• M V A RAJU BAHUBALENDRUNI

• Optimal Assembly Sequence generation through computational approach

Optimal assembly sequence (OAS) is always an interesting aspect for an industrial engineer to minimize assembly time and cost, which gives number of assembly levels and sequence of assembly operations. An assembly sequence with more number of parallel possible stable sub-assemblies significantly reduces theoverall assembly time for large scale products. Finding such optimal sequences from huge set of all assembly sequences (SAAS) is challenging due to involvement of multiple assembly feasibility validation criteria namely; assembly coherence, geometric feasibility, part stability and mechanical feasibility. In this paper, an efficient computational method is proposed to generate optimal assembly sequences. The method considers extended assembly stability relations to identify stable sub-assemblies for parallel execution. The method is proven ingenerating optimal solutions for any given product effectively. The method is well discussed and compared with prominent computational methods with suitable product illustrations.

• Optimal disassembly sequence generation and disposal of parts using stability graph cut-set method for End of Life product

Disassembly is one of the essential operations in manufacturing to recover the useful parts of the product after End of Life (EOL). Moreover, by generating an optimal disassembly sequence, the time to dismantle the product will be reduced, and in turn, cost also reduces. However, achieving an optimal disassembly sequence is not an easy task as it is an NP-hard combinatorial problem. Many researchers followed different approaches like mathematical, knowledge-based and artificial intelligence (AI)-based methods to generate optimal disassembly sequences. Most of the researchers concentrate on generating the optimal disassembly sequence, but only a few of them discuss the disposal of the parts after EOL. It is very much essential to consider the type of disposal that has to follow the individual components after dismantle to reduce the effect on the environment due to parts of the EOL product. In this research work, a stability graph cut-set method is applied to generate optimal disassembly sequences by considering the minimum number of directional changes as a fitness equation. In the proposed methodology, a stability graph is formulated for the considered assembly to apply cut-set rules for generating optimal assembly sequences. Later, the reverse of the obtained optimal assembly sequences is followed to generate the optimal disassembly sequences. In this strategy, along with the generation of optimal disassembly sequences, the type of disposal (like landfill, incineration and recycling) that has to follow for the individual parts is also discussed using a SOLIDWORKS sustainable tool. The proposed stability graph cut-set method is validated using an eleven-part punching machine assembly to generate the optimal disassembly sequences; also the type of disposal that has to follow for each part after dismantle is discussed. Moreover, the proposed methodology is compared to the well-known algorithms [genetic algorithm (GA) and ant colony optimization (ACO) algorithm] in terms of the number of iterations, the number of optimal disassembly sequences generated and fitness value to check the performance of the algorithm

• A multi-layered disassembly sequence planning method to support decision making in de-manufacturing

End of Life (EoL) products management through crude recycling methods and direct shredding causes severe threat to environment by different kinds of pollution. The environmental benefits such as reduced CO2 emissions observed through recycling are mitigated by the direct release of toxic gases. De-manufacturing through systematic disassembly operations can reduce environmental damage but it requires more input cost to work within the threshold limit value (TLV). Complete disassembly sequence planning (CDSP) methods are not given importance in de-manufacturing objectives, which produce non-optimalsolutions. The objective of present work is to produce an optimal solution to extract valuable materials from the EoL products while separating the toxic elements within TLV. A multi-layered method has been proposed that consists of five different layers, namely data input layer, application layer, modification layer, implementation layer and performance layer. It requires product bill of materials (BOM) and disassembly attributes such as liaison, geometric feasibility and stability as input from the user and generates an optimal solution bysuggesting permissible operations to work within TLV. The proposed method has been applied on a case study and workability at producing practically feasible optimal solution has been confirmed. The effectiveness in working has been evaluated by comparing to the existing DSP methods. The proposed method can be used as a tool to achieve maximum profits through systematic disassembly operations within TLV by supplying essential information.