Abstract
Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recovery after the oral surgery procedures. We provided a deep learning network (DLN) with calibrated simulation data that came from different simulation conditions with experimental verification. The DLN achieves nearly exact result of simulated bone healing history around implants. The correlation of the predicted essential physical properties of surrounding bones (e.g. strain and fluid velocity) and performance indexes of implants (e.g. bone area and bone-implant contact) were greater than 0.980 and 0.947, respectively. The testing AUC values for the classification of each tissue phenotype were ranging from 0.90 to 0.99. The DLN reduced hours of simulation time to seconds. Moreover, our DLN is explainable via Deep Taylor decomposition, suggesting that the transverse fluid velocity, upper and lower parts of dental implants are the keys that influence bone healing and the distribution of tissue phenotypes the most. Many examples of commercial dental implants with designs which follow these design strategies can be found. This work demonstrates that DLN with proper network design is capable to replace complex, time-dependent, multi-physical models/theories, as well as to reveal the underlying features without prior professional knowledge.
Highlights
Machine learning provides high level of assistance to solve real physical problems and to accelerate engineering design process
The results proved that the current deep learning network (DLN) is capable to capture the hidden attributions and provide physical insights without professional supervision
A deep learning network which can instantly evaluate the performance of dental implants before surgeries is developed
Summary
Machine learning provides high level of assistance to solve real physical problems and to accelerate engineering design process. PauliNet outperforms conventional ansatzes for molecules with up to 30 electrons with high accuracy compared with it of QMC method while maintaining high computational efficiency. These cases demonstrated the possibility of using multiple networks to be an alternate solution for conventional numerical methods. The model can predict the distribution of the tissue phenotypes, such as resorption, mature bone, immature bone, cartilage, and fibrous tissue on each day during the healing period, which is difficult to be revealed experimentally This enables the preoperative evaluations for the dental implants, as well as reduces sacrifices of animals. The contribution of this work is to provide a proof of concept that deep learning approaches can be used as a highly-complex model substitute in on-fly inference, and most importantly, provide in-depth insight with no prior professional knowledge in dental clinical expertise
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