Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous. In this study, we proposed a defect detection model based on a parallel type convolution neural network for detecting bone plate crack and pore deformation. All size filters were different according to the defect shape. A convolution neural network detected pore defects. Another convolution neural network detected the crack. Two convolution neural networks simultaneously detected different defect types. The performance of the defect detection model was measured and used for the F1- score. We confirmed that performance of the defect detection model was 98.4%. We confirmed that the defect detection time was 0.21 seconds.

Bone plates made of biodegradable polymers have been used to fix broken bones. 3D printers are used to produce the bone plates for fracture fixing in the industry. The dimensional accuracy of the product printed by a 3D printer is less than 80%. Fracture fixing plates with less than 80% dimensional accuracy cause problems during surgery. There is an urgent need to improve the dimensional accuracy of the product in the industry. In this paper, a methodology using machine learning was proposed to improve the dimensional accuracy. The proposed methodology was evaluated through case studies. The results predicted by the machine learning methodology proposed in this paper and the experimental results were compared through the experiment. After verification, results of the proposed prediction model and the experimental results were in good agreement with each other.