Assembly Planning by Subassembly Decomposition Using Blocking Reduction

Abstract

The sequence in which a complex product is assembled directly impacts the ease and efficiency of the assembly process, whether executed by a human or a robot. A sequence that gives the assembler the greatest freedom of movement is therefore desirable. Our main contribution is an expression of obstruction relationships between parts as a disassembly interference graph (DIG). We validate this heuristic by developing a disassembly sequence planner that partitions assemblies in a way that prioritizes access to parts, resulting in plans that are comparable in efficiency to two state-of-the-art assembly methods in terms of total plan length. Using DIG, our method generates successive subassembly decompositions, yielding a tree structure that makes parallization opportunities apparent. Our planner generates viable disassembly plans by minimizing our part blockage measure, and thereby demonstrates that this measure is a valuable addition to the Assembly Sequence Planning toolkit.

Industry 4.0 and Prospects of Circular Economy: A Survey of Robotic Assembly and Disassembly

“…Ideally, such a smart system should be capable of selfdiagnosing the problems, and perform self-monitoring, thereby obviating the necessity of the presence of a human operator, and enhance the level of automation through more efficient data exchange strategies made possible by means of AI, cognitive computing, cloud computing, industrial Internet-of-Things and Cyber-Physical Systems (CPS). Internet-of-Things 19 for manufacturing, Internet-of-Everything 20 , Manufacturing 4.0, smart factory, connected enterprise and industrial internet often indicate a similar, if not the same, concept. A smart factory resembling Industry 4.0 employs a modular structure, where a CPS monitors physical processes, and decisions are made in a decentralized manner.…”

“…considering minimizing blockage of the parts as the target. Consequently, a series of subassembly decomposition is developed in the form of a tree structure that is suitable for parallelization [19]. It is worth noticing that parallelization of assembly or disassembly tasks requires ensuring the stability of the subassemblies, which demands inferring and taking into account extended subassembly stability relationships, aiming at avoiding possible undesired divergences of the main product structure during the course of performing RAD [20].…”

Optimal decision making in robotic assembly and other trial-and-error tasks

“…Planning is strongly related to the specific objectives of the assembly problem at task level. In our previous work [24] we have explored algorithms to find sub-assemblies that maximize the success of an assembly [24], albeit in a completely deterministic environment. We consider and minimize the expected error of placement of beams in a truss [25], and maximizes the stability of the resulting structure [26].…”

Research on assembly sequence planning method based on artificial neural networks

“…Current research on assembly sequence planning can be categorized into two primary approaches: the first revolves around generating feasible sequences through constraint-based reasoning and then optimizing them (methods include constraint reasoning [12] , decomposition [13] , knowledge [14] and case-based approaches [15] ); the second involves modern optimization techniques that evolve feasible sequences and filter them during the process (methods like genetic algorithms [16] , simulated annealing [17] , etc.). The first approach generally manages products with fewer than 20 parts due to computational limitations, which directs the research towards modern optimization algorithms.…”