Surface Roughness

Surface roughness, a measure of a surface's texture irregularities, is crucial for determining the performance and functionality of various materials and components. Current research heavily emphasizes developing accurate and efficient prediction models for surface roughness across diverse manufacturing processes (e.g., milling, additive manufacturing, polishing) using machine learning techniques, including random forests, Bayesian hierarchical models, and even quantum algorithms. These advancements aim to optimize manufacturing processes, improve quality control, and reduce reliance on time-consuming and labor-intensive traditional measurement methods. The resulting improvements in predictive capabilities have significant implications for various industries, from precision engineering to construction.

Papers

September 16, 2024

Efficient Milling Quality Prediction with Explainable Machine Learning
Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch, Mohamed Elmansori
Random Forest Explainable Machine Learning Milling Process Surface Roughness Roughness Field

June 20, 2024

Toward data-driven research: preliminary study to predict surface roughness in material extrusion using previously published data with Machine Learning
Fátima García-Martínez, Diego Carou, Francisco de Arriba-Pérez, Silvia García-Méndez
Machine Learning Raw Data Data Driven Additive Manufacturing Profile Extrusion Surface Roughness

December 16, 2023

Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data
Antonios Alexos, Junze Liu, Akash Tiwari, Kshitij Bhardwaj, Sean Hayes, Pierre Baldi, Satish Bukkapatnam, Suhas Bhandarkar
Human Prediction New Machine Limited Data Surface Roughness Inertial Confinement Fusion

October 31, 2023

A Synthetic Modal Generation of Additive Manufacturing Roughness Surfaces from Images
T. B. Keesom, P. P. Popov, P. Dhyani, G. B. Jacobs
Surface Roughness Roughness Field Turbulence Simulation Modal Synthesis

October 23, 2023

Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE
Sara Sacchi, Bappaditya Dey, Iacopo Mochi, Sandip Halder, Philippe Leray
Signal to Noise Ratio Image Denoiser SEM Image Extreme Ultraviolet Lithography Surface Roughness Roughness Field

October 9, 2023

Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno, Chandula T. Wickramarachchi, Elizabeth J. Cross, Timothy J. Rogers, Keith Worden, Nikolaos Dervilis, Aidan J. Hughes
Industrial Machine Information Sharing Bayesian Regression Surface Roughness Machining Feature Hierarchical Bayesian Model Surface Prediction

July 6, 2023

Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models
Alex Milne, Xianghua Xie
Machine Learning Model Object Surface High Power Laser Surface Roughness Parallel Tempering Steel Manufacturing Texture Editing

April 24, 2023

Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens
Akshansh Mishra, Vijaykumar S. Jatti
Human Prediction Quantum Machine Learning Quantum Neural Network Variational Quantum Classifier Deep Forest Surface Roughness

September 18, 2022

VisTaNet: Attention Guided Deep Fusion for Surface Roughness Classification
Prasanna Kumar Routray, Aditya Sanjiv Kanade, Jay Bhanushali, Manivannan Muniyandi
Human Attention Sensory Input Tactile Modality W Net Tactile Data Deep Fusion Surface Roughness

August 4, 2022

AG2U -- Autonomous Grading Under Uncertainties
Yakov Miron, Yuval Goldfracht, Chana Ross, Dotan Di Castro, Itzik Klein
Robust Policy Observable Markov Decision Process High Uncertainty Value Automatic Grading Localization Uncertainty Surface Roughness

April 12, 2022

Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform
Melih C. Yesilli, Jisheng Chen, Firas A. Khasawneh, Yang Guo
Discrete Cosine Transform Automatic Thresholding Discrete Wavelet Transform Surface Roughness Surface Generation Surface Recognition

Surface Roughness

Papers

Efficient Milling Quality Prediction with Explainable Machine Learning

Toward data-driven research: preliminary study to predict surface roughness in material extrusion using previously published data with Machine Learning

Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data

A Synthetic Modal Generation of Additive Manufacturing Roughness Surfaces from Images

Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE

Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models

Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens

VisTaNet: Attention Guided Deep Fusion for Surface Roughness Classification

AG2U -- Autonomous Grading Under Uncertainties

Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform