Optimizing Casting Surface Finish: A Data-Driven Approach to Al-7%Si Alloy
This technical summary is based on the academic paper "Parametric Optimization of Casting Surface Roughness Produced by Ceramic Shell Investment Casting Process" by Balwinder Singh, Pardeep Kumar, and B.K. Mishra, published in the International Journal of Surface Engineering & Materials Technology (2011). It has been analyzed and summarized for technical experts by CASTMAN.
Keywords
- Primary Keyword: Casting Surface Roughness Optimization
- Secondary Keywords: Ceramic Shell Investment Casting, Taguchi Method, Al-7%Si Alloy, Process Parameter Optimization, Surface Finish
Executive Summary
- The Challenge: Achieving a consistently smooth surface finish in aluminum alloy castings is critical for reducing post-processing costs and improving fatigue strength, but many process variables can negatively impact quality.
- The Method: The Taguchi L9 orthogonal array was used to systematically investigate the effects of four key process parameters—mould preheat temperature, stucco grainfiness number, firing temperature, and pouring temperature—on the surface roughness of Al-7%Si alloy investment castings.
- The Key Breakthrough: The study statistically proved that mould preheat temperature, grainfiness number, and firing temperature are the most significant factors controlling surface roughness, while pouring temperature was found to be insignificant within the tested range.
- The Bottom Line: By optimizing the three significant parameters, a predicted optimal surface roughness of 2.09 µm can be achieved, offering a clear, data-backed pathway to superior casting quality.
The Challenge: Why This Research Matters for HPDC Professionals
In the aerospace, automotive, and tooling industries, the demand for near-net-shape components with excellent surface quality is relentless. For aluminum alloy castings, a high surface roughness not only increases the cost of secondary finishing and polishing operations but can also significantly reduce the fatigue strength of the final part. As noted by H. Jiang et al., a decrease in surface roughness reduces the size of surface pores, which increases resistance to crack initiation and improves fatigue life.
While ceramic shell investment casting can produce a surface smoothness around 2 µm—a significant improvement over the 10-50 µm range typical of sand casting—achieving this consistently is a complex challenge. Numerous factors, from slurry composition to firing protocols, can influence the final outcome. This research was undertaken to move beyond trial-and-error and identify the most influential process parameters, providing a scientific framework for optimizing the surface roughness of Al-7%Si alloy castings.
The Approach: Unpacking the Methodology
To efficiently analyze the process, the researchers employed the Taguchi method, a powerful statistical tool for experimental design. This approach drastically reduced the number of required experiments from a potential 81 (full factorial) to just nine, without sacrificing the ability to identify key influencing parameters.
- Material: The study focused on Al-7%Si alloy, a common material in aerospace and automotive applications valued for its combination of lightness, strength, and low cost.
- Process: The ceramic shell investment casting process was used. Wax patterns were produced and coated with a primary slurry (zircon flour and fused silica) and backup slurries, followed by stuccoing with fused silica sand.
- Key Variables & Levels: Four critical process parameters were selected for optimization, each tested at three distinct levels:
- Mould Preheat Temperature (A): 150°C, 250°C, 350°C
- Grainfiness Number (Stucco Size) (B): 25, 45, 65 (AFS No.)
- Firing Temperature (C): 800°C, 900°C, 1000°C
- Pouring Temperature (D): 650°C, 700°C, 750°C
- Measurement: The resulting surface roughness (Ra value) of the cast specimens was measured using an Optical Profilometer (Wyko NT1100) with a vertical scanning interferometer (VSI) mode. The average of three measurements was used for analysis.
The Breakthrough: Key Findings & Data
The experimental results were analyzed using both raw data and signal-to-noise (S/N) ratios to determine the effect of each parameter. The analysis of variance (ANOVA) provided clear, statistical proof of which factors mattered most.
