To estimate weld quality of the resistance spot-welding, the acoustic emission features are investigated from the total acoustic emission signal at the single-spot weld. Typically, the resistance spot welding process consists of several stages: set-down of the electrodes, squeeze, current flow, forging, hold time, and lift-off. Various types of acoustic emission response corresponding to each stage can be separately analyzed by using back-propagation neural network classifier and wavelet transform technique. The presented machine learning results provide a validation for using back-propagation neural network and wavelet transform technique as a valuable insights into the resistance spot-welding process. Especially, a wavelet transform technique is demonstrated and the plots are very powerful in the recognition of the acoustic emission features

In this study, carbon/epoxy composite DCB(double cantilever beam) specimens based on K-means clustering and wavelet transform analyses are presented. For the fracture Mode I, the fiber orientation θ = [0 ]24 and θ = [±45]12 both shown up stable crack growth in DCB testing. For the fiber orientation θ = [0 ]24 , the continuous type AE signal showed at central frequency 130～270kHz, which means that matrix micro cracking was occurred. The Burst type AE signal was occurred at central frequency 200～300kHz due to fiber bridging and fiber breaking. Other burst type AE signals were occurred at central frequency 130～180kHz with very high amplitude due to fiber bridging. For the fiber orientation θ = [±45]12 , the burst type signal showed at central frequency 220～300kHz, which means that fiber breaking was occurred. Mixed type of burst and continuous signals were captured at central frequency 250～480kHz due to fiber friction.

This study deals with the high frequency induction hardening (HF at 850℃, 120kHz & 50kW condition) SM45C steel. (1) The HF specimen, which was tempered at 150℃, did not show any tempering effect. A brittle fracture occurred at rounded area of the tensile specimen. AE (acoustic emission) amplitude distribution showed between 45dB and 60dB. (2) A slip and fracture occurred at the hole area of the HF specimen which was tempered at 300℃. As they pass the yield point, the AE energy is increased intermittently and AE amplitude distribution exists between 70dB and 85dB. In addition, after imposing the maximum tensile load, AE signals showed high amplitude and energy distribution. The AE amplitude showed between 45dB and 70dB. (3) A brittle fracture occurred at HF specimen which was tempered at 450℃ as if it is torn in the direction of 45° on parallel area over the both sides of the tensile specimen, which lead to several peak appeared in AE energy. It was found that the AE amplitude was relatively low and the AE energy was high.

This study investigated tensile deformation of the stress aging heat-treated SM45C steel which are aging temperature at 250℃ and 300℃; aging time at 1 hour and, 3 hours; applied load at 300N and 400N by using an acoustic emission techniques (AEs). A signal processing technology is applied to evaluate an AE source characterization of different AE measurement systems DiSP & PCI-2. In this study, most suitable aging condition appeared at 250℃, 3 hours and 300N. But in cases of 250℃, 3 hours, 400N and 300℃, 3 hours, 400N conditions, yield load decreased compare to other conditions according to the over-aging phenomena. On the other hand, when arranged via AE amplitude results by K-means clustering pattern recognition of AE raw signals, tendency of signal strength appeared non-heat treatment condition, 'Class 1 < Class 2 < Class 3'; optimal condition, 'Class 3 < Class 2'; over-aging condition, 'Class 3 < Class 2 < Class 1'. This is judged by emitting a lot of AE energy when material causes plastic deformation because ductility increases on factor by over-aging phenomenon.

This study is deal with the high frequency induction hardening (HF at 850℃, 120kHz & 50kW condition) SM45C steel. (1) The HF specimen which was tempered at 150℃, did not appear any tempering effect. A brittle fracture occurred at rounded area of the tensile specimen. AE amplitude distribution showed between 45~60dB. (2) The HF specimen which was tempered at 300℃, slip and fracture occurred at the hole area of the tensile specimen. As it passes the yield point, the AE energy increased intermittently and AE amplitude distribution showed between 70~85dB. In addition, after the maximum tensile load, it showed high amplitude and energy distribution. The AE amplitude showed between 45~70dB. (3) The HF specimen which was tempered at 450℃, a brittle fracture occurred as if it is torn in the direction of 45℃ on parallel area over the both sides of the tensile specimen, which led to several peak to be appeared in AE energy. It was found that the AE amplitude was relatively low and the AE energy was high.

This paper investigates tensile characteristics of the stress aging heat-treated SM45C steel which are aging temperature at 250℃, 300℃, aging time at 1, 3 hours, and applied load at 300, 400N conditions by using acoustic emission. Most suitable aging condition was aging temperature 300℃, aging time 1 hour, and aging applied load 300N. And increased yield load 28.3% than non-treatment specimen in this condition. AE energy in elastic limit increased about 16.7 times than non-treatment specimen. When aging time is 3 hours, yield load decreased than other conditions that possibility is high to have itself defect on inside the specimen or coarse grain size precipitation is different in happened over-aging phenomenon. Especially, in case of 300℃, 3 hours and 400N condition appeared AE energy in elastic limit fairly high about 30 times than non-treatment specimen. This is considered by emit a lot of energies when material causes plastic deformation because the ductility increases on specimen by over-aging phenomenon.

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's λ, D&B(Rij) & Tou are discussed.

This paper investigates the optimal nesting system for a board. A hybrid method is used to search the optimal solution for rectangular nesting problem. This method is composed of heuristic approach algorithm. An engineer's experience of board nesting in which a loss occurred to sheet because of various individual error and diffidence. So, item layout at resource sheet were evaluated in engineering algorithm logic in which specially designed was installed. The nesting system consists of Lisp and Visual Basic. The system was controlled by AutoCAD so as to best item batch path test.

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Monotonic simple tension and AE tests were conducted against the 3 kinds of welded specimen. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multi-variate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

This paper investigates the optimal nesting system for a board. A hybrid method is used to search the optimal solution for rectangular nesting problem. This method is composed of heuristic approach algorithm. An engineer's experience of board nesting in which a loss occurred to sheet because of various individual error and diffidence. So, item layout at resource sheet were evaluated in engineering algorithm logic in which specially designed was installed. The nesting system consists of Lisp and Visual Basic. The system was controlled by AutoCAD so as to best item batch path test.

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Simple tension and AE tests were conducted against the 3 kind of welding test specimens. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multivariate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.