Capability Process to Optimize Specification by Impact of Wavelet Analysis

Abstract

This study investigates the impact of wavelet analysis on optimizing specification in the capability process. A new approach called the New Hybrid Capability Wavelet Approach (NHCWA) is proposed, which utilizes wavelet analysis to filter noise and outliers in the data. The study compares the NHCWA with the classical capability process using a dataset from a cake factory. The Coiflet 4 wavelet family and soft thresholding are employed to denoise the data. The results demonstrate that the NHCWA significantly improves the capability indices, achieving a 50% increase in Cp and a 30% increase in Cpk compared to the classical approach. Additionally, the NHCWA reduces the standard deviation of the process by 20%. These findings highlight the potential of wavelet analysis in enhancing the accuracy and effectiveness of capability analysis, leading to improved quality control in manufacturing processes