Finding 1: Three Parameters Dominate Surface Quality; One is Insignificant
The ANOVA results (Table 4) definitively show that Mould Preheat Temperature (A), Grainfiness Number (B), and Firing Temperature (C) are statistically significant factors in determining the final surface roughness. Their F-Ratios were 8.00, 6.95, and 4.00, respectively, with percent contributions (P%) of 24.88%, 21.14%, and 10.68%. In stark contrast, Pouring Temperature (D) was found to be insignificant, with an F-Ratio of just 0.187. This indicates that efforts to control surface finish should be focused on the first three parameters.
Finding 2: The Optimal Combination for a Superior Finish
By analyzing the main effects plots (Figure 5) and S/N ratios, the study identified the ideal level for each parameter to achieve the lowest possible surface roughness (a "lower-the-better" characteristic).
- Optimal Levels (from Table 5):
- Mould Preheat Temperature (A3): 350°C
- Grainfiness Number (B3): 65 (i.e., the finest stucco)
- Firing Temperature (C1): 800°C
- Pouring Temperature (D1): 650°C
The data clearly shows that a higher mould preheat temperature and a finer stucco grain size lead to a smoother surface. Interestingly, a lower firing temperature (800°C) produced better results than higher temperatures within the tested range. As shown in Figure 6, the surface produced at a mould preheat of 350°C is visibly smoother than that produced at 150°C.
Practical Implications for R&D and Operations
- For Process Engineers: This study suggests that to minimize surface roughness, priority should be given to maintaining a high mould preheat temperature (350°C) and using the finest available stucco (65 AFS No.). Firing temperature should be tightly controlled at the lower end of the viable range (800°C). Pouring temperature offers a wider process window without significantly impacting surface finish.
- For Quality Control Teams: The data in Figure 5 and Table 3 provides clear cause-and-effect relationships that can inform troubleshooting. If surface roughness increases, inspection should first focus on mould preheating, stucco quality, and firing furnace calibration. The optical micrographs in Figure 6 demonstrate the tangible difference these parameters make, which could be used to develop visual inspection standards.
- For Design Engineers: While not a direct focus of the study, the ability to achieve a 2.09 µm surface finish directly from casting reduces the need for post-machining. This could allow for the design of more complex and intricate components where secondary finishing operations would be difficult or impossible, expanding design freedom.
Paper Details
Parametric Optimization of Casting Surface Roughness Produced by Ceramic Shell Investment Casting Process
1. Overview:
- Title: Parametric Optimization of Casting Surface Roughness Produced by Ceramic Shell Investment Casting Process
- Author: Balwinder Singh, Pardeep Kumar and B.K. Mishra
- Year of publication: 2011
- Journal/academic society of publication: International Journal of Surface Engineering & Materials Technology, Vol. 1 No. 1 July-Dec. 2011
- Keywords: Ceramic Shell Investment Casting Process; Taguchi Method; Surface Roughness; Optimization
2. Abstract:
In the present paper Taguchi's approach has been applied to the ceramic shell investment casting process of Al-7%Si alloy to determine the most influential control factors, which will provide better and consistent surface roughness to the castings regardless of the noise factors present. In order to evaluate the effect of process parameters such as mould preheat temperature, grainfiness number (stucco size), firing temperature and pouring temperature on surface roughness of ceramic shell investment castings, the Taguchi parameter design and optimization approach is used. The results indicate that the moulds preheat temperature, grainfiness number (stucco size) and firing temperature are the significant parameters in deciding the surface roughness of Al-7%Si alloy castings. Pouring temperature is the insignificant parameters. The predicted optimal value of surface roughness of Al-7%Si alloy castings produced by ceramic shell investment casting process is 2.09µm. The results are confirmed by further experiments.
3. Introduction:
Ceramic shell Investment casting is one of the technologies which have the potential to satisfy the requirements of the near net shape casting of aerospace industry, automobile parts and hand tools. This process gives a perfect surface quality with intricate details and dimensional stability. Aluminium alloy investment castings have been used extensively because of their excellent characteristics, offering an acceptable combination of lightness, strength and low cost. High surface roughness can significantly reduce the fatigue strength of aluminium castings. H. Jiang et al studied fatigue performance in a sand cast Al-7Si-Mg alloy and concluded that decrease in surface roughness would