Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach

Optimizing High Pressure Die Casting Parameters: A Simulation-Driven Path to Six Sigma Quality

This technical summary is based on the academic paper "Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach" by Suraj R. Marathe and Dr. Carmo E. Quadros, published in the International Journal of Engineering and Management Research (2021).

Keywords

• Primary Keyword: High Pressure Die Casting Parameters
• Secondary Keywords: Six Sigma Quality, Numerical Simulation, ProCAST, Casting Defects, Solidification Time, Process Optimisation

Executive Summary

• The Challenge: High rejection rates (11-13%) in die casting processes are caused by defects like blowholes, porosities, and hot spots stemming from incorrect process parameter settings.
• The Method: A numerical simulation approach using ProCAST software was employed to systematically analyze and optimize four critical High Pressure Die Casting (HPDC) parameters: molten metal temperature, injection pressure, plunger velocity, and holding time.
• The Key Breakthrough: The study identified a precise set of optimal parameters that minimize solidification time and demonstrated that molten metal temperature has the most significant influence on final casting quality compared to other variables.
• The Bottom Line: By leveraging numerical simulation to define optimal process windows, manufacturers can drastically reduce defects, improve product quality to Six Sigma levels, and cut rejection-related costs, as proven by a reduction in motor rejections from 4.35% to 0.89% in an industrial case study.

The Challenge: Why This Research Matters for HPDC Professionals

In the competitive world of manufacturing, die casting processes frequently suffer from poor quality and low productivity. The industry grapples with rejection levels as high as 11-13%, a direct result of defects like blowholes, insufficient injection pressure, improper filling, porosities, and hot spots. These issues arise from the complex interplay of process parameters, and a lack of understanding of their specific effects leads to inconsistent outcomes. For engineers and managers, this means wasted material, lost production time, and increased costs. The research presented in this paper tackles this core problem head-on by seeking a systematic, data-driven method to control High Pressure Die Casting Parameters and achieve zero-defect parts.

The Approach: Unpacking the Methodology

The research team adopted a robust numerical simulation strategy to analyze and optimize the HPDC process for a specific rotor component. This approach allowed for the precise evaluation of process parameters without the cost and time of extensive physical trials.

Method 1: Numerical Simulation Framework
The study utilized ProCAST simulation software, which employs finite element analysis (FEA) to model the die casting process. A 3D model of a rotor component (100 mm outer radius, 135 mm height) was created and meshed for analysis.

Method 2: Four-Stage Parameter Optimisation
A systematic, four-stage experimentation plan was designed to isolate the impact of each key parameter. The four critical variables analyzed were:
1. Molten Metal Temperature: Varied from 680°C to 752°C.
2. Injection Pressure: Varied from 300 bar to 348 bar.
3. Plunger Velocity: Varied from 116.6 m/s to 121.4 m/s.
4. Holding Time: Varied from 52 s to 76 s.

In each of the four stages, one parameter was the primary focus while the others were varied across 25 levels, resulting in a total of 7800 simulated experiments to identify the optimal settings for minimizing solidification time.

The Breakthrough: Key Findings & Data

The comprehensive simulation yielded clear, actionable data for optimizing the HPDC process. The central goal was to find the combination of parameters that resulted in the minimum solidification time, as this is directly linked to a reduction in internal defects.

Finding 1: Temperature is the Dominant Factor in Casting Quality

The analysis consistently showed that molten metal temperature had a more profound influence on casting quality and solidification time than pressure, velocity, or holding time. As shown in Table 3, the temperature analysis (Stage 1) identified an optimal temperature of 728°C. At this temperature, with pressure at 330 bar, velocity at 119.6 m/s, and holding time at 67 s, the minimum solidification time for the rotor component was achieved at 9.68 seconds, with the cross-sections solidifying in 9.45 s (CS BB) and 8.35 s (CS AA).

Finding 2: A Precise Window for Optimal Process Control

The multi-stage analysis successfully narrowed down the ideal operating window for all four parameters. The final two-sided optimization (Table 11) provides a clear recipe for achieving Six Sigma quality:
- Temperature (T): 728°C ≤ T ≤ 731°C
- Pressure (P): 330 bar ≤ P ≤ 332 bar
- Velocity (V): 119.6 m/s ≤ V ≤ 120 m/s
- Holding Time (HT): 65 s ≤ HT ≤ 69 s

Following these parameters, confirmation experiments in an industrial setting (CGL) demonstrated a dramatic reduction in motor rejections due to rotor defects from 4.35% to just 0.89%.

