The Analysis of Optimization Strategy of Industrial Design in Automatic Sketch Generation Based on Deep Learning

• Published in: IEEE access Vol. 12; p. 1
• Main Author: Ye, Tian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automatic programming; Convolutional neural networks; Data models; Datasets; Deep learning; Design engineering; Design optimization; Evolutionary algorithms; Evolutionary computation; Generative adversarial network; Generative adversarial networks; genetic algorithm; Genetic algorithms; Graphics; Industrial design; Industrial engineering; Machine learning; Mathematical models; Optimization; Optimization algorithms; Optimization methods; optimization strategy; Parameters; Shape measurement; sketch generation; Sketches; Strategy
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3370438

This study is devoted to exploring the strategy of automatic sketch generation and optimization of industrial design based on deep learning. By combining the Generative Adversarial Network (GAN) with the optimization algorithm, this paper proposes an innovative method to realize the automatic generation of high-quality and diverse industrial design sketches. In the experiment, this paper selects SketchyCAD and other public data sets, trains them through deep learning model, and introduces genetic algorithm(GA) and differential evolution algorithm to optimize the parameters. In terms of experimental results, we observed that the quality of generated sketches was significantly improved, and the design sketches generated by the mode (GAN+GA) were more realistic and innovative. The introduction of optimization strategy further improves the generation effect and intelligently adjusts the model parameters to adapt to different design styles. In this paper, the influence of hyperparameter tuning is analyzed in detail, and it is found that the adjustment of learning rate plays a key role in generating quality and diversity. However, the experiment also revealed some challenges and room for improvement. We noticed that the generated results may have the risk of over-fitting in the training process, and with the increase of training times, the diversity gradually decreased. This suggests that more complex model structure and richer data sets are needed to improve the generalization performance. Generally speaking, this study provides new ideas and methods for the integration of deep learning and industrial design. By innovatively combining generation model and optimization algorithm, this research has contributed beneficial research results to the development of industrial design automation. This research is of great significance to promote the intelligence and innovation in the field of industrial design.