Research On EOL Product Disassembly Sequence Planning

The increasing of end-of-life(EOL) produces brings unprecedented pressure to the ecological resources and environment all over the world. The technologies about disassembly and recycling are becoming research focus. Most electro-mechanical products cannot be recycled only because they cannot be disassembled well.Aiming at the EOL electric-mechanical product, this dissertation focuses on the research on disassembly modeling and the decision of the optimal disassembly sequence under multi-objective and multi-working- condition. The main contents are as following:Disassembly modeling, that is to express all disassembly sequences from the product to each component. As a NP-Complete problem, with the little quantity increasing of product's components, combination explosion will be happen in common modeling algorithms. Two methods are proposed to solve above problem.Method 1: starting with the part structure, parts are reclassified and defined with the generating algorithm flows. In this means disassembly modeling is equal to finding the disassembly level relationships. And other types of part are compressed to the LSU list as its accessories, which can decrease the scale of disassembly system in modeling process greatly.Method 2: the quantity in disassembly system can be decreased by building the disassembly modules. In practice, based on the fuzzy clustering algorithm, at first, define the membership function of any two parts in disassembly system, using the connection strength functions in engineer. The disassembly membership matrix is then gained. Next, modify reference are defined based on the disassembly level relationship of position connection parts unit (PCU),with which the disassembly membership matrix is modified in global range. As result, the fuzzy equivalence matrix can be obtained after n-times of transitive closure operations. Judging by this, aλ-cut moment dynamic clustering graph is generated to divide disassembly system into several modules.The damage degree of EOL product and uncertain factors in operation can directly influence the decision of the optimal disassembly sequence. So it is important to manage and use information about the products and the disassembly process. Firstly, the knowledge are classified and be stored correspondingly. Secondly, it is also defined the relationship and the rules among these knowledge. Several kind of key random variables involved in decision algorithm are also defined. These data give a guarantee to the optimal algorithm for disassembly sequence.At last, A KBDPN (knowledge-based disassembly Petri net) model is built to express the nested relations of disassembly system with all topology information, in which the base Petri net model can express the geometrical information about disassembly system. As for the non-geometrical topology information (such as the disassembly time, recycle benefits et al.) is effected in form of references by the disassembly operation (trigger) or part (place) independently. In this way, the disassembly sequence planning of EOL products can be realized under multi-objective and multi-working-condition.