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Related Experiment Video

Updated: Sep 24, 2025

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Published on: March 13, 2018

Multi-objective optimization of directed energy deposition process by using Taguchi-Grey relational analysis.

Yu-Yang Chang¹, Jun-Ru Qiu¹, Sheng-Jye Hwang¹

¹Department of Mechanical Engineering, National Cheng Kung University, No.1, University Road, Tainan, 701401 Taiwan.

The International Journal, Advanced Manufacturing Technology

|May 9, 2022

Summary

This study optimized the directed energy deposition (DED) process using Taguchi-Grey relational analysis. The optimal settings improved DED product qualities like efficiency, surface roughness, and porosity.

Keywords:

Cladding efficiency Directional energy deposition Porosity Surface roughness Taguchi-Grey relational analysis

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Area of Science:

Materials Science and Engineering
Additive Manufacturing
Process Optimization

Background:

Directed Energy Deposition (DED) is a key additive manufacturing process.
Optimizing DED parameters is crucial for achieving desired product qualities.
Existing methods may not effectively balance multiple quality objectives.

Purpose of the Study:

To perform a multi-objective optimization of the DED process.
To identify optimal control factor settings for improved DED product qualities.
To develop predictive models for DED quality characteristics.

Main Methods:

Taguchi experiments were conducted to investigate the effects of five control factors.
Grey relational analysis (GRA) was employed for multi-objective optimization.
Analysis of Variance (ANOVA) and regression modeling were used to analyze significant factors and predict qualities.

Main Results:

The effects of laser power, overlap ratio, powder feed rate, scanning speed, and laser defocus distance on cladding efficiency, surface roughness, and porosity were analyzed.
An optimal factor setting was determined using GRA, yielding superior deposition results.
ANOVA identified significant factors influencing each quality metric, and predictive models were developed.

Conclusions:

The combined Taguchi-Grey relational analysis effectively optimized the DED process for multiple quality objectives.
The identified optimal settings significantly improved DED product qualities compared to previous settings.
Regression models were validated, demonstrating their accuracy in predicting DED qualities.