Design and Optimization of a Novel Silicone Rubber Elastic Button based on BPNN Model

In this paper, based on modern Surface Mount Technology (SMT), a novel silicone rubber elastic button structure with built-in soldering pins for SMT applications is proposed, and the rationality of the proposed button structure is verified by the mechanical simulation. First, the effects of the elastic modulus of the matrix of button, the thickness of elastic oblique wall, the inclination angle of elastic oblique wall, and the stroke of the button on the pressing performance of the silicone rubber elastic button are studied, using an orthogonal experiment to analyze the influence significance of various factors on the tactility value. Second, the Back Propagation Neural Network (BPNN) model is employed for optimizing and selecting the structure parameters of the proposed SMT buttons. The result shows that the thickness of the elastic oblique wall has the most significant influence on the tactility value. Moreover, the BPNN model is useful for optimizing the proposed silicone rubber elastic buttons. Various designs for one tactility value of silicone rubber elastic buttons can be achieved through the application of BPNN model, which is of great significance for optimizing the product design and its process and significantly reducing the production cost.