A Proposed Non-Destructive Method Based on Sphere Launching and Piezoelectric Diaphragm.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The pencil-lead break (PLB) tests were also conducted for comparisons with the proposed method.

Thermal Modeling of Tool Temperature Distribution during High Pressure Coolant Assisted Turning of Inconel 718.

This paper presents a finite-element modeling (FEM) of tool temperature distribution during high pressure coolant assisted turning of Inconel 718, which belongs to the heat resistance superalloys of the Nickel-Chromium family. Machining trials were conducted under four machining conditions: dry, conventional wet machining, high pressure coolant at 50 bar, and high pressure coolant at 80 bar. Temperature during machining plays a very important role in the overall performance of machining processes.

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1-PZT Diaphragm Transducer Response and EMI Sensing Technique.

Low-cost piezoelectric lead zirconate titanate (PZT) diaphragm transducers have attracted increasing attention as effective sensing devices, based on the electromechanical impedance (EMI) principle, for applications in many engineering sectors. Due to the considerable potential of PZT diaphragm transducers in terms of excellent electromechanical coupling properties, low implementation cost and wide-band frequency response, this technique provides a new alternative approach for tool condition monitoring in grinding processes competing with the conventional and expensive indirect sensor monitoring methods. This paper aims at assessing the structural changes caused by wear in single-point dressers during their lifetime, in order to ensure the reliable monitoring of the tool condition during dressing operations.

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2-Neural Networks and K-Nearest Neighbor Classifier Approach.

This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools.