Enhancing manufacturing sustainability has become more significant and urgent than ever before. In particular, energy demand for running machine tools greatly influences energy consumption in the manufacturing sector. As energy costs become expensive due to financial risks and political and national security risks, considering energy efficiency in manufacturing has become more important. Our previous work proposed an energy efficiency model based on specific energy consumption, indicating that a power function of the material removal rate represents energy efficiency as a performance measure. The model was examined solely in dry machining of aluminum alloys. In this study, we examine the effects of the material removal rate on energy efficiency in wet and dry machining of 12 types of metal materials. This study then demonstrates the capability of the proposed energy efficiency in machining by applying it to the assessment of energy efficiency between wet and dry end-milling processes. As a result, the proposed models can evaluate energy efficiency in machining under various conditions. Dry machining is advantageous to energy-efficient machining as the material removal rate increases.

Although Ti-6Al-4V has excellent mechanical properties, it is a difficult-to-machine material. Thus, highly efficient manufacturing methods for Ti-6Al-4V products is required. Laser metal wire deposition (LMWD), one of the metal additive manufacturing technologies, is expected to produce Ti-6Al-4V products with a minimum amount of cutting due to its high deposition rate and relatively high shape accuracy of deposited objects. In this study, the deposition conditions for Ti-6Al-4V using LMWD and the mechanical properties of the deposited objects were investigated. First, the deposition parameters without bead defects were investigated, and the conditions suitable for forming a block sample were selected in respect to the cross-sectional shape and the oxidation state of the beads. Next, the deposition paths that minimize internal defects and oxidation in multipath-multilayer deposition were investigated. A block sample was then deposited and its tensile properties and micro hardness were investigated. The deposited Ti-6Al-4V block sample was heavily oxidized and exhibited high tensile strength, proof stress, hardness, and low elongation.

In dies and molds machining, high surface quality is necessary to manufacture current industrial products. Free-form surfaces of die and mold are usually machined using reciprocating tool paths with a ball-end mill. However, position error of tool center points may occur in the tool paths generated by using a computer-aided manufacturing (CAM) software. Because the position error causes a lack of uniformity of the machined surface, the tool center points are modified by skillful operators to achieve high surface appearance. Therefore, a modification method to correct the position error is devised to automate the manual modification in this study. In the method, sensitivity is defined as the index to quantify the influence of position error on uniformity of the machined surface. The tool center point having the position error is detected by introducing the thresholds based on the sensitivity and an appearance indicator that is the degree of non-uniformity of the machined surface. Then, the coordinates of the tool center point are automatically compensated to satisfy the targeted appearance. Simulation results confirm that the indicator meets the targeted value by the modification. Finally, the machining experiments demonstrate that the proposed modification method can be used to improve surface appearance.

This study proposes a rotation-robust anomaly detection method based on inverse projection error from a normal feature representation space. Conventional methods often have trouble detecting anomalies in objects that rotate, as their anomaly metrics, like Mahalanobis and Euclidean distances. Our method addresses this by creating normal representation space using only normal samples, capturing normal feature variations through principal component analysis (PCA) in a low-dimensional subspace. We then project test features into this space and inverse-project them back, using the difference between the original and reconstructed features as an anomaly score. This score helps distinguish between normal and abnormal features even when objects are rotated. Unlike deep learning methods, which often require more complex processing, this approach is simpler. Tests on the MVTec AD dataset show that our method performs well, achieving an AUROC of 96.8% for the Screw class, which is higher than PaDiM's 94.9% and PatchCore's 95.6%. This shows our method's strong ability to detect anomalies in rotated objects, making it effective for manufacturing needs and adaptable to various inspection scenarios.