Much attention is paid to developing a circular system of electric vehicle (EV) batteries towards a circular economy. Numerous circular battery business practices are found, but most of them are still at a small scale. Aiming to clarify challenges and opportunities to achieve a sustainable battery circular systems, this paper presents a summary of cross-country comparison between Japan, Germany, and Norway, which differ in terms of associated legislation and EV penetration.

This paper proposes a self-calibration method for 2.5-dimensional measurement of workpiece geometry performed on five-axis machine tools. The objective of the proposed method is to identify the geometry of a workpiece in the vertical Z-direction by on-machine measurement using a touch-trigger probe, when the machine tool has unknown error motions in the Z-direction. The part shape error determined by the proposed method is compared with a CMM measurement.

Persistent homology (PH) has become the most useful tool in topological data analysis, and is applied in a wide range of scientific and engineering fields. Furthermore, recent advances in imaging technology have made it crucial to develop efficient PH methods for images. In this research, we propose an efficient computational framework to estimate persistence diagrams (PDs) for 3D binary images. Our framework is based on approximating a 3D binary image by a Vietoris-Rips (VR) complex, and utilizes a popular PH library (GUDHI/Ripser) to compute its PD. Our VR-complex consists of a weighted proximity graph of connected regions in the 3D binary image, where its connectivity is determined by the geometric dual of the generalized Voronoi diagram obtained by the fast GPU-based method (PBA+) for the region boundaries, and half of the minimum distance between two regions is assigned to each edge weight. The vertex number of our graph is equal to the number of connected regions. Hence, the number of elements required to compute PD is significantly reduced compared with conventional representations such as alpha and cubical complexes consisting of dense voxels, which results in efficient PD estimations.

There is a growing interest in using X-ray CT data of industrial products for reverse engineering and model-based design. In this process, the image processing operation, called segmentation, for separating assembled parts into each single one is required. Currently, manual segmentation is often required and it costs a huge amount of man-time. In this study, we introduce a method that uses Active Contour and deep learning to apply a man-operated single segmentation to surrounding similar images.

In reverse engineering of precision mechanical parts, ensuring the quality of measurement data is crucial, as it forms the basis for accurate modeling. To validate the accuracy of measurements, it is necessary to repeatedly measure the same shape and assess the variability. In this study, a coordinate measuring machine (CMM) was used to perform multiple measurements of extrusion-shaped samples at various locations. Detailed analysis of the variability in the contour data obtained provided valuable insights for improving the accuracy of reverse modeling.