The web-enabled quality control process presents many benefits to industry, such as universal access, remote control capability, and integration of production equipment into information networks for improved efficiency. This capability has a great potential, since engineers can access and control the equipment anytime, anywhere as the design stages evolve. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of Support Vector machine learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions and a detailed analysis is presented for six different case studies. The results indicate the robustness of Support Vector classification for the experimental data with two output classes.
