Bidirectional process prediction in the laser-induced-graphene production using blackbox deep learning

Machine-learning techniques are highly advantageous for automation of a manufacturing process, since they facilitate prediction of the process parameters and product properties. However, the need for process-specific prior knowledge, for elaboration of complex analytical models, and for collection of a comprehensive training dataset notably limits their integration into the real-world applications. We pioneered and studied usage of a streamlined non-analytical approach to predict and optimize process parameters, radically adapted to the conditions of resource and knowledge constraints. The approach was employed for laser-induced graphene, which is an emerging flexible-electronics fabrication technique. The fabrication settings were successfully predicted and controlled by a blackbox neural network from the desired properties of the device, despite a small amount of moderate-quality training data. To prove feasibility of the concept, we designed and manufactured a functional electronic circuit. The proposed procedure is applicable for a broad range of functional materials and fabrication methods.