Shinzo Enomoto

Effect of grinding parameters on acoustic emission signals while grinding ceramics

Abstract Grinding process of engineering ceramics is always accompanied by cracking. For automation of machining process it is necessary that a reliable sensing system be devised to detect the workpiece cracking during grinding. In this paper an acoustic emission (AE) sensor was examined for in-process detection of workpiece conditions. Different grinding conditions were performed for evaluation of sensitivity of AE and understanding the effect of each grinding parameter on AE activities during grinding process of alumina ceramics. The results of experiments indicate that AE activities increase with increasing wheel depth of cut and table speed, however when the wheel speed increases, AE activities decrease. As a result, it is shown that AE is basically a function of abrasive grain depth of cut which is in turn, the main factor for determining the surface integrity of fine ceramics.

Acoustic Emission Characteristics in Multipoint Scratching of Fine Ceramics

Using acoustic emission (AE) signal analysis for in-process monitoring of grinding damage in ceramic workpieces requires understanding the characteristics of AE signal during abrading in a free-of-noise environment. In this work multipoint scratching tests were performed using segments of different diamond grinding belts as a model for the grinding process and AE signals were detected and analyzed. Microscopic observations of surfaces and chips were used for understanding the mechanism of material removal and for evaluation of AE data. These observations confirm that the removal mode is a mixture of plastic deformation and fracture. As a result, AE activities were found to be highly sensitive to the material removal mode and phenomena such as the abrasive belt performance, adhesive force and elasticity of the bond, sinking of abrasive grains into the bond, and wear and abrasive separation from the bond.

Study on Visual Evaluation for Barrel Finished Surfaces

In this paper, an intelligent visual evaluation system using a neural network is developed in order to evaluate the barrel finished surfaces. It is well known that the surface integrity after barrel finishing depends on type of finishing machines, nature of the media and the compounds. The integrity of barrel finished surfaces, when different compounds were used, have typically been evaluated by visual inspection of expert workers. It is necessary to introduce the optical qualitative evaluation system instead of visual inspection in an automated inspection line. However, it is very difficult to evaluate the finished surface using image processing and/or other measuring equipment, because of complexity of data analyses for slightly different light reflection data among each finishing condition. As a result, not many papers have been published on the optical evaluation system for the barrel finished surfaces when different compounds were used. The objectives of the paper are to analyze the image of barrel finished surfaces using image processing, and to construct a neural network system which is able to evaluate the finished surface integrity. In this paper, it is found that features of barrel finished surfaces are decided by image analyses. As a result, it is possible to construct a neural network system composed of the finished surface image features as input layer and grades by the sensory test as output layer.

2-15 Project Management Education for New Manufacturing System

The specific ebjective of this paper is to verify the impact of etiucation effects in field of prQject maagement to train the both creation and system integration capability through the case stud.y. Tlie research and deve]opment (R&D) project for the factory of the future using network technology has been introdueed to train lhe ereatien and integration eapabi]ities efthe students. As the results. the