A mechanism motion error sensitivity analysis method based on principal component analysis and artificial neural network

Present sensitivity analysis of motion error usually focuses on the trajectory deviation of the mechanism, which inevitably introduces an intractable time dependent problem. For efficiently and accurately measuring the motion error of the planar mechanism with dimension and clearance uncertainties by global sensitivity analysis (GSA), a novel method is proposed in this work. By applying the principal component analysis (PCA), the motion error is transformed into new vector output and cleverly avoids the time dependent problem. To ensure the accuracy of PCA in the case of small samples, the Bootstrap method is introduced. Based on the PCA results, the artificial neural network (ANN) surrogate model is established between the input variables and the vector output. Then the classical variance-based GSA method is applied to obtain the variable importance ranking for different PCs, and the synthesized GSA indices are introduced. Four representative examples are studied to demonstrate the versatility and effectiveness of the proposed method.

Automated summary

A novel method is proposed to efficiently and accurately measure the motion error of planar mechanisms with dimension and clearance uncertainties by using global sensitivity analysis (GSA). This method transforms the motion error into a new vector output through principal component analysis (PCA) to avoid the time dependent problem. The artificial neural network (ANN) surrogate model is established based on the PCA results, and the classical variance-based GSA method is applied to obtain the variable importance ranking for different principal components (PCs) and introduce the synthesized GSA indices. Four representative examples are studied to demonstrate the versatility and effectiveness of the proposed method.