Acoustic Emission Detection of Macro-Cracks on Engraving Tool Steel Inserts during the Injection Molding Cycle Using PZT Sensors

Abstract

This paper presents an improved monitoring system for the failure detection of engraving tool steel inserts during the injection molding cycle. This system uses acoustic emission PZT sensors mounted through acoustic waveguides on the engraving insert. We were thus able to clearly distinguish the defect through measured AE signals. Two engraving tool steel inserts were tested during the production of standard test specimens, each under the same processing conditions. By closely comparing the captured AE signals on both engraving inserts during the filling and packing stages, we were able to detect the presence of macro-cracks on one engraving insert. Gabor wavelet analysis was used for closer examination of the captured AE signals' peak amplitudes during the filling and packing stages. The obtained results revealed that such a system could be used successfully as an improved tool for monitoring the integrity of an injection molding process.

Development of A Low-Cost FPGA-Based Measurement System for Real-Time Processing of Acoustic Emission Data: Proof of Concept Using Control of Pulsed Laser Ablation in Liquids

“…Here, adhesive and abrasive wear could be distinguished according to the frequency content of AE signals [ 12 ]. Moreover, Svecko et al [ 13 ] used AE on an injection molding machine to detect damages of engraving tools during the manufacturing process. Furthermore, a review of AE related to chemical processes is provided by Boyd and Varley [ 10 ].…”

Acoustic Emission Signal Analysis for the Integrity Evaluation

“…Measurement of AE signals during production processes or during loading of different structures usually generates a high number of detected signals. These signals can be associated to a patterns (vectors) composed of multiple relevant descriptors [13] to [16] with the intention to discriminate different damage mechanisms described with clusters. The patterns (vectors) can be classified into clusters according to their similarity by the use of multivariable data analyses based on pattern recognition algorithms [17].…”

Thermally-Induced Crack Evaluation in H13 Tool Steel

“…The samples are initially immersed in molten aluminum, before being quenched in water bath at room temperature. At the end of each testing cycle, the processed samples were examined for the development of metallurgical and mechanical behaviors, as well as checking for the initiation of thermal cracks [22][23][24]. The oxidation and thermal cycle-induced damages on the sample's surfaces and transverse sections were examined under optical microscopy and Scanning Electron Microscopy (SEM).…”