Park S#, Yang SM#, Kim G#, Kim DM, Kim DC, Lee H, Choi JG, Jeon S, Lim S*, Park HW*. Towards holistic machinability estimation of titanium alloy: An integrated approach with enhanced feature extraction and physics-guided deep multi-task learning. Advanced Engineering Informatics. Under Review.

Lim C, Kim G*, Lim S*. A deep active learning framework for defect classification of wafer bin maps under noisy labels. Advanced Engineering Informatics. Under Review.

Kim G, Yang SM, Jeon S, Park S, Choi JG, Park HW, Lim S*. Robust tool wear prediction under novel operating conditions via physics-guided unsupervised domain adaptation. Advanced Engineering Informatics. 2026 Jan 1;69:103883.

Kim G, Choi JG, Jeon S, Park S, Lim S*. Towards efficient data-driven fault diagnosis under low-budget scenarios via hybrid deep active learning. Reliability Engineering & System Safety. 2026 Feb 1;266:111637.

Choi G#, Kim DC#, Kim DM, Kim G, Jeon S, Lim S*, Park HW*. Supervised contrastive learning for multi-source domain generalization: Tool failure prediction in data-deficient cryogenic milling processes. Applied Soft Computing. 2026 Feb 1;187:114217.

Hwang G#, Kim J#, Choi J#, Han G, Kim G, Lim S*. Class-imbalanced gas thermal image detection in manufacturing using thermal region masking and density-based reweighting. Applied Soft Computing. 2026 Jan 1;186:114186.

Kim G, Kang YS, Yang SM, Choi JG, Hwang G, Park HW, Lim S*. Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions. Reliability Engineering & System Safety. 2025 Jan 1;253:110549.

Choi JG#, Kim DC#, Chung M, Kim G, Park HW*, Lim S*. Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drilling. Expert Systems with Applications. 2024 Dec 1;255:124656.

Kim G#, Yang SM#, Kim DM, Choi JG, Lim S*, Park HW*. Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V. Journal of Manufacturing Systems. 2024 Oct 1;76:133-57.

Kim G, Choi JG, Lim S*. Using transformer and a reweighting technique to develop a remaining useful life estimation method for turbofan engines. Engineering Applications of Artificial Intelligence. 2024 Jul 1;133:108475.

Kim G, Park S, Choi JG, Yang SM, Park HW, Lim S*. Developing a data-driven system for grinding process parameter optimization using machine learning and metaheuristic algorithms. CIRP Journal of Manufacturing Science and Technology. 2024 Jul 1;51:20-35.

Kim G, Yang SM, Kim S, Kim DY, Choi JG, Park HW, Lim S*. A multi-domain mixture density network for tool wear prediction under multiple machining conditions. International Journal of Production Research. 2023 Dec 5:1-20.

Kim G#, Yang SM#, Kim DM, Kim S, Choi JG, Ku M, Lim S*, Park HW*. Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy. Applied Soft Computing. 2023 Nov 1;148:110922.

Kim G, Choi JG, Ku M, Lim S*. Developing a semi-supervised learning and ordinal classification framework for quality level prediction in manufacturing. Computers & Industrial Engineering. 2023 Jul 1;181:109286.

Kim G#, Shin DH#, Choi JG, Lim S*. A deep learning-based cryptocurrency price prediction model that uses on-chain data. IEEE Access. 2022 May 25;10:56232-48.

Kim G, Lim S*. Development of an interpretable maritime accident prediction system using machine learning techniques. IEEE Access. 2022 Apr 18;10:41313-29.

Choi JG, Kong CW, Kim G, Lim S*. Car crash detection using ensemble deep learning and multimodal data from dashboard cameras. Expert Systems with Applications. 2021 Nov 30;183:115400.

Kim G#, Choi JG#, Ku M, Cho H, Lim S*. A multimodal deep learning-based fault detection model for a plastic injection molding process. IEEE Access. 2021 Sep 24;9:132455-67.

Park S#, Yang SM#, Kim G#, Kim DM, Lee HH, Choi JG, Jeon S, Lim S*, Park HW*. Accurate monitoring of machining process of Ti-6Al-4V using deep multi-task learning. In 2024 International Conference on Advanced Mechatronic Systems (ICAMechS) 2024 Nov 26 (pp. 147-152). IEEE.

Kim G#, Yang SM#, Kim S, Kim DM, Lim S*, Park HW*. Tool wear prediction in the end milling process of Ti-6Al-4V using Bayesian learning. In 2022 International Conference on Advanced Mechatronic Systems (ICAMechS) 2022 Dec 17 (pp. 64-69). IEEE.