We have proposed a machining method that utilizes the flank surface of an end mill by counter-rotating the tool, and have confirmed the application of compressive residual stress to the machined surface with the aid of ultrasonic vibration. Furthermore, when the machining point was observed with a high-speed camera, differences in machining phenomena were observed between the right and left side regions of the tool. This result indicates that machining characteristics differ depending on the pick-feed direction. Therefore, we attempted to experimentally clarify the machining characteristics depending on the pick-feed direction.

Usually, metallic rivets and bolts are used for the connection of vehicle parts. However, there is a dement that metallic rivets and bolts are heavy. The purpose of this research is to make automotive lighter. In order to achieve this purpose, in the present study, we investigated feasibility of blind rivet made of CFRTP. As a result, its joint strength was about 1kN.

Elastic stress waves (acoustic emission waves, AE waves) generated during deformation and fracture of materials are measured and analyzed to establish proactive condition monitoring techniques for various miniaturized manufacturing systems. This report summarizes the characteristics of AE signals obtained from various experimental investigations of miniature bearings, micro cutting, and micro grinding, and presents guidelines for proactive condition monitoring.

Acoustic emission (AE) is a phenomenon in which strain energy stored in a material is released as elastic waves. In this study, two AE signals were measured and evaluated simultaneously using two AE sensors installed at the tool and spindle sides for in-process measurement of cutting and spindle drive conditions on a micro machine tool. This time, the AE signals measured under different tool wear conditions are analyzed to investigate the correlation between cutting conditions and AE signal characteristics.

In the finishing process of diamond grinding, even the slightest defect in the process causes a significant reduction in product quality. Currently, contributions of veteran craftsmen to the in-process assessment of product state are essential for maintaining product quality. This study aims to measure elastic stress waves (acoustic emissions, AEs) generated by material deformation and fracture propagation, with the goal of establishing a novel technique for in-process assessment of the microscopic state of products. This paper reports on the effects of dressing and the adding free abrasive on AEs during the grinding process of a small-size single-crystal diamond using a compact grinding machine.

Nanoimprint lithography is a technology that allows for nano-order pattern transfer. This technology also allows for roll-to-roll transfer, like printing. This paper describes examples of roll nanoimprinting and its transfer accuracy.

It is needed to estimate the dynamic characteristics of the workpiece during processing to analyze the processing accuracy in grinding process. So far, though the dynamic characteristics of a workpiece during non-processing (at rest) have been measured by hammering tests, the dynamic characteristics of a workpiece during processing can not be measured. In this study, we measured the dynamic characteristics of the workpiece during contact between the grinding wheel and the workpiece using a measuring device based on non-contact excitation by electromagnetic force, and confirmed that the dynamic characteristics of the workpiece change according to the grinding efficiency.

Most previous research has observed the grinding phenomenon in the manner of either ex-situ or in-situ observation with visible light. However, these methods have significant limitations and falls short of meeting the requirements for DX manufacturing. In this paper, in process observation to the grinding process is achieved by the high-speed X-ray imaging technique. Because this imaging approach can observe the material removal phenomenon at grit-workpiece interface, more in-depth analysis of the grinding status, such as grit wear, clogging and surface integrity can be realised. Compared to the result in our first report, the image resolution is significantly improved and varied workpiece material including Pb and brass with different X-ray transmittance are employed in this work.

The chatter vibration that occurs during the gear grinding process has always affected the surface quality of products. To obtain more detailed information about chatter vibration in continuous generating grinding (CGG), vibration data was measured during grinding process. The frequency of vibration caused by the wheel-gear interaction was then identified to be 265 Hz. It was also observed that, throughout several additional experiments by varying wheel speed and axial feed, the vibration is closely related to the gear surface waviness. Finally, a vibration simulation model was constructed in Simulink by incorporating the regenerative chattering mechanism as well as the cutting force periodic variation. The proposed simulation successfully reproduced the 265 Hz vibration during grinding for input grinding conditions. And by removing the regenerative effect module, the 265 Hz vibration disappeared. The simulation also indicated that the occurrence of vibration during the grinding process leads to a higher likelihood of heat damage, also an increase of surface roughness by about 5% - 11%. Therefore, it can be reasonably claimed that the proposed model can provide effective help in understanding the surface quality, geometric accuracy, and heat behavior in CGG.

Methods for measuring approach distances of the microtool edge in evanescent light field on solid material surfaces such as glass and SiC has been established. However, it is necessary to bring the tool edge to several hundred nm from the surface, so tool edges and substrates may be broken because of touching. Accordingly, in order to examine the practicality of evanescent light on soft matter surfaces, we report on evaluating the measurement accuracy of the distance between the silicone surface and the tool edge height-controlled by piezoelectric element in this article.

The study aims to digitalize machined surfaces using image data for modeling and simulation of these surfaces in digital media. The primary profile of a machined surface consists of trend, noise, and burst. It is necessary to extract numerical data of trend, noise, and burst from the image data of a machined surface. In order to achieve this, this study develops an intelligent system. The efficacy of this system is shown using some real-life examples of machined surfaces. The results obtained can help create Digital Manufacturing Commons (DMC) of machined surface from the context of smart manufacturing.

In recent years, with the spread of IoT devices, a huge amount of data has been acquired from various sensors installed in processing machines. These data are expected to be applied to the failure detection in cutting tools, but detailed studies have not been sufficiently conducted. Therefore, in this study, for the tool failure detection, we report the results of clustering the distribution of cutting forces acquired during side-turning, using with the k-means method, which is one of the data science methods.