In-Ho Hwang
Sungkyunkwan University · Engineering
About the Lab
Professor In-Ho Hwang's research lab specializes in advanced welding processes and quality monitoring, with a strong focus on the development of intelligent control systems and non-destructive evaluation techniques for high-strength and advanced high-strength steels. The lab investigates the hyponormality of Toeplitz operators in functional analysis, applying mathematical tools to understand operator theory and its applications in signal processing and system modeling. In materials processing, the lab emphasizes process optimization through current and force control in resistance spot welding, spectral monitoring in laser beam welding, and the prediction of weld quality using artificial intelligence, particularly artificial neural networks. The integration of mathematical analysis with industrial welding applications defines the lab’s interdisciplinary approach to improving manufacturing reliability and performance.
Research Overview
Research Output Trend
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Selected Papers
15In this paper we establish a tractable and explicit criterion for the hyponormality of arbitrary trigonometric Toeplitz operators, i.e., Toeplitz operators $T_{\varphi }$ with trigonometric polynomial symbols $\varphi$. Our criterion involves the zeros of an analytic polynomial $f$ induced by the Fourier coefficients of $\varphi$. Moreover the rank of the selfcommutator of $T_{\varphi }$ is computed from the number of zeros of $f$ in the open unit disk $\mathbb {D}$ and in $\mathbb {C}\setminus
In this note we consider the hyponormality of Toeplitz operators on the Bergman space (D) with symbol in the class of functions f + g with polynomials f and g
There is a welding problem such as expulsion in resistance spot welding of high strength steel. This is due to overheating induced by increase of total heat input. Recently, many studies are carried out to solve the problems using electrode force control and current control. In this study, we tried to achieve expulsion reduction through controlling welding current. Steel sheet coated by Al-Si that has strength of 1500 MPa was selected as a base material. The control results were compared with co
In this study, the effect of weld bead shape on the fatigue strength of lap fillet joints using the gas metal arc welding (GMAW) process was investigated. The base material used in the experiment was 590 MPa grade galvanealed steel sheet with 2.3 mm and 2.6 mm thickness. In order to make the four types of weld beads with different shapes by factors such as length, angle, and area, the welding process, wire feeding speed, and joint shape were changed. The stress-number of cycles to failure (S–N)
For the automation of a laser beam welding (LBW) process, the weld quality must be monitored without destructive testing, and the quality must be assessed. A deep neural network (DNN)-based quality assessment method in spectrometry-based LBW is presented in this study. A spectrometer with a response range of 225–975 nm is designed and fabricated to measure and analyze the light reflected from the welding area in the LBW process. The weld quality is classified through welding experiments, and the
In this study, the weld quality of 780 MPa grade dual phase (DP) steel with 1.0 mm thickness was predicted using adaptive resonance theory (ART) artificial neural networks. The welding voltage and current signals measured during resistance spot welding (RSW) were used as the input layer data, and the tensile shear strength, nugget size, and fracture shape of the weld were used as the output layer data. The learning was performed by the ART artificial neural networks using the input layer and out
In this note we consider the hyponormality of Toeplitz operators T on the Weighted Bergman space A 2 (D) with symbol in the class of functions f + g with polynomials f and g of degree 2.
Abstract In this note we consider the hyponormality of Toeplitz operators <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>T</mml:mi> <mml:mi>φ</mml:mi> </mml:msub> </mml:math> on weighted Bergman space <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>A</mml:mi> <mml:mi>α</mml:mi> <mml:mn>2</mml:mn> </mml:msubsup> <mml:mo>(</mml:mo> <mml:mi>D</mml:mi> <mml:mo>)</mml:mo> </mml:math> with symbol in the class of functions <mml:math xmlns:mm
Research Areas
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