Data Mining and Machine Learning Modeling

multi-source data integration, data cleaning, denoising, missing-value handling, outlier detection, feature engineering, dimensionality reduction, regression / classification, cross-validation, SHAP interpretability, literature data extraction, spectral / cycling time-series data

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Calculation Purpose

Clean and engineer features from multi-source research data (experiments, characterization, calculations, literature), build regression or classification models with cross-validation and interpretability analysis, and report the key factors and the model's domain of applicability.

Case Reference

Material-agnostic machine learning approach enables high relative density in powder bed fusion products

This study introduces a method that is applicable across various powder materials to predict process conditions that yield a product with a relative density greater than 98% by laser powder bed fusion. We develop an XGBoost model using a dataset comprising material properties of powder and process conditions, and its output, relative density, undergoes a transformation using a sigmoid function to increase accuracy. We deeply examine the relationships between input features and the target value using Shapley additive explanations. Experimental validation with stainless steel 316 L, AlSi10Mg, and Fe60Co15Ni15Cr10 medium entropy alloy powders verifies the method’s reproducibility and transferability. This research contributes to laser powder bed fusion additive manufacturing by offering a universally applicable strategy to optimize process conditions.

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