2
Archived Theses
0
DOIs Assigned
0%
DOI Rate
Archived Theses
Otomotiv süspansiyon ve yönlendirme parçaları için yapay zeka tabanlı maliyet tahmini
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.
Akademik kalite ölçümü için yapay zeka tabanli bir yaklaşim
In the context of academic recruitment at universities and research institutions, establish- ing consistent and effective evaluation criteria remains a complex challenge. Identifying robust metrics that align with globally recognized standards of academic quality is essen- tial to ensure merit-based evaluation of researchers and maintain institutional credibility. In this thesis, we address this challenge through an AI-based solution aimed at developing a data-driven approach to quantify academic quality. As a benchmark of academic excellence, we use Nobel laureates' profiles in Physics, Chemistry, Physiology or Medicine, and Economics as a reference cohort. Comparison group includes researchers from the same fields affiliated with universities with an average ranking based on the Times Higher Education World University Rankings. By defining bibliometric features of the academic profiles of both Nobel laureates and the comparison group, we aim to develop machine learning models to quantify academic quality and identify the key features that define academic excellence. The ultimate goal is to support the decision-making process of universities and research institutions in academic recruitment, creating a fair and objective evaluation criteria.