. The different life spans of the reusable components affect the disassembly order, especially in destructive disassembly, and the influence of components with different life spans on DSG is analysed. First, AND/OR graphs are used to generate all feasible disassembly sequences, and then AND/OR graphs are transferred into Petri net graphs while accessibility values and life span values of components are taken into account to obtain the ODS. A program using Microsoft C++ is developed to generate the ODS. The disassembly of a C-clamp is used as a trial example.

A Lagrangian Relaxation Based Approach to Schedule Asset Overhaul and Repair Services

Overhaul and repair services are important segments of the remanufacturing industry, and are characterized by complicated disassembly, repair and assembly process plans, stochastic operations, and the usage of rotable inventory. In view of today's time-based competition, effectively scheduling such services and managing rotable inventory and uncertainties are becoming imperative to achieve on-time deliveries and low overall costs. In this paper, a novel formulation for overhaul and repair services is presented where key characteristics, such as uncertain asset arrivals and operation processing times, and rotable parts are abstracted to model an overhaul center and multiple repair shops in a distributed framework to reflect organizational structures. Interactions between the overhaul center and repair shops are described by sets of coupling constraints across the organizations. Rotable inventory dynamics is formulated in terms of repair operation completion times and asset assembly beginning times to facilitate minimization of inventory holding costs through scheduling. A solution methodology combining Lagrangian relaxation, stochastic dynamic programming, and heuristics is developed to schedule operations in a coordinated manner to minimize total tardiness, earliness, and inventory holding costs. Additionally, penalty terms associated with coupling constraint violations are introduced to the objective function to improve algorithm convergence and schedule quality, and a surrogate optimization framework is used to overcome the inseparability difficulty caused by the penalty terms. Numerical testing results show that the new approach is computationally effective to handle rotable inventory and uncertainties, and provides high quality schedules with low overall costs for stochastic remanufacturing systems. Note to Practitioners-Overhaul and repair services for jet engines, helicopters, airplanes, are important segments of the remanufacturing industry, and are characterized by complicated disassembly, repair and assembly process plans, stochastic operations, and the usage of rotable inventory. In view of today's highly competitive business climate, effectively scheduling such services and managing rotable inventory and uncertainties are becoming critical to achieve on-time deliveries and low overall costs. In this paper, a novel formulation for overhaul and repair services is presented where key characteristics, such as uncertain asset arrivals and operation processing times, and rotable parts are abstracted to model an overhaul center and multiple repair shops in a distributed framework to reflect organizational structures. A solution methodology based on decomposition and coordination is developed to schedule operations to minimize total tardiness, earliness, and inventory holding costs. Numerical testing results show that the method is computationally efficient for managing rotable inventory and uncertainties, and generates high quality schedules with low overall costs. The value of rotable inventory to reduce tardiness costs and buffer uncertainties is demonstrated, and the robustness of the new method is evaluated by cases with different settings of machine utilization levels and uncertainty levels. The scalability of the method to solve large problems with hundreds of assets is also demonstrated.

Modelling and planning of the disassembly processes using an enhanced expert Petri net

A sound disassembly Petri net model for the effective planning of disassembly processes and tasks is outlined. Owing to the unmanageable complexity associated with modelling of the disassembly processes and tasks, it becomes essential to have a more powerful Petri net model developed by incorporating the concepts of expert system, knowledge representation techniques, etc. Disassembly task planning at high and low levels can easily be represented by proposed high- and low-level expert Petri net. An algorithmic approach is also suggested for evaluating the end-of-life values of a product. These values are used to determine an optimal disassembly sequence and it is incorporated in the expert disassembly Petri net. A proposed expert enhanced high-level coloured disassembly Petri net is empowered to express such details vividly. The application of the proposed expert enhanced high-level coloured disassembly Petri net model is demonstrated through the sample disassembly of a flashlight.

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.

