Volume 175
Published on July 2025Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering
Machine learning (ML) has become a key driver of innovation in industrial manufacturing, enhancing quality control, predictive maintenance, and process optimization. Manufacturers can achieve improved efficiency, reduced costs, and enhanced operational reliability by leveraging advanced ML algorithms, such as deep learning and traditional models. However, challenges remain in the large-scale deployment of ML, including issues with data privacy, legacy system interoperability, and the need for high-quality datasets. This paper investigates three core research questions: the enhancement of manufacturing processes via ML algorithms, the technical impediments to ML implementation, and the resolution of these challenges through emerging technologies such as digital twins and IoT. The study reveals that ML has significantly improved fault diagnosis, reduced downtime, and optimized energy use. However, it also highlights ongoing concerns around data privacy and system integration. The paper concludes by discussing the potential of future technologies to advance ML adoption in manufacturing further while emphasizing sustainability and innovative manufacturing initiatives.
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The transformer model was used to train and generate story text this time because certain parts or endings of the original story were not satisfactory. This study tried to use the model training to obtain other story paths. The main purpose is to study two paths: one is how to use pre-trained models for fine-tuning to achieve the desired effect, and the other is how to build a model trained from scratch to achieve the desired effect. DeepSeek R1 will be used as a control group to evaluate the generation effect.According to the results, the pre-trained model performs better on smaller datasets, generating logical sentences and paragraphs, while the model trained from scratch has not yet achieved good results on smaller datasets. As an improvement measure, a larger dataset will be used to enhance the model's generation performance, while adjusting new hyperparameters to fit the dataset.
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