Sintered tungsten carbide which has high hardness and high heat resistance, has been widely used in molds and dies. Research on the development of a cutting technology for sintered tungsten carbide (sintered WC-Co alloy) has been pursued mainly with the use of a turning process. We focused on building an efficient milling method for sintered tungsten carbide by using diamond-coated ball end tools, and have investigated their basic properties under specific cutting conditions. This study extends our previous work by enhancing cutting distance in the milling of sintered tungsten carbide, especially that with a “fine” WC grain. The surface roughness of cut workpieces is evaluated from the point of view of the quality of surface roughness. A series of cutting experiments under different cutting conditions were carried out, and the possibility of deriving a suitable cutting condition for the ball end milling of sintered tungsten carbide is discussed.

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It has become important to consider energy-efficient optimization not only in a process design but also in the operations of manufacturing systems to promote sustainable and green manufacturing. This paper extends authors’ previous work to a more practical situation to demonstrate the applicability of the proposed framework of energy-efficient manufacturing operations based on a resource-constrained project scheduling problem (RCPSP). Both have varying resource requirements and multi processing modes, which can produce a suitable energy-load profiles for complete manufacturing systems. This study proposes a mathematical model for producing optimal energy-load profiles, and based on these profiles, each given operation is allocated to a machine tool with a specific processing mode. A processing mode refers to machining conditions for the corresponding operation, conditions that provide a predictive processing time and estimated electrical energy consumption. Through some cutting experiments on aluminum alloy performed on a three-axis machining center, we provide several possible processing modes for workpieces (operations), and we generate energy-load profiles by applying multi start local searches. We then discuss the applicability and capability of the energy-load profiles as an energy-aware production control.

Prediction of energy consumption in the entire production system is crucial for managing production and pursuing environmentally friendly manufacturing. One critical issue that must be addressed to realize green manufacturing is to construct a method for predicting the electric power consumed by each manufacturing device. To address this problem, we have proposed a regression-based power consumption model to predict in-process power consumption based on the strong correlation between MRR and SEC. This study is an extension of our previous work, and here, we conducted face milling experiments by utilizing ten different materials to demonstrate the applicability and generalization capability of the model. We focused on the face milling process and measured the power consumption of the machine tool during the milling process. We also determined the characteristics of the in-process power consumption in face milling from the viewpoint of SEC and MRR and the influence of the work material on SEC. The prediction accuracy of the proposed model is demonstrated by comparison with a conventional model. It was revealed that the proposed model can describe the influence of the entire machine tool on power consumption depending on the characteristics of the work materials.