Deep Pattern Matching for Energy Consumption Prediction of Complex Structures in Ecological Additive Manufacturing

We propose a novel and effective deep learning method, called deep pattern matching, for predicting the energy consumption of complex structures, which helps designers to develop ecological solutions with minimal fabrication energy for additive manufacturing. This new method does not necessitate the real energy consumption values of complex structures for training the prediction model, substantially reducing the cost of training data collection, which can be prohibitively expensive. This novel method exploits simple structures whose real energy consumption values are far cheaper to measure, by matching the similar infill pattern of complex structures from simple structures, and then approximating the energy value of the pattern in the complex structures by the matched one in training phase. This effective algorithm is designed dynamically for allowing us to match patterns with arbitrary shapes. We evaluate our deep pattern matching algorithm on various complex structures, where the highest total energy accuracy is up to 97.3%. The extensive empirical results confirm the effectiveness and robustness of the proposed method, exhibiting a great potential to advance the real usage of deep learning models for energy consumption prediction of complex structures in ecological additive manufacturing.