Practical Implications for R&D and Operations

• For Process Engineers: This study suggests that focusing initial optimization efforts on molten metal temperature (around 730°C) can yield the most significant improvements. The defined parameter ranges in Table 11 provide an excellent starting point for process validation and achieving consistent, defect-free production.
• For Quality Control Teams: The data in the paper, particularly the simulation results for shrinkage porosity and misrun sensitivity (Figure 16), illustrates how optimized parameters lead to castings with no predicted defects. This can inform the development of more targeted, proactive quality inspection criteria rather than reactive defect detection.
• For Design Engineers: While not the primary focus, the findings on solidification patterns (e.g., Figure 9) highlight how heat is extracted from the component. This reinforces the importance of designing parts that promote uniform cooling to avoid hot spots, a key consideration in the early design phase.

Paper Details

Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach

1. Overview:

• Title: Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach
• Author: Suraj R. Marathe, Dr. Carmo E. Quadros
• Year of publication: 2021
• Journal/academic society of publication: International Journal of Engineering and Management Research, Volume-11, Issue-1 (February 2021)
• Keywords: High Pressure Die Casting (HPDC), Parameters, ProCAST, Simulation, Six Sigma Quality

2. Abstract:

A numerical simulation approach is proposed to predict the optimal parameter setting during high pressure die casting. The contribution from the optimal parameters, the temperature, showed more influence on the casting quality than the other parameters. This study's outcome was beneficial for finding the solution for casting defects that occurs due to incorrect setting of process parameters in die casting. Thus, a combination of numerical optimisation techniques and casting simulation serves as a tool to improve the casting product quality in die casting industries. This paper aims to analyse and optimise critical parameters like injection pressure, molten metal temperature, holding time, and plunger velocity, contributing to the defects. In this research paper, an effort has been made to give optimal pressure, temperature, holding time, and plunger velocity parameters using ProCAST simulation software that uses finite element analysis technology. Numerical analysis for optimising the parameters by varying the temperature of molten metal, injection pressure, holding time, and plunger velocity, concerning solidification time at hot spots, is an essential parameter for studying the defect analysis in the simulated model.

3. Introduction:

Die casting processes suffer from poor quality and productivity due to the involvement of various process parameters. The rejection level in the die casting process has been found to be between 11 to 13 percent, primarily due to defects such as blowholes, insufficient injection pressure, improper filling time, porosities, and hot spots. To control these defects and achieve zero-defect parts, it is essential to understand the effect of process parameters on casting quality. This investigation focuses on a die-cast rotor component and utilizes a numerical simulation approach to analyze and optimize critical process parameters.

4. Summary of the study:

Background of the research topic:

High Pressure Die Casting (HPDC) is a widely used manufacturing process affected by numerous technological and controllable parameters. Incorrect settings for parameters like plunger velocity, pressure, filling time, and molten metal temperature lead to significant quality issues and high rejection rates.

Status of previous research:

Previous studies by Mohanty and Jena (2014) highlighted the need to control process parameters for zero-defect parts. Other researchers have noted the influence of individual parameters; for example, Kumar, s. et al. (2012) found that molten metal temperature has an essential effect on defects, while Zhang, M. et al. (2008) discussed the requirements for clamp pressure. This study builds on that foundation by proposing a comprehensive, multi-stage numerical optimization approach to analyze the combined effect of these parameters.

Purpose of the study:

The primary aim of this paper is to analyze and optimize critical HPDC parameters—injection pressure, molten metal temperature, holding time, and plunger velocity—to find the optimal settings that minimize casting defects. The study uses ProCAST simulation software to predict these optimal parameters and provide a tool for improving product quality in die casting industries toward a Six Sigma level.

Core study:

The core of the study involved a four-stage numerical simulation to optimize the HPDC parameters for a rotor component. A total of 7800 simulation experiments were conducted using ProCAST software. In each stage, one parameter (temperature, pressure, velocity, or holding time) was analyzed in detail while varying the others to find the combination that resulted in the minimum solidification time, which is an indicator of reduced defects. The final results were compiled into an optimal process window, and the effectiveness of this approach was confirmed through industrial data showing a significant reduction in product rejection rates.

5. Research Methodology

Research Design:

The study employed a quantitative, simulation-based research design. A numerical optimization algorithm was structured in four sequential stages to analyze the influence of four key process parameters on the solidification time of a die-cast rotor. The goal was to minimize the solidification time to reduce defects.

Data Collection and Analysis Methods:

Data was generated through numerical simulations using ProCAST software, which is based on the finite element method (FEM). The geometrical model of the rotor was created in Unigraphics NX4.0 and imported into ProCAST. The process parameters were varied systematically across 25 levels for each of the four stages. The output data, primarily solidification time at the component and specific cross-sections (AA and BB), was analyzed to identify the optimal parameter settings at each stage and for the overall process.