Process planning for product disassembly

A disassembly plan is described by a disassembly bill of materials (DBOM), the sequence of processing steps, the type of disassembly action, the part or fastener worked on each step, the tools used, and the resulting material and part outputs. The disassembly process planning problem (DP3) involves the generation of a feasible plan and its implementation at a disassembly facility. In this paper, we introduce and describe the DP3 model. This model includes a structured format for creating, documenting, and evaluating a disassembly process plan. The resulting plan is intended to be a readily implemented solution and hence can be adopted by any reclamation facility. A key utility of this model is that it provides a format for transmitting product knowledge from the original product manufacturer to the consumer and the end-of-life disassembler, via the disassembly bill of materials (DBOM). The DP3 is a descriptive model, in that it describes a plan that can be readily developed by a manufacturer and efficiently distributed to the disassembly community. It is left to the user though, to determine the sequence of disassembly steps. The model introduces a variety of standards for identifying unfastening actions, destructive actions, and the required tools. The DP3 model also provides an economic evaluation of different plans.

Petri net approach to disassembly process planning for products with complex AND/OR precedence relationships

We present a Petri net (PN)-based approach to automatically generate disassembly process plans (DPPs) for product recycling or remanufacturing. We define an algorithm to generate a geometrically-based disassembly precedence matrix (DPM) from a CAD drawing of the product. We then define an algorithm to automatically generate a disassembly Petri net (DPN) from the DPM; the DPN is live, bounded, and reversible. The resulting DPN can be analyzed using the reachability tree method to generate feasible DPPs, and cost functions can be used to determine the optimal DPP. Since reachability tree generation is NP-complete, we develop a heuristic to dynamically explore the v likeliest lowest cost branches of the tree, to identify optimal or near-optimal DPPs. The cost function incorporates tool changes, changes in direction of movement, and individual part characteristics (e.g., hazardous). An example is used to illustrate the procedure. This approach can be used for products containing AND, OR, and complex AND/OR disassembly precedence relationships.

A general approach to disassembly planning and control

Closing product and material cycles have emerged as a paradigm for industry in the 21st century, and are directed towards an economical and responsible use of our limited resources. Disassembly plays a key role in recycling. It enables the recovery of functioning components for re-use of pure materials for re-utilization and the separation of harmful substances. Major challenges are presented by the enormous variety of products to be disassembled, by uncertainty as to quantity and by their unknown properties. An appropriate planning and control of disassembly processes is essential for an economic disassembly. Based on an investigation of the suitability of common production planning and control (PPC) methods for disassembly, a specific disassembly planning and control concept (DPC) is outlined.

A methodology for modeling and adaptive planning of disassembly processes

A principal process during the remanufacturing of worn-out or malfunctioning products is disassembly that enables the dumping, cleaning, repair or replacement of components as desired. Products subjected to disassembly exhibit uncertainty in the product structure and component conditions. Hence the termination goal (the level of disassembly) is subject to change, and the disassembly plan must be adapted. This paper proposes a mathematically sound disassembly Petri net (DPN) for the modeling and adaptive planning of disassembly processes. The planning algorithm guarantees the optimal remanufacturing value when each node's utility function (benefit of a subassembly or part and cost of a disassembly operation) is fixed. It can be modified to deal with the cases when the disassembly operation success rates vary with the product condition and reliability of resources performing the operations. The proposed methodology and algorithms are demonstrated through the sample disassembly of a telephone.

A Petri net approach to disassembly process planning

Recycling and remanufacturing involve product disassembly to retrieve the desired parts and/or subassemblies. Disassembly is a systematic method for separating a product into its constituent parts, components, or other groupings. Disassembly process planning is critical in minimizing the amount of resources (e.g., time and money) invested in disassembly and maximizing the level of automation of the disassembly process and the quality of the parts (or materials) recovered. We propose an algorithm which automatically generates a disassembly Petri net (DPN) from a geometrically-based precedence matrix. The resulting DPN can be analyzed to generate all feasible disassembly process plans (DPPs), and cost functions can be used to determine the optimal DPP; alternatively, heuristic methods may be used to generate near-optimal DPPs.