AI-Based cost estimation for automotive suspension and steering parts

This thesis presents a machine learning approach that predicts unit manufacturing cost directly from two dimensional engineering drawings of suspension and steering parts. The dataset contains 13,684 drawings grouped into twenty four product categories. Drawings are parsed to extract geometric and dimensional entities, and about two hundred features are derived for each case. These features include descriptive statistics for lines, arcs, circles, and dimension values, histogram summaries with twelve bins, and distances between drawing level histograms and product group references using the Euclidean distance and the Kullback--Leibler divergence. Cost is modeled with methods that use gradient boosting with decision trees. Three machine learning algorithms are evaluated, namely XGBoost, CatBoost, and LightGBM. Models are trained per product group under a common validation and tuning protocol. Across the twenty four groups, the average mean absolute percentage error (MAPE) for XGBoost, CatBoost, and LightGBM is about 11% on the test dataset. Interpretation through feature importance and SHAP shows that maxima of rotated dimensions, statistics that describe arc geometry, and divergence measures from the histogram comparisons are consistent drivers of cost. The proposed end-to-end pipeline has the potential to operate without detailed process plans, shorten quotation lead times, improve consistency within part families, and be linked to design or enterprise systems to provide near real-time cost feedback.