Peer-reviewed Journal Publications

(†: Corresponding Author, #: Equal Contribution)

• G. Kim, S. M. Yang, S. Jeon, S. Park, J.G. Choi, H.W. Park, and S. Lim†. “Development of a robust tool wear prediction method under novel operating conditions using deep unsupervised domain adaptation and physics-guided adjustment.” Advanced Engineering Informatics (Accepted for Publication)

• G. Kim, J.G. Choi, S. Jeon, S. Park, & S. Lim†. “Towards efficient data-driven fault diagnosis under low-budget scenarios via hybrid deep active learning.” Reliability Engineering & System Safety 266 (2026): 111637.

• Y. S. Kang, J. G. Choi, S. M. Yang, S. Lim, D.C. Kim†, &H.W. Park†. "Artificial Intelligence Augmented Digital Twin for Improving the Machinability in a Robotic Carbon Fiber-reinforced Plastics Machining Process." Journal of Intelligent Manufacturing (2025): 1-16.

• G. Kim, Y. S. Kang, S. M. Yang, J. G. Choi, G. Hwang, H. W. Park, & S. Lim†. “Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions.” Reliability Engineering & System Safety 253 (2025): 110549.

• G. Kim#, S. M. Yang#, D. M. Kim, J.G. Choi, S. Lim† & H. W. Park† (2024). 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)

• J.G. Choi, D.C. Kim, M. Chung, G. Kim, H.W. Park, & S. Lim†. “Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drilling.” Expert Systems with Applications (2024)

• G. Kim, J.G. Choi, & S. Lim†. “Using Transformer and a Reweighting Technique to Develop a Remaining Useful Life Estimation Method for Turbofan Engines.” Engineering Applications of Artificial Intelligence 133 (2024)

• G. Kim, S. Park, J.G. Choi, S.M. Yang, H.W. Park, & S. Lim†. “Developing a Data-driven System for Grinding Process Parameter Optimization Using Machine Learning and Metaheuristic Algorithms.” CIRP Journal of Manufacturing Science and Technology (2024)

• J.G. Choi#, D.C. Kim#, M. Chung#, S. Lim†, & H.W. Park†. “Multimodal 1D CNN for delamination prediction in CFRP drilling process with industrial robots.” Computers & Industrial Engineering (2024)

• G. Kim, S.M. Yang, S. Kim, D.Y. Kim, J.G. Choi, H.W. Park, & S. Lim†. “A multi-domain mixture density network for tool wear prediction under multiple machining conditions.” International Journal of Production Research (2023)

• G. Kim#, S.M. Yang#, D.M. Kim, S. Kim, J.G. Choi, M. Ku, S. Lim†, & H.W. Park†. “Bayesian-Based Uncertainty-Aware Tool-Wear Prediction Model in End-Milling Process of Titanium Alloy.” Applied Soft Computing (2023)

• G. Kim, J.G. Choi, M. Ku, & S. Lim†. “Developing a semi-supervised learning and ordinal classification framework for quality level prediction in manufacturing.” Computers & Industrial Engineering (2023)

• G. Kim#, D.H. Shin#, J.G. Choi, & S. Lim†. “A Deep Learning-based Cryptocurrency Price Prediction Model That Uses On-chain Data.” IEEE ACCESS (2022)

• G. Kim#, J.G. Choi#, M. Ku, H. Cho, & S. Lim†. “A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process.” IEEE Access (2021)

• J.G. Choi, C.W. Kong, G. Kim, & S. Lim†. “Car crash detection using ensemble deep learning and multimodal data from dashboard cameras.” Expert Systems with Applications (2021)

Working Paper

(†: Corresponding Author, #: Equal Contribution)

• J.G. Choi#, D.C. Kim#, D. Kim, G. Kim, S. Jeon, S. Lim†, & H.W. Park†. “Multi-source domain generalization for tool failure prediction in cryogenic milling of Ti-6Al-4V titanium alloy.” Applied Soft Computing (under review)

• J.G. Choi#, Y.S. Kang#, G. Kim, S. Lim†, & H.W. Park†. “Diff-S2I: Multimodal sensor-to-surface image generation via latent diffusion for industrial defect prediction.” Information Fusion (in preparation)

Peer-reviewed Conference Proceedings

(*: Presenter, †: Corresponding Author, #: Equal Contribution)

• J.G. Choi*, Y.S. Kang, H.W. Park, & S. Lim†. “Multimodal sensor-conditioned diffusion model for machined surface image synthesis” Korea Data Mining Society Summer Conference, Gyeongju, Republic of Korea, (Nov. 2024) - Poster.

