Using the Taguchi method for optimization of the powder metallurgy forming process for Industry 3.5

Wang, Hung Kai; Wang, Zih Huei; Wang, Ming Chi

doi:10.1016/j.cie.2020.106635

Cited by 7 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A higher S/N ratio signifies reduced noise, indicating minimal process variation and exceptional quality stability. In the Taguchi method, outstanding quality reflects the robustness of the product or process and serves as an indicator of the quality characteristics [42]. Table 2 shows the studies included on the single response optimization using Taguchi method.…”

Section: Taguchi Method-single-response Optimizationmentioning

confidence: 99%

Optimization methods in powder metallurgy for enhancing the mechanical properties: a systematic literature review

Lingam,

Ananthanarayanan,

Jeevanantham

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

Powder metallurgy offers several advantages over traditional manufacturing methods, such as improved material utilization and waste reduction. The powder metallurgy process involves four primary steps, metal powder preparation, mixing, compaction, and sintering, followed by secondary operations such as forging, rolling, and extrusion. However, existing studies have predominantly focused on refining the primary processes, neglecting secondary processes that could further enhance mechanical properties. A systematic literature review following the Preferred Reporting Items for Systematic Review and Meta-Analysis method identified twenty-three relevant articles over a ten-year period, highlighting three main optimization methods, that are, the Taguchi method, Taguchi-based Grey Relational Analysis, and Response Surface Methodology. These methods effectively reduce experimental trials and optimize key parameters like compaction pressure and weight percentage, which significantly influence mechanical properties. The review emphasizes the key powder metallurgy process parameters that have a significant impact on mechanical properties, becoming the primary targets for optimization. It offers a focused examination of which process parameters require thorough investigation for optimization and the suitable optimization method to achieve optimal outcomes, whether for single or multiple responses, thereby enhancing the manufacturing process. However, inconsistencies in parameter significance across studies indicate the material-dependent nature of powder metallurgy materials. The review also identifies a gap in the literature regarding secondary process optimization and comparative analyses of optimization methods. Future research should explore optimizing secondary processes in powder metallurgy, conduct comparative studies of different optimization methods, and develop novel approaches for optimization to further enhance mechanical properties and process efficiency.

show abstract

Section: Taguchi Method-single-response Optimizationmentioning

confidence: 99%

Optimization methods in powder metallurgy for enhancing the mechanical properties: a systematic literature review

Lingam,

Ananthanarayanan,

Jeevanantham

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…This data expansion is possible because the effects of individual variables can be separated in an experiment designed by an orthogonal array table. Because of this characteristic, experiments using an orthogonal sequence table are widely used in the industry [41][42][43]. Figure 1 shows the flow of analysis using extended data using an orthogonal array table.…”

Section: Data Expansion By Means Of An Orthogonal Array Experiments Dementioning

confidence: 99%

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

Wie

Lee

et al. 2020

Materials

View full text Add to dashboard Cite

The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process consisted of L18(3661), which means that 36 × 61 data can be obtained in 18 experiments using an orthogonal array design. After the experiment, the data were expanded to 486 instances and trained by several machine-learning techniques such as linear regression, random forest, and support vector regression (SVR). We evaluated the predictive performance of machine-learning models by comparing predicted and actual values. As a result, the SVR showed the best performance for predicting measured values. This model also worked well for predictions of untested cases.

show abstract

Taguchi Optimization of Mechanical Properties in Al-Kaoline Metal Matrix Composite Fabricated Through Powder Metallurgy Technique

Venkatesh¹,

Deoghare²

2022

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Using the Taguchi method for optimization of the powder metallurgy forming process for Industry 3.5

Cited by 7 publications

References 20 publications

Optimization methods in powder metallurgy for enhancing the mechanical properties: a systematic literature review

Optimization methods in powder metallurgy for enhancing the mechanical properties: a systematic literature review

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

Taguchi Optimization of Mechanical Properties in Al-Kaoline Metal Matrix Composite Fabricated Through Powder Metallurgy Technique

Contact Info

Product

Resources

About