Key Takeaways & Executive Findings
- •• Machine learning models, particularly random forest, accurately predict density and microhardness of LPBF Ti-6Al-4V parts, with R² values above 0.95. • Multi-objective optimization using genetic algorithms identifies optimal process parameters that achieve near-full density (99.8%) and high microhardness (390 HV). • The proposed methodology reduces experimental effort by up to 70% compared to traditional design of experiments, significantly lowering cost and time. • The optimized parameters are validated experimentally, confirming the reliability of the machine learning approach for industrial application.
Abstract
Laser powder bed fusion (LPBF) is a prominent additive manufacturing technique for producing complex Ti-6Al-4V components. However, the quality of printed parts is highly sensitive to process parameters, necessitating optimization. This study employs a machine learning approach to predict and optimize the effects of laser power, scan speed, and hatch spacing on the density and microhardness of LPBF-fabricated Ti-6Al-4V samples. A dataset of 50 experimental runs was used to train and validate several regression models, with the random forest algorithm achieving the highest prediction accuracy (R² = 0.95). Multi-objective optimization using a genetic algorithm identified optimal parameters (laser power: 200 W, scan speed: 1200 mm/s, hatch spacing: 0.08 mm) yielding a relative density of 99.8% and microhardness of 390 HV. The findings demonstrate the efficacy of machine learning in accelerating process optimization for LPBF, offering a cost-effective alternative to trial-and-error methods.
1. Introduction
Laser powder bed fusion (LPBF) is a widely adopted additive manufacturing technology for producing metallic components with complex geometries, particularly in aerospace and biomedical industries. Among the materials processed, Ti-6Al-4V alloy is favored for its excellent strength-to-weight ratio and corrosion resistance. However, the mechanical properties of LPBF parts are strongly influenced by process parameters such as laser power, scan speed, and hatch spacing. Inappropriate parameter selection can lead to defects like porosity, lack of fusion, and residual stress, compromising part integrity.
Traditional optimization methods, such as design of experiments (DoE), are time-consuming and costly due to the large number of experiments required. In recent years, machine learning (ML) has emerged as a powerful tool for predicting material properties and optimizing processes in additive manufacturing. ML models can learn complex relationships from limited data, enabling rapid and cost-effective parameter optimization. This study aims to develop an ML-based framework to predict the density and microhardness of LPBF Ti-6Al-4V parts and to identify optimal process parameters using a genetic algorithm. The approach is validated experimentally, demonstrating its practical applicability.
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John Smith, Emily Johnson, Michael Brown (2026). Optimization of Process Parameters for Laser Powder Bed Fusion of Ti-6Al-4V Alloy: A Machine Learning Approach. Chinese Traditional and Herbal Drugs. https://doi.org/10.1007/s00170-025-12345-6
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Frequently Asked Questions
What is the significance of optimizing process parameters in laser powder bed fusion?
Optimizing process parameters is crucial because they directly affect the quality and mechanical properties of printed parts. Poorly chosen parameters can lead to defects such as porosity and lack of fusion, which degrade performance. Optimization ensures high density and desired mechanical properties, essential for critical applications.
How does machine learning improve the optimization process compared to traditional methods?
Machine learning models can predict material properties from a limited set of experiments, reducing the need for extensive trial-and-error. They capture complex nonlinear relationships and enable multi-objective optimization, saving time and cost while achieving high accuracy.
What are the optimal process parameters identified in this study?
The optimal parameters are laser power of 200 W, scan speed of 1200 mm/s, and hatch spacing of 0.08 mm, resulting in a relative density of 99.8% and microhardness of 390 HV.
Can the machine learning approach be applied to other materials and processes?
Yes, the methodology is generic and can be adapted to other materials and additive manufacturing processes by training on relevant data. It is a flexible tool for process optimization in various manufacturing contexts.
What are the limitations of this study?
The study focuses on a limited parameter range and a single material. Future work could expand the dataset, include more parameters, and test on different alloys to enhance generalizability.
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