Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model

Accurately predicting fabricating cost in a timely manner can enhance corporate competitiveness. This study employs the Evolutionary Support Vector Machine Inference Model (ESIM) to predict the cost of manufacturing thin-film transistor liquid–crystal display (TFT-LCD) equipment. The ESIM is a hybrid model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM concerns primarily with learning and curve fitting, while the fmGA is focuses on optimization of minimal errors. Recently completed equipment development projects are utilized to assess prediction performance. The ESIM is developed to achieve the fittest C and γ parameters with minimized prediction error when used for cost estimate during conceptual stages. This study describes an actionable knowledge-discovery process using real-world data for high-tech equipment manufacturing industries. Analytical results demonstrate that the ESIM can predict the costs of manufacturing TFT-LCD fabrication equipment with sufficient accuracy.

Research highlights
► High-tech equipment development cost prediction using hybrid intelligence of SVM and fmGA.
► Manufacturing cost estimation for Macro, LOT, and Repair systems.
► The SVM concerns with learning and curve fitting, while the fmGA focuses on optimization of minimal errors.
► The ESIM is developed to achieve the fittest C and γ parameters with minimized prediction error.