International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 12 | Dec 2025
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
An Integrated Taguchi–Regression Approach for Shrinkage Porosity Reduction in High Pressure Die Cast Aluminum ADC10 Components Vigneshraja K S1, Velmurugan C2, Jai Harish D3, Vikkram L K4, , Sandeep N V5 1,3,4,5 UG Scholar, Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, India 2Professor, Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, India
-------------------------------------------------------------------------***--------------------------------------------------------------------------
Abstract - HPDC is widely adopted in the automotive industry to produce geometrically complex aluminum alloy parts
with a high level of productivity and dimensional accuracy. However, shrinkage porosity remains one of the most critical quality problems in aluminum die castings. It generally becomes evident during post-machining operations and has been one of the primary causes of increased rejection rates, inflated production cost, and reduced process efficiency. In this investigation, the authors undertook a systematic optimization of the HPDC process parameters to minimize shrinkage porosity in an aluminum ADC10 starter motor casing. Based on industrial practice and literature surveys, five major controlling parameters, namely holding furnace temperature, die temperature, first-stage plunger velocity, second-stage plunger velocity, and multiplied pressure, have been selected for optimization. The Taguchi technique based on an L27 orthogonal array was harnessed for efficient design of experiments. Performance characteristics were measured following the "smaller-the-better" signal-to-noise ratio criterion. To overcome the limitations of discrete optimization, a multivariable linear regression model has been developed that builds a continuous predictive relationship between process variables and porosity, yielding a strong coefficient of determination of R² = 0.964. Subsequently, the regression model was used as a constituent objective function for constrained analytical optimization. Key Words: High pressures die casting, Shrinkage porosity, Taguchi method, MVLR, Process optimization
1.INTRODUCTION This The high-pressure die-casting process is one of the most widely adopted manufacturing technologies to produce complex aluminum alloy components with great dimensional accuracy, excellent surface finish, and very good repeatability. Because HPDC provides a great opportunity for high production rates and near-net-shape manufacturing, this casting technology has been extensively utilized in the automotive, electrical, and consumer goods industries for the manufacture of various components like engine, transmission casings, and starter motor housings. Aluminum alloys, particularly Al-SiCu-based alloys, such as ADC10 alloy, have been preferred in HPDC applications due to their good cast ability, adequate mechanical strength, and favorable thermal properties. Though there is an advantage in the HPDC process, it is prone to producing casting defects due to its sensitivity to fluctuations in process parameters. Among these, shrinkage porosity is one of the most important and persistent defects affecting quality and reliability in aluminum die cast components. Shrinkage porosity generally occurs during solidification due to insufficient feeding in regions undergoing volumetric contraction. These defects may not be apparent in as-cast conditions but often evidence after machining results in leakage, reduced mechanical strength, and functional failure of components. Thus, shrinkage porosity contributes significantly to rejection rates, rework, and increased production costs in industrial HPDC operations. In HPDC, shrinkage porosity forms because of a complex interaction among multiple processing parameters, such as melt temperature, die temperature, injection velocities, and applied pressure during solidification. Conventionally, in industrial practice, these parameters are optimized based on trial-and-error methods and operator experience. These methods may yield acceptable process windows after several attempts; however, they are very time-consuming and expensive, and often they cannot produce robust solutions given the multivariate and nonlinear nature of the process in HPDC. Furthermore, trialbased optimization provides little insight into the relative importance of each of these parameters and their interactions on defect formation. Statistical and analytical techniques of optimization, therefore, have received great attention in recent times. The Taguchi method has traditionally been one of the most popular techniques used in the optimization of the casting process because of its effective usage of orthogonal arrays, which greatly reduces the number of experiments needed with minimal loss in the balance of evaluation of process parameters. The signal-to-noise ratio analysis utilized in the Taguchi approach helps in the identification of the parameter-setting combinations that improve not only the quality but also the robustness against variability in the process. The main limitation of the Taguchi method, however, is that being discrete, optimal solutions are constrained at specified levels of parameters.
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 1110