Research on a gingival line extraction method to automatically process invisible braces

Gingival line cutting is the core process of invisible brace fabrication. It mainly relies on manual or semi-automatic cutting, which results in low efficiency and a lack of precision. To solve the problems of the difficult acquisition of cutting path data for invisible braces and the cumbersome generation of the gingival line cutting code, a method for automatically extracting the gingival line and automatically generating a computerized numerical control (CNC) machining code was proposed. First, the gingival margin boundary contour data were extracted from the overall tooth model using the mean curvature as a judgment index. Then, a path search algorithm was designed to obtain the ordered and accurate gingival line feature point data, and the non-uniform rational B-spline interpolation method was applied to generate a complete, continuous, and smooth gingival line. Second, by converting the vectors of the gingival line data points into tool axis vectors, a software module for automatically compiling the machining program was developed in the Microsoft Visual Studio programming environment and integrated into a five-axis numerical control system to automatically generate a machining code. Based on this method, a visualized interface for automatic gingival line extraction and machining code generation was constructed in combination with Qt (a cross-platform program development library), and the effectiveness of the method was verified through several experiments. Finally, the gingival line data were applied to a five-axis machine tool to successfully achieve the automated cutting of invisible braces. Compared with manual processing, the quality of the automated cut invisible braces was more stable and the shape was closer to the gingival contour, which fully satisfies the processing requirements. The extraction efficiency of the automated extraction technique was significantly improved: 50.5 tooth models could be processed in one hour, whereas the manual extraction technique could only process 4.6 in the same amount of time. In other words, the proposed technique is seven times more efficient. The extraction accuracy improved by approximately 75 %. Thus, the automated extraction is significantly better than the manual extraction in terms of processing efficiency and accuracy, and it promotes the automation of invisible-brace fabrication.