Narrow Results

Grinding is used to improve surface roughness and dimensioning precision in the metal industry. A large amount of heat is released during grinding. Most of this heat is transferred to the workpiece. In this case, a grinding burn may occur on the workpiece. Grinding burn is a significant issue in the production of bearings. If a burn occurs on the workpiece during grinding, the surface quality deteriorates and the internal structure and mechanical qualities of the material are adversely affected. Therefore, detecting grinding burn is critical for bearing manufacturers. In this study, during the grinding of the bearing parts, the machine conditions were changed in accordance with the real grinding scenario, and burnt and non-burned bearing data were obtained with the acoustic emission sensor. Then, time-frequency representations were obtained from these data with the continuous wavelet transform. These images have been classified in the GoogLeNet Network by transfer learning. Combinations of faulty/ normal data processed under different machine settings and combinations of faulty/ normal data processed with the same machine parameters were classified with the proposed method and compared. Faulty bearings processed with the same machine characteristics were detected with 100% accuracy using the proposed method. This percentage gives a reliable result for bearing producers. This study contributes to the literature in three ways: (a) It is based on data collected under real-world grinding situations. (12 different machine settings were employed.) (b) Various machine conditions were compared. (c) Faulty bearings were detected with high accuracy.

The numerical solution of the elastodynamic problem with kinematic boundary conditions is considered using mixed finite elements for the space discretization and a staggered leap-frog scheme for the discretization in time. The stability of the numerical scheme is shown under the usual CFL condition. Using the general form of Robin-type boundary conditions some cases of debonding and the resulting acoustic emission are studied. The methodology presented finds applications to geophysics such as in seismic waves simulation with dynamic rupture and energy release. In this paper, we focus on problems of fracture occurring at the interface of composite materials. Our goal is to study both the mechanism of crack initiation and propagation, as well as the acoustic emission of the released structure-borne energy which may be used in structural health monitoring and prognosis applications.

Nondestructive evaluation (NDE) of materials is now widely used in defect detection and classification, material characterization and discrimination, and machine inspection. To improve the capability of NDE instrumentation, pattern recognition is the key to automating the inspection process as well as providing information for making objective decisions. This chapter presents the major activities and results of pattern recognition in NDE, including both traditional statistical pattern recognition and modern neural network classifiers for ultrasonic, acoustic emission and eddy current signals and radiographic images.

Nondestructive evaluation (NDE) of materials is now widely used in defect detection and classification, material characterization and discrimination, and machine inspection. To improve the capability of NDE instrumentation, pattern recognition is the key to automating the inspection process as well as providing information for making objective decisions. This chapter presents the major activities and results of pattern recognition in NDE, including both traditional statistical pattern recognition and modern neural network classifiers for ultrasonic, acoustic emission and eddy current signals and radio-graphic images.