Abstract
Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.
Highlights
Process planning for prismatic parts is a very complex and difficult process
The combination of TWC, total tool cost (TTC), total machine change cost (TMCC), total tool change cost (TTCC), and TSCC will be used as the objective of process planning problem, which can be defined as total production cost (TPC) andcalculated by total production costs (TPCs) = w1 ∗ total machine cost (TMC) + w2 ∗ TTC + w3 ∗ TMCC (14)
Local Update Rule is introduced so that the elite process plan solutions are used to update the pheromone on the arcs again, which will accelerate the convergence of the algorithm to the optimal process plan
Summary
Process planning for prismatic parts is a very complex and difficult process. For a prismatic part with complex structures and numerous features, process planning involves selecting machining operations for every feature and sequencing them considering precedence constraints, choosing available manufacturing resources, determining setup plans, and machining parameters, and so forth. Some bioinspired algorithms are applied in complex decision-making process of solve combinatorial optimization problem [1,2,3]. An improved ant colony optimization (ACO) approach is proposed to deal with process planning problem based on a weight graph. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the operation nodes on the graph along the directed/undirected arcs. The heuristic information of operation nodes and pheromone amount on the arcs will guide ant colony to achieve the optimal nodes set and arc set, which represents the optimal solution with the objective of minimizing total production costs (TPCs). Some efforts have been adopted to improve the efficiency of the approach
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