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. 2026 Sep 1;74:104621.

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.