Generation of assembly sequences with ternary operations

Abstract

This paper describes a method for the generation of assembly sequences with ternary operations. While most assembly sequences planners dear1 with sequences where the operations mate two parts, we propose a methlod that generate sequences with ternary operations, that assemble simultaneously three parts. This method examines first the situations where a ternary operation is more efficient than two binary operations. Then it determines the irernary operations that are missed by the assembly sequences planners with binary operations. This methoa! is an extension of the traditional assembly sequences planners where the combinatorial explosion of such prob1e)ms can still be handled.

Survey on assembly sequencing: a combinatorial and geometrical perspective

“…Precedence relationships can also be indirectly encoded as Geometric constraints, which express the absence of collision-free trajectories that allow two subassemblies to contact. They are used in Bonneville et al (1995) to detect assembly sequences that require ternary operations (i.e., three-handed operations).…”

“…The process is stochastic: random choices, with certain fixed probabilities, can be made in the application of the genetic operators (the gene that mutates, the parents selected for crossover) or in the order they are applied. In the pioneering work of Bonneville et al (1995), for example, crossbreed is systematically applied to the pair of fittest individuals, but mutation is randomly applied on the offspring, while in Sebaaly et al (1996) reproduction, crossover and mutation are applied in a cyclic fashion but the mates for crossbreeding are selected randomly. Alternatively, Chen and Liu (2001) propose a multi-layered strategy where the genetic operator probability setting itself is updated dynamically in a second-level genetic algorithm whose chromosomes encode the probability of applying each operator in the primary level.…”

“…Petri nets a flashlight (4) Caselli and Zanichelli (1995) and a ball-point pen (5) Suzuki et al (1993); 2. simulated annealing a relay (10) Hong and Cho (1999); 3. neural networks a pince (4), a hinge (4) and a coupling system (7) Cem Sinanoglu (2005), a gearbox (10) Chen (1992), a relay (10) and an alternator (13) Hong and Cho (1995), and an electric torch (16) Chen et al (2008); 4. genetic algorithms an oil pump (5) Bonneville et al (1995), an industrial controller (19) Guan et al (2002), an hydraulic linear motor (25) Marian et al (2006), an air condition control (28) Sebaaly et al (1996), a signaling relay ( This list -which by no means pretends to be exhaustive-should be interpreted as an illustration of the variety of assemblies dealt with in the existing literature, not as a ranking of the suitability of the different sequencing and optimization algorithms. Furthermore, the examples provided by the authors are generally more oriented towards explaining how their algorithms work than to demonstrate their performance.…”

Assembly sequence planning of automobile body components based on liaison graph

“…Part i and part j can all be assembled at first place. The priority of this graph is lower than that of direct graph, but the graph has priority in the ternary operations (Bonneville et al, 1995), which means that part i and part j must be firstly assembled on fixtures, and together are assembled to part k. . If part i and part j have precedence relationship, the graph is a directed graph.…”

“…The pattern-matching algorithm was to match liaisons or parts with one of the answers to obtain the currently last assembly operation. Bonneville et al (1995) proposed a method for the generation of assembly sequences with ternary operations, which assembled simultaneously three parts. The algorithm examined first the situations where a ternary operation was more efficient than two binary operations.…”