Research Topics and Scope:

The research focused on the High Pressure Die Casting (HPDC) of an aluminum alloy rotor component. The scope was limited to the analysis and optimization of four specific process parameters: molten metal temperature, injection pressure, plunger velocity, and holding time. The study's outcome is an optimized set of parameters intended to achieve Six Sigma quality by minimizing defects related to solidification.

6. Key Results:

Key Results:

• The numerical simulation approach successfully identified an optimal process window for the HPDC parameters.
• Molten metal temperature was found to have the most significant influence on casting quality and solidification time compared to pressure, velocity, and holding time.
• The optimal parameters for Stage 1 (Temperature Analysis) were found at experiment 392: Temperature 728°C, Pressure 330 bar, Velocity 119.6 m/s, and Holding Time 67 s, yielding a minimum solidification time of 9.68 s for the rotor.
• The final two-sided optimum parameters were determined to be: Temperature 728–731°C, Pressure 330–332 bar, Velocity 119.6–120 m/s, and Holding Time 65–69 s.
• Application of these optimal parameters in an industrial setting (CGL) led to a reduction in motor rejections (due to rotor contribution) from 4.35% to 0.89%.
• Simulations of the optimized process in ProCAST showed no presence of defects like shrinkage porosity or misruns.

Figure Name List:

• Figure 1: Die casting rotor component and cs at AA and BB for numerical analysis
• Figure 2: Die casting heat process cycle represented on a timeline in seconds (Industrial case-CGL)
• Figure 3: Experimentation setup: vertical die casting machine of the capacity of 100 Tons (Industrial case-CGL)
• Figure 4: The proposed experimentation set up for solving flow for optimisation of parameters
• Figure 5: Block diagram showing four stages of numerical simulation for the optimisation of solidification time
• Figure 6: Flow diagram showing the final stage of a numerical simulation approach
• Figure 7: Meshed and position of cross-sections of the rotor component
• Figure 8: Graphical representation of temperature analysis of the rotor and at cross-sections AA and BB
• Figure 9: (a, b, and c) Simulation output for solidification time at rotor component, Cross-Section AA and BB.
• Figure 10: Graphical representation of pressure analysis at various sections
• Figure 11: (a, b, and c). Simulation output for solidification time at rotor component, Cross-Section AA, and BB.
• Figure 12: Graphical representation of plunger velocity analysis at various sections
• Figure 13: (a, b, and c) Simulation output for solidification time at rotor component, Cross-Section AA and BB.
• Figure 14: Graphical representation of holding time at various sections
• Figure 15: (a, b, and c) Simulation output for solidification time at rotor component, Cross-Section AA and BB.
• Figure 16: (a, b, c,) ProCAST simulated results for filling time, total shrinkage porosity and misrun sensitivity

7. Conclusion:

The study successfully demonstrates that a numerical simulation approach is an effective tool for optimizing HPDC process parameters to minimize solidification time and, consequently, eliminate internal defects like blowholes and porosity. The algorithm, which involved 7800 simulation experiments, provided a decision tool for setting optimum parameters to achieve Six Sigma quality. The results clearly indicate that temperature is the most influential parameter. Confirmation experiments using the mean optimal settings (730°C temperature, 331 bar pressure, 119.8 m/s velocity, 67 s holding time) validated the simulation, reducing industrial rejection rates significantly. The combination of numerical optimization and casting simulation serves as a powerful tool for improving casting product quality in the die casting industry.

