Edge–Cloud Cooperation-Driven Sustainable Smart Optimization Strategy for Additive Manufacturing
提出一种边缘-云端协同的智能优化策略，通过CNN-Transformer混合模型预测工艺参数，并利用改进Pareto算法优化表面粗糙度、加工时间和能耗，在选择性激光熔化技术中验证了降低能耗和加工时间的效果。
Additive manufacturing (AM) is widely used in fields, such as aerospace and medical treatment. However, the massive heterogeneous data generated during its production process face challenges, such as high transmission latency and large energy consumption. This article proposes a sustainable intelligent optimization strategy based on edge–cloud collaboration to enhance the intelligence and sustainability of AM. First, a hybrid model that integrates the local feature extraction of convolutional neural network (CNN) and the global dependency modeling of transformer (CNN–transformer) is designed to accurately predict the key process parameters of AM. Second, a multiobjective optimization model for surface roughness, processing time, and energy consumption is constructed. Combined with the improved Pareto set learning (PSL) algorithm, the collaborative optimization of economic and environmental sustainability is achieved. Finally, verification is carried out on selective laser melting (SLM) technology. The experimental results show that the prediction error of the CNN–transformer is lower than that of traditional models. It can reduce energy consumption and processing time while ensuring surface quality, thus providing a systematic solution for green intelligent manufacturing from Industry 4.0 to Industry 5.0.