• J.G. Choi*, & S. Lim†. “Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes.” 16th Annual Conference of the Prognostics and Health Management (PHM) Society, Doctoral Symposium, Tennessee, USA (Nov. 2024)

• J.G. Choi*, & S. Lim†. “Multimodal sensor-guided diffusion model for machined surface image synthesis.” 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), PhD Consortium, Barcelona, Spain (Aug. 2024) - Poster

• J.G. Choi*, D.C. Kim, M. Chung, H.W. Park, &  S. Lim†. “Sensor to Machined Surface Image Generation in CFRP Drilling.“ Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo 2023, New Orleans, Louisiana (May. 2023) - Oral

• J.G. Choi*#, D.C. Kim*#, D. Kim, H.W. Park†, & S. Lim†. “Autoencoder-Based Tool Failure Diagnosis and Prediction in Cryogenic Milling of Ti-6Al-4V Titanium Alloy.” International Symposium on Precision Engineering and Sustainable Manufacturing (PRESM) 2023, Okinawa, Japan (July. 2023) - Poster

• J.G. Choi*#, D.C. Kim#, D. Kim, H.W. Park†, &  S. Lim†. “A Contrastive Autoencoder-based Model for Tool Failure Diagnosis in Cryogenic Milling of Ti-6Al-4V Titanium Alloy.” PHM Korea 2023, Seoul, Korea (July. 2023) - Poster

• J.G. Choi*#, G.H. Kim#, S. Kim#, D.C. Kim, M. Chung, H. Park†, & S. Lim†. “AI-based delamination prediction and cause analysis in carbon fiber reinforced plastic (CFRP) drilling processes.” Korean Society for Precision Engineering Fall Conference, Daegu, Korea (Oct. 2022) - Poster

• J.G. Choi*#, D.C Kim#, M. Chung#, H. Park†, & S. Lim†. “A multimodal 1D convolutional neural network for delamination prediction in carbon fiber reinforced plastic (CFRP) drilling processes.” Joint conference of Korean Operations Research and Management Science Society & Korean Institute of Industrial Engineers, Jeju, Korea (Jun. 2022) - Oral

• D.C Kim*#, J.G. Choi#, M. Chung#, J.S. Kim, S. Lim†, &  H. Park†. “AI-based delamination prediction in carbon fiber reinforced plastic (CFRP) drilling processes with an industrial robot machining system.” The Korean Society of Manufacturing Technology Engineers, Jeju, Korea (Dec. 2021) - Poster

•  J.G. Choi*, C. Cong, G. Kim, & S. Lim†. “Car crash detection using ensemble deep learning and multimodal data from dashboard cameras.” Korea Safety Management & Science, Ulsan, Korea (Oct. 2021) - Oral

• G. Kim#, J.G. Choi#, M. Ku, H. Cho, &  S. Lim†. “Developing a deep learning-based fault detection model for plastic injection molding for car parts companies.” KSQM Spring Conference, Seoul, Korea (May. 2021) - Oral

• J.G. Choi*, C. Cong, & S. Lim†. “Developing machine-learning-based car crash detection systems using video and audio data.” Korean Institute of Industrial Engineers (KIIE), Seoul, Korea (Nov. 2019) - Oral

Patents

• S. Lim, J.G. Choi, M. Ku, &  S. Kim. “SYSTEM AND METHOD FOR ESTIMATING THREE-DIMENSIONAL POSE USING VISIBILITY SCORE.” KR-Application No. 10- 2022-0003775 (technology transfer)