8. References:

• [1] Domkin, K., Hattel, J., & Thorborg, J. (2009). Modelling of high temperature and diffusion controlled die soldering in high aluminium pressure die casting. Journal of Material Processing Technology, 209(8), 4051-4061.
• [2] Fiorese, E., Richiedei, D., & Bonollo, F. (2016). Improving the quality of die castings through optimal plunger motion planning: analytical computation and experimental validation. International Journal of Advanced Manufacturing Technology, 88, 1475–1484.
• [3] Fu, J. & Wang, K. (2014) Modelling and simulation of the die casting process for A356 semi-solid alloy. Procedia Engineering, 81, 1565–1570.
• [4] Jorstad, J. & Apelian, D. (2009). Pressure assisted processes for high integrity aluminium castings - part 1. International Journal of Metal Casting, 250-254.
• [5] Kumar, S., Gupta, A., & Chandna, P. (2012). Optimisation of process parameters of pressure die casting using taguchi methodology. World Academy of Science, Engineering and Technology, 6, 590-594.
• [6] Lattanzi, L., Fabrizi, A., Fortini, A., Merlin, M., & Timelli, G. (2017). Effects of microstructure and casting defects on the fatigue behaviour of the high-pressure die-cast AlSi9Cu3 (Fe) alloy. Procedia Structural Integrity, 7, 505-512.
• [7] Mohanty, C. & Jena, B. (2014). Optimisation of aluminium die casting process using artificial neural network. International Journal of Emerging Technology and Advanced Engineering, 4(7), 146-149.
• [8] Syrcos, G. (2002). Die casting process optimisation using taguchi methods. Journal of Material Processing Technology, 135, 68-74.
• [9] Wang, L., Turnley, P., & Savage, G. (2011) Gas content in high pressure die castings. Journal of Materials Processing Technology, 211, 1510–1515.
• [10] Zhang, M., Xing, S., Xiao, L., Bao, P., Liu, W., & Xin, Q. (2008). Design of process parameters for direct squeeze casting. Journal of University of Science and Technology, 15(3), 339-343.
• [11] Zhang, X., Xiong, S., & Xu, Q. (2006). Numerical methods to improve the computational efficiency of solidification simulation for the investment casting process. Journal of Materials Processing Technology, 173, 70-74.

Expert Q&A: Your Top Questions Answered

Q1: Why was a four-stage optimization approach used instead of a single, simultaneous multi-variable optimization?

A1: The paper employed a four-stage approach, as depicted in Figure 4 and Figure 5, to systematically isolate and understand the influence of each individual parameter (temperature, pressure, velocity, holding time) on the solidification time. By focusing on one primary variable per stage while varying the others, the researchers could clearly identify the most influential parameter—which turned out to be temperature—and then fine-tune the others accordingly. This methodical process ensures a robust and understandable optimization path.

Q2: The study mentions "Six Sigma Quality." How does minimizing solidification time relate to achieving this standard?

A2: The paper establishes a direct link between lower solidification time and reduced internal defects. As stated in the "Discussions and Conclusions" section, when solidification time is lower, defects such as blowholes and porosity are eliminated. Six Sigma is a quality management methodology that aims for near-perfect products (3.4 defects per million opportunities). By using simulation to find parameters that minimize solidification time, the process inherently targets the root cause of many defects, making the achievement of Six Sigma quality a tangible goal.

Q3: What specific material was being cast, and how might that affect the results?

A3: The paper focuses on the HPDC process for an aluminum alloy rotor component. While the specific alloy is not named in the abstract or introduction, the principles of optimizing temperature, pressure, and velocity to control solidification are fundamental to die casting. The exact optimal values would change for different alloys (e.g., zinc or magnesium), but the simulation-based methodology presented is broadly applicable for determining the ideal High Pressure Die Casting Parameters for any material.

Q4: How were the initial ranges for the process parameters (e.g., temperature from 680°C to 752°C) determined?

A4: The paper draws on established industry knowledge and previous research for the parameter ranges. For instance, it cites Domkin, K. et al. (2009), who state that pouring molten metal temperature generally varies from 650 to 800 degrees Celsius. The ranges selected for the simulation represent typical operational windows for aluminum HPDC, ensuring the study's results are relevant and practical for industrial application.

Q5: The paper reports a reduction in motor rejections from 4.35% to 0.89%. Was this result from a simulation or a physical trial?

A5: This result was from a physical confirmation experiment. The "Discussions and Conclusions" section explicitly states that "Confirmation experiments with the optimal process parameters" were conducted. The data, sourced from Crompton Greaves Ltd (CGL), shows that when rotors were produced using the optimized settings derived from the simulation, the real-world rejection rate for motors (due to the rotor's contribution) dropped dramatically, validating the accuracy and effectiveness of the numerical simulation approach.

Conclusion: Paving the Way for Higher Quality and Productivity

The challenge of high rejection rates and inconsistent quality in die casting is a significant barrier to productivity. This research demonstrates a clear, data-driven solution. By systematically optimizing High Pressure Die Casting Parameters through numerical simulation, manufacturers can pinpoint the ideal process window to eliminate defects at their source. The key breakthrough is the confirmation that temperature is the most critical lever for control, and that a well-defined combination of all four parameters can lead to transformative results, paving the way for Six Sigma quality levels and drastically reduced waste.

At CASTMAN, we are committed to applying the latest industry research to help our customers achieve higher productivity and quality. If the challenges discussed in this paper align with your operational goals, contact our engineering team to explore how these principles can be implemented in your components.

Copyright Information

This content is a summary and analysis based on the paper "Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach" by "Suraj R. Marathe and Dr. Carmo E. Quadros".

Source: https://doi.org/10.31033/ijemr.11.1.15

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