Augmented reality applied to design for disassembly assessment for a volumetric pump with rotating cylinder

Design for Disassembly (DfD) and Augmented Reality (AR) have become promising approaches to improve sustainability, by providing efficient delivery and learning assets. This study combines DfD and AR to deliver a method that helps to streamline maintenance processes and operator training. It focuses on a common part in the process industry that requires frequent maintenance and repair. DfD was applied to the pump’s design to ease disassembly and reduce material waste, energy consumption, and maintenance time. AR was used to provide an interactive guide to improve the operator understanding of its internal parts and assembly/disassembly procedures. The resulting DfD-AR led to a reduction in maintenance time and shows potential to deliver better training. This highlights the potential of DfD and AR to enhance sustainability, learning, and productivity. The resulting disassembly sequence was taken to an AR simulation, helping process designers to better understand the procedure and further optimize the solution with other constraints

Disassembly sequence planning validated thru augmented reality for a speed reducer

The lifecycle of a product is getting shorter in today’s market realities. Latest developments in the industry are heading towards achieving products that are easy to recycle, by developing further technological advances in raw materials ought to include input from End of Life (EOL) products so a reduction of natural harm could be achieved, hence reducing the overall production environmental footprint. Therefore, the approach taken as a design for environment, a key request nowadays in order to develop products that would ease the reverse manufacturing process leading to a more efficient element recycling for later use as spare parts or remanufacturing. The methodology proposed compares three probable disassembly sequences following a comparison of literature-found procedures between genetic algorithms and as a “state space search” problem, followed by a hybrid approach developed by the authors. Time and evaluation of these procedures reached to the best performing sequence. A subsequent augmented reality disassembly simulation was performed with the top-scored operation sequence with which the user is better able to familiarize himself with the assembly than a traditional paper manual, therefore enlightening the feasibility of the top performing sequence in the real world

Shortest-Path Optimization of Ship Diesel Engine Disassembly and Assembly Based on AND/OR Network

Ship diesel engine disassembly and assembly (SDEDA) is essential for ship inspection and maintenance and navigation safety. The SDEDA consists of various machinery parts and operations. It is crucial to develop a system of SDEDA operations to improve the efficiency of disassembly and assembly (D&A). Considering the “AND” and “OR” relations (modeled as links) among the D&A operations (modeled as nodes), an “AND/OR” network is developed to extend a specialized graph model for the D&A sequencing problem in the context of education and training. Then, we devised a mixed-integer linear program (MILP) to optimize the SDEDA sequence based on the AND/OR network. Considering the flow balance in the AND/OR network, we developed exact algorithms and random search algorithms using breadth-first, branch cut and depth-first strategies to minimize the cost of the shortest path that represents an optimal sequence of D&A operations. To the best of our knowledge, it is the first try to formulate the D&A operations by an extended network model. Numerical experiments show that the proposed algorithms are practical for solving large-scale instances with more than 2000 D&A operations. The breadth-first shortest-path algorithm outperforms the MILP solver from the perspective of solution quality and computing time, and all developed algorithms are competitive in terms of computing time