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Optimization of process parameters in metal injection molding for turbine blade using response surface methodology.

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Title: Optimization of process parameters in metal injection molding for turbine blade using response surface methodology.
Authors: Rosli, Mohd Uzair1 (AUTHOR) uzair@unimap.edu.my, Khor, Chu Yee1 (AUTHOR), Zailani, Zainal Abidin1 (AUTHOR)
Source: International Journal of Advanced Manufacturing Technology. Apr2025, Vol. 137 Issue 11, p5899-5912. 14p.
Subjects: Injection molding of metals, Turbine blades, Analysis of variance, Response surfaces (Statistics), Surface analysis, Product quality
Abstract: Manufacturing turbine blades using metal injection molding (MIM) is a complex process that requires precise control over parameters to achieve high dimensional accuracy. Inadequate management of shrinkage, frozen volume, and volume filled leads to dimensional deviations, resulting in defects or reduced performance of turbine blades in operation. Optimizing these response factors ensures reliable production and high-quality turbine blades. This study investigates the influence of process parameters in metal injection molding by evaluating their significance and interaction. A three-level central composite design (CCD) approach-based response surface methodology analysis was applied to statistically specify the effect of important numerical and categorical process variables: mold temperature, melt temperature, injection time, flow rate on the critical response process output variables concerning product quality, namely shrinkage, frozen volume, and volume filled. By using a face-centered design, a total of 30 simulation data was fitted. Analysis of variance (ANOVA) was then performed to assess the significance of factors and their interactions at a 95% confidence level (p < 0.05). Subsequently, empirical models were developed and rigorously validated against the simulation results. The optimum process parameters of the metal part were characterized as follows: mold temperature of 15 °C, 138 °C of melt temperature, 2.5 s of injection time, and 94 cm3/s flow rate. The results are expected to advance the metal injection molding industry by providing valuable references and enhancing the understanding of the optimization process. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Manufacturing turbine blades using metal injection molding (MIM) is a complex process that requires precise control over parameters to achieve high dimensional accuracy. Inadequate management of shrinkage, frozen volume, and volume filled leads to dimensional deviations, resulting in defects or reduced performance of turbine blades in operation. Optimizing these response factors ensures reliable production and high-quality turbine blades. This study investigates the influence of process parameters in metal injection molding by evaluating their significance and interaction. A three-level central composite design (CCD) approach-based response surface methodology analysis was applied to statistically specify the effect of important numerical and categorical process variables: mold temperature, melt temperature, injection time, flow rate on the critical response process output variables concerning product quality, namely shrinkage, frozen volume, and volume filled. By using a face-centered design, a total of 30 simulation data was fitted. Analysis of variance (ANOVA) was then performed to assess the significance of factors and their interactions at a 95% confidence level (p &lt; 0.05). Subsequently, empirical models were developed and rigorously validated against the simulation results. The optimum process parameters of the metal part were characterized as follows: mold temperature of 15 &#176;C, 138 &#176;C of melt temperature, 2.5 s of injection time, and 94 cm3/s flow rate. The results are expected to advance the metal injection molding industry by providing valuable references and enhancing the understanding of the optimization process. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00170-025-15496-w
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        Text: English
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        PageCount: 14
        StartPage: 5899
    Subjects:
      – SubjectFull: Injection molding of metals
        Type: general
      – SubjectFull: Turbine blades
        Type: general
      – SubjectFull: Analysis of variance
        Type: general
      – SubjectFull: Response surfaces (Statistics)
        Type: general
      – SubjectFull: Surface analysis
        Type: general
      – SubjectFull: Product quality
        Type: general
    Titles:
      – TitleFull: Optimization of process parameters in metal injection molding for turbine blade using response surface methodology.
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            NameFull: Rosli, Mohd Uzair
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            NameFull: Khor, Chu Yee
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            NameFull: Zailani, Zainal Abidin
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            – D: 28
              M: 04
              Text: Apr2025
              Type: published
              Y: 2025
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