In the modern manufacturing industry, the role of computer-aided process planning (CAPP) is becoming increasingly crucial. Through the application of new technologies, experience, and intelligence, CAPP is contributing to the automation of manufacturing processes. In this article, the integration of a proposed CAPP system that is named as BKCAPP and G-code generation module provides a completed CAD–CAPP–CNC system that does not involve any manual processing in the CAM modules. The BKCAPP system is capable of automatically performing machining feature and operation recognition processes from design features in three-dimensional (3D) solid models, incorporating technical requirements such as the surface roughness, geometric dimensions, and tolerance in order to provide process planning for machining processes, including information on the machine tools, cutting tools, machining conditions, and operation sequences. G-code programs based on macro programming are automatically generated by the G-code generation module on the basis of the basic information for the machining features, such as the contour shape, basic dimensions, and cutting information obtained from BKCAPP. The G-code generation module can be applied to standard machining features, such as faces, pockets, bosses, slots, holes, and contours. This novel integration approach produces a practical CAPP method enabling end users to generate operation consequences and G-code files and to customize specific cutting tools and machine tool data. In this paper, a machining part consisting of basic machining features was used in order to describe the method and verify its implementation.

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...

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Process planning is an important bridge between design and machining in a manufacturing system. However, this process in Vietnam is mostly done manually. At that time, process planning needs quite a lot of effort and time of the engineer. In modern manufacturing, CAD/CAM/CNC integrated technology has developed so much. However, the development of computer-aided process planning (CAPP) is limited and has not caught with the rapid development of CAD/CAM technology. This article presents the methodology for developing and building computer-aided process planning systems for prismatic parts. In this system, the entire feature recognition and some basic modules of the process planning, such as equipment selection as well as operation sequences, are carried out automatically based on diversity database suitable for practical production. The system automatically generates a process planning instruction sheet directly from the solid 3D model in the SolidWorks environment. Testing of the system shows that process planning preparation time is reduced by up to 10 times compared to the manual method while ensuring technical requirements are met.