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This study developed a model to predict the tensile shear strength of Gas Metal Arc Welding (GMAW) welds using statistical analysis of laser vision sensor profiles and Gaussian Process Regression (GPR). Despite GMAW's widespread use, welding defects due to disturbances like thermal deformation can compromise mechanical properties. To address this, weld bead external profile data, acquired via a laser vision sensor, and process variables were utilized for model development. A cumulative sum (CUSUM)-based change point detection algorithm extracted top and bottom plate boundary points rapidly and accurately from weld bead profiles. These points were transformed to minimize measurement condition influence. Subsequently, a GPR model was constructed, employing these transformed feature points and process variables as inputs, to predict tensile shear strength. The model demonstrated excellent predictive performance with an R² of 0.9587, RMSE of 13.9447 MPa, and MAPE of 8.6958 %. Analysis revealed voltage setting was the most influential variable in predicting tensile shear strength. Transformed feature point coordinates, representing the distance from the bottom plate boundary to the weld reinforcement feature point, also showed significant influence. This study confirmed that the tensile shear strength of GMAW weldments can be predicted accurately with limited input data and fast processing time.

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권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 목차
(A) study on real-time process defect monitoring based on object detection in coaxial LW-DED = 동축 LW-DED 공정에서의 객체 탐지 기반 실시간 공정 결함 모니터링에 관한 연구 Taesoon Jeong, Taehwan Ko, Seong Hun Ji, Hyub Lee, Seung Hwan Lee p. 343-355, [1-13]

Development of tensile shear strength prediction model for GMAW welds using laser vision sensor and Gaussian process regression = 레이저 비전 센서와 가우시안 프로세스 회귀를 이용한 GMAW 용접부 인장전단강도 예측 모델 개발 Sol Mi Lee, Dong-Yoon Kim, Jong-Kyu Park, Daewon Kim, Jiyoung Yu, Seung Hwan Lee p. 356-363, [1-7]

Machine learning-based fatigue life prediction using weld geometry of lap GMAW joints = GMAW 겹치기 용접부의 형상 인자를 이용한 머신러닝 기반 피로 수명 예측 Jaesang Lee, Jiyoung Yu, Jong-Kyu Park, Daewon Kim, Dong-Yoon Kim, Dongchoul Kim p. 364-376, [1-13]

DNN-based quality monitoring of Al-Cu dissimilar dual-beam laser welding using spectrometer and photodiode signals = DNN 모델기반 Al-Cu 이종재 듀얼빔 레이저 용접 품질 모니터링 연구 Byeong-Ju Jin, Seung Hwan Lee, Hui Jun Lee, Young Kim p. 377-385, [1-8]

(A) study on the prediction performance of the temperature field in the GMAW process according to the collocation point distribution of the physics-informed neural network (PINN) = Collocation point 분포에 따른 물리기반 인공신경망(PINN)의 GMAW 공정의 온도장 예측 성능에 관한 연구 Younghoon Lee, Jaeheon Lee, Hwani Hwang, Seung Hwan Lee p. 386-399, [1-13]

Study of the deposition parameter and mechanical properties of arc-based 3D printing using solid wire = 솔리드 와이어를 이용한 아크기반 금속 3D 프린팅 적층 매개변수 및 기계적 특성 비교 Young Keun Park, Jooyong Cheon, Jae-Deuk Kim, Changwook Ji p. 400-412, [1-12]

Thermo-mechanical coupled finite element analysis of ultrasonic metal welding in multilayer copper foil stacks = 다층 구리 포일 스택의 초음파 용접에 대한 열-기계 연성 유한요소해석 Yong-Hyuk Kwon, Hee-Seon Bang, Bum-Su Go p. 413-422, [1-9]

CALPHAD-based thermodynamic modeling in welding and joining processes : applications and technical implications Young-Min Kim, Insung Hwang p. 423-435

Optimizing welding parameters to mitigate hydrogen-induced cracking under varying relative humidity conditions using the G-BOP test Arash Dehghan, Alireza Ebrahimi, Eslam RanjbarNoodeh, Ashkan Dadrasi p. 436-446

(A) force based determination of hot cracking susceptibility Philipp Liepold, Arne Kromm, Thomas Kannengiesser p. 447-457

(A) study on keyhole-plasma behavior and multi-sensor correlation analysis in laser welding = 레이저 용접에서 키홀-플라즈마 거동 및 다중 센서 상관성 분석에 대한 연구 Seong Jun Mun, Yong Joon Cho, Young Whan Park p. 458-468, [1-11]

(A) study on the optimization of reinforcement bead geometry and stiffness enhancement in automotive body structures using arc-based deposition = 아크 기반 적층을 활용한 자동차 차체 보강 비드 형상 최적화 및 강성 향상에 관한 연구 Hyeon-Do Jeon, Su-Min Park, Jae-Hyeok Lee, Yeong-Do Park p. 469-481, [1-12]

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