Application of non-dominated sorting genetic algorithm (NSGA-III) and radial basis function (RBF) interpolation for...

With the rapid development of wearable technology, smart contact lenses (SCL) are gradually gaining attention as a breakthrough innovation. The emergence of these products suggests that smart glasses, which incorporate electronic components and visual aids, are expected to become the mainstream of human-computer interaction in the future. However, realizing this vision requires not only advanced electronics but also highly sophisticated manufacturing processes. Therefore, this paper provides an in-depth discussion on the process of manufacturing smart contact lenses using in-mold electronic decoration technology and focuses on the multi-objective problem of optimizing injection parameters such as melt temperature and holding pressure to achieve on micro-molecular displacements as well as residual stresses. First, the background and technical requirements of smart contact lenses are described in detail, emphasizing the prospect of SCL for a wide range of applications in augmented reality, healthcare, and smart assistance. Subsequently, the key role of IME technology in SCL manufacturing is discussed. Focusing on the optimization of melting temperature, holding pressure and holding time, the effects of these three key parameters on eyewear were systematically analyzed with the goal of improving the overall performance and biocompatibility of SCL. The multi-objective optimization of melting temperature and holding pressure was achieved by NSGA-III. Radial basis function interpolation was used as an auxiliary method to provide finer optimization results for NSGA-III. During the multi-objective optimization process, efforts were made to achieve uniform flow of melt temperature and optimal adjustment of holding pressure to maximize the transparency, stability and comfort of SCL. The final results obtained achieved an optimization rate of 95.60% and 93.47% for nodal displacement and residual stress, respectively, compared with the initially recommended process parameters.