Effective method of operation sequence optimization in CAPP based on modified clustering algorithm

Abstract

Computer aided process planning (CAPP) is an important bridge between computer aided design (CAD) and computer aided manufacturing (CAM) in computer integrated manufacturing environment. Operation sequence generation is one of the most difficult tasks in CAPP. The aim of operation sequencing in CAPP is to determine the best order of machining operations with minimal manufacturing cost while satisfying all the precedence constraints. This paper presents a proposed method for optimizing operation sequence using modified clustering algorithm. The key concept of method is that the precedence constraints are firstly checked for selecting all possible next operations of the last operation in the sequence and their traveling costs are compared to choose the optimal feasible operation which has the minimum traveling cost in the sequence. Then, all operation sequences are calculated the total traveling cost for obtaining the optimal sequence result. Because of removing all unfeasible sequences at the beginning of procedure and selecting the optimal operation into sequence in each step, the time can be significantly reduced. The capability and performance of the proposed method are demonstrated in three specific case studies. The comparisons show that the proposed method can solve the problem in much lesser computational time while generating more alternate optimal feasible sequences than previous algorithms. Phung, Tran, Hoang and Truong, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol.11, No.1 (2017) cutting tools, and set-up allows reducing the machining cost while the machine changes, set-up changes, and cutting tool changes related to traveling cost. The selection of manufacturing resources is based on the machine, setup or cutting tool cost. The optimal selection should have the minimum manufacturing resource cost while ensuring machining technology. Many researchers have approached the problem of minimizing the traveling cost to obtain the optimal operation sequences based on all selected manufacturing resources. To solve this issue, several researchers proposed various methods using artificial intelligence algorithms (Roman Stryczek 2007). Bhaskara Reddy SV et al. (1999) applied genetic algorithm to obtain the optimal operation sequence. It is based on consideration of traveling cost and precedence constraints. Jaber Abu Qudeiri et al. (2007) found the efficient sequence of operations located in asymmetrical locations and different levels to achieve the shortest cutting tool travel path based on genetic algorithm. It is effectively demonstrated by the application of finding operation sequence in hole making series in different levels. JinFeng Wang et al. (2011) developed a modified genetic algorithm for process planning optimization. The natural number composing of five decimal codes was adopted in coding strategy. Krishna AG and Rao KM (2006) presented an operation sequence optimization method based on ant colony algorithm. G. Nallakumarasamy et al. (2011) developed an algori...

Novel Integration of CAPP in a G-Code Generation Module Using Macro Programming for CNC Application

“…Operation sequence generation: For each machining feature, some data are determined for the operation sequence generation process, including the machine, cutting tool, setup, and precedence constraints. The operation sequence is generated using a developed clustering algorithm in order to minimize machining costs based on the traveling costs for the machine, setup, and cutting tool changes, while still ensuring that the precedence constraints are not violated [25]. The concept for the algorithm is based on the calculation of a similarity coefficient between any two operations from the operation list for a part.…”