Machine learning enables roughness-driven inverse design of milling processes

2026-06-14 · Source: Machine Learning · Field: Manufacturing & Industrial — Smart Manufacturing & Industry 4.0, Automation & Robotics, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A machine learning (ML)-based framework has been developed for the inverse design of surface milling processes, specifically targeting surface roughness as the primary design objective. This framework addresses challenges like limited datasets and robustness issues in inverse design by utilizing a high-fidelity synthetic dataset generated from computational simulations. It employs forward training of two distinct ML models: a deep neural network (DNN) and a random forest (RF) ensemble. These trained models are then integrated into a Bayesian optimization (BO) procedure. This integration helps overcome the multiplicity problem inherent in the many-to-one mapping of milling parameters to roughness. The methodology successfully identifies optimal milling process configurations, encompassing both process and tool parameters, from the complete solution space. The proposed models demonstrate robustness and reliability, achieving average relative errors below 5% when compared to reference results.

Key takeaway

For Manufacturing Engineers or Process Designers aiming to optimize surface roughness in milling operations, this ML-based inverse design framework offers a robust solution. You can utilize simulation-generated synthetic data to train predictive DNN and RF models, then integrate them with Bayesian optimization to efficiently identify optimal process and tool parameters. This approach allows you to achieve specific roughness targets with high confidence, potentially reducing costly physical trials and accelerating process development.

Key insights

An ML-based framework integrates DNN/RF models with Bayesian optimization for inverse design of milling processes, optimizing surface roughness with <5% error.

Principles

• Data-driven models predict manufacturing outcomes.
• Synthetic data can train high-fidelity ML models.
• Bayesian optimization handles inverse design multiplicity.

Method

Generate high-fidelity synthetic data, forward train DNN and RF models, then integrate into Bayesian optimization to identify optimal milling process and tool parameters for desired surface roughness.

In practice

• Optimize milling parameters for target surface roughness.
• Design new milling processes using ML predictions.
• Reduce physical trials via simulation-trained models.

Topics

• Machine Learning
• Inverse Design
• Milling Process
• Surface Roughness
• Bayesian Optimization
• Synthetic Datasets

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.