Manufacturing Engineering
基于机器学习的表面粗糙度预测
Machine Learning-Based Surface Roughness PredictionA framework utilizing Machine Learning (ML) to estimate surface roughness (Ra) of machined components, specifically drilled holes, using image-derived texture features, machining parameters, and chip morphology descriptors. / 一种利用机器学习（ML）根据图像纹理特征、加工参数和切屑形态描述符来估算加工件（特别是钻削孔）表面粗糙度（Ra）的框架。
- Knowledge type
- 实际应用
- First proposed or discovered
- 2026
- Proposer
- Shilpa Karegoudra, Vamsidhar Yendapalli
- Creator
- 农场主
- Last updated
- 2026-10-31 18:07:44
Sub-knowledge points
Linear Regression (LR) with GLCM texture featuresExtreme Gradient Boosting (XGBoost) combining texture and parametersXGBoost using chip morphology features
Higher-order knowledge points
No connected node yet
Main parameters and performance indicators
Texture features: Gray-Level Co-occurrence Matrix (GLCM)Machining parameters: Spindle speed, Feed rateChip morphology descriptors: Chip area, Curl index
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsMachine Learning-Based Surface Roughness Prediction of Drilled Hole Surfaces Using Texture and Chip Morphology FeaturesOpen source
Related products and links
Streamlit-based interface
Creation and modification trail
1 recordsRecord title: Machine Learning-Based Surface Roughness Prediction.
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