In order to perform accurately scheduling of machining using a machine tool, it is necessary to estimate the actual machining time. The machining time is generally estimated by CAM systems. However, the error between the estimated and real cutting times is considerable because the systems do not consider the control and functional characteristics of the machine tool. In addition, control functions are installed in machine tools to achieve high precision and high speed motion while optimizing the tool paths and control parameters. The functions significantly affect the machining time. However, estimating machining time is difficult, thereby complicating optimization process. In this study, a method to identify the control characteristics and actual tool paths and a system to estimate the cutting time were developed. Furthermore, an estimation system using a deep neural network (DNN) was constructed to incorporate a control function. Finally, verification experiments were conducted wherein the estimation accuracy of the machining time was found to be within 5%.

Three-dimensional CAD systems contribute extensively to the detailed design processes of products. In detailed design work, designers are required not only to accurately define shapes of products but also to assign attribute information which is essential for the manufacturing process. However, it is extremely hard for inexperienced designers to determine optimum attribute values and design values. This paper introduces “Function Features” which give information on the function and role of each area of a designed part. The “Function Features” enables designers to acquire design attribute information and know-how which are related to a “Function Feature” by easy operations. A system based on the “Function Features” for the design of injection molds was developed and the results of simple design experiments confirmed the usefulness of the proposed method using “Function Features”.

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In machining, hole-making process takes up large part of the manufacturing processes. In previous hole-making processes optimization researches, researcher considered machine tools to have movement control on 3-axis. Thus, it is difficult to apply the result of the researches to 5-axis machining. In addition, in these studies, it is assumed that tool needed to make a hole are always available or only a single tool is needed to make a hole. However in a real working environment, number of the tools available are limited and also single tool cannot make a required hole diameter and tolerance in the most cases. Thus, the result of past researches is difficult to apply in real working environment. This research investigated hole-making optimization that can be applied to 5-axis machining, and considering the tool movement, tool switching, and limitation in the tool. Also tolerances of the holes were considered as a machining accuracy. Optimization can be done using brute-force approach and method solving traveling salesman problem (TSP). However, brute-force approach will be difficult to apply due to the longer time for calculation, when number of the hole pattern increases. For optimization, Genetic Algorithm (GA) was used to create optimization system. System was compared against the brute-force approach to check its validity by comparing the result and calculation time. After validity check, system was applied to the engine block model to obtain optimized hole-making processes. Guiotoko, Aoyama and Sano, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol.11, No.4 (2017) makes the optimization of hole making complex. Previous studies on this topic focused on solving the hole-making problem by adopting approaches similar to the traveling salesman problem (TSP). For instance, Kolahan & Liang utilized tabu-search to minimize the total processing cost, which included tool travel time, tool switching time, and cutting time in hole-making operations (Kolahan and Liang, 2000). Ghaiebi & Solimanpur used ant-colony optimization algorithm to optimize a process in which several tools were required to finish a hole (Ghaiebi and Solimanpur, 2007). Their target involved minimizing tool airtime and tool switch time. Abu Qudeiri used genetic algorithm to optimize the operation sequence in a CNC machine with operations located in asymmetrical locations and at different levels (Abu Qudeiri et al., 2007). Their target for the research was to reduce the cutting tool travel path. Lim utilized a hybrid cuckoo search genetic algorithm to optimize the hole-making sequence (Lim et al., 2016). These studies considered machine tools to have movement control on 3-axis. Thus, it is difficult to apply the result of the research to the 5-axis machining, which consist of additional two rotational axis. The usage of the 5-axis machining is increasing since complex shapes can be machined in a single set-up, which gives greater machining productivity (Modern machine shop, 2004). In addition, in these studies, it is a...

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