Korea Advanced Institute of Science and Technology
Assembly Line
Development of an injection molding production condition inference system based on diffusion model
Plastic injection molding is a crucial process for the mass production of various products. However, traditional methods for setting production conditions have heavily relied on skilled operators to adjust parameters through trial and error. This approach is not only inefficient but also results in inconsistent quality control. To address these challenges, this study proposes a new machine learning based model that automatically infers process parameters, enabling real time adaptation to external environmental changes. A surrogate model is first developed to learn the relationship between process parameters, environmental variables, and product quality, predicting whether a given set of parameters will result in a good or defective product. Building on this, a diffusion model, a type of deep generative model, was employed to generate diverse sets of process parameters likely to yield defect free products under specific environmental conditions. The proposed diffusion model outperforms existing generative models such as generative adversarial network (GAN) and variational autoencoder (VAE) in both accuracy and diversity of generated parameters. Notably, the diffusion model achieved an error rate of 1.63%, significantly outperforming GAN and VAE, which exhibited error rates of 23.42% and 44.54%, respectively. Additionally, the applicability of the proposed diffusion model was experimentally validated in a real world testbed. Several experiments conducted under various external environmental conditions demonstrated that the quality of the products produced using the process parameters generated by the diffusion model matched the quality predicted by the model. This study introduces a novel approach to improving both the efficiency and quality of injection molding processes and holds promise for broader applications in manufacturing.
New System Monitors EV Batteries
Engineers at the Korea Advanced Institute of Science & Technology (KAIST) have developed a way to precisely determine the health of batteries by only using small amounts of electrical current. Their electrochemical impedance spectroscopy (EIS) technology may improve the stability and performance of high-capacity batteries in electric vehicles. In addition to assessing the state of charge and state of health of batteries, the tool can be used to identify thermal characteristics, chemical or physical changes, predict battery life and determine the causes of failures.
Traditional EIS equipment is expensive and complex, making it difficult to install, operate and maintain. And, due to sensitivity and precision limitations, applying current disturbances of several amperes to a battery can cause significant electrical stress, increasing the risk of battery failure or fire. The KAIST system can precisely measure battery impedance with low current disturbances, minimizing thermal effects and safety issues during the measurement process. It minimizes bulky and costly components, making it easy to integrate into EVs.