Optimizing assembly planning through a three-stage integrated approach

Abstract

This study proposes a three-stage integrated approach with heuristic working rules to assist the planner to develop a better assembly plan. In the first stage, Above Graph and transforming rules were used to create a correct Explosion Graph of the assembly models. In the second stage, a three-level relational model was developed to create a complete relational model graph and an incidence matrix. In the third stage, a mathematical model, based on a penalty index was formulated, and a revised minimum spanning table method was used to generate and evaluate a feasible assembly sequence. The example of a military system is presented to demonstrate the feasibility of the proposed method. The contribution of this study is based on a design for assembly potential applications of three-dimensional (3D) component models to assist manual or automatic assembly in a virtual environment, and allows the designer to recognize the relative position, constraints and relationships of 3D components using graph-oriented methods: Above Graph, assembly precedent diagram, and relational model graph. r

Assembly Sequence Planning with Fireworks Algorithm

“…The traditional methods [2], [3] are difficult to solve assembly sequence planning for complex products. In recent 20 years, many scholars have applied the intelligent optimization algorithms to the assembly sequence planning, such as genetic algorithm (GA) [4], particle swarm optimization (PSO) [5], simulated annealing algorithm and ant colony algorithm (AC) [6], which provided the powerful methods to obtain the assembly sequence of the complex products.…”

“…As the number of the assembly sequence increases exponentially when the components of the product increase, so the complex products which have a lot of components will cause the combinatorial explosion problem about assembly sequence planning. Therefore, the method of assembly sequence planning will fall into the class of nondeterministic polynomial bounded hard problems [1].The traditional methods [2], [3] are difficult to solve assembly sequence planning for complex products. In recent 20 years, many scholars have applied the intelligent optimization algorithms to the assembly sequence planning, such as genetic algorithm (GA) [4], particle swarm optimization (PSO) [5], simulated annealing algorithm and ant colony algorithm (AC) [6], which provided the powerful methods to obtain the assembly sequence of the complex products.…”

A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN

“…An effective assembly plan must include other graphs, such as the explosion graph, the relational model graph, the incidence matrix, the assembly precedence diagram (APD), etc. In reality, few experts or engineers know exactly how to derive a correct explosion graph, draw a complete relational model graph or incidence matrix among the components, or determine a complete APD to generate an optimal assembly sequence (Chen, Lu, & Tai, 2004b, 2008 The other approach to Knowledge-based engineering (KBE) is a technology that allows an engineer to create a product model based on rules and the powerful CAD/CAM applications that used to design, configure and assemble products, examples of which include the so-called expert systems, web-based knowledge bases involving the engineering knowledge (i.e., Knowledge Fusion) and becoming an critical part of business strategy (Homen de Mello & Sanderson, 1991b). In addition, numerous researchers have employed an artificial intelligence (AI) tree search or graph search methodology to generate an assembly sequence (Chen, Lu, & Tai, 2004a;Homen de Mello & Sanderson, 1991b).…”

A Decision Support System for Integrated Assembly and Disassembly Planning Using a Ga Approach

“…The third category focuses on assembly analysis and evaluation for searching the better or the optimal assembly sequence. The research in this class includes de Mello and Sanderson (1991), Ben-Arieh, and Kramer (1994), Laperriere and ElMaraghy (1996), Gottipolu and Ghosh (1997), Tseng and Liou (2000), and Chen et al (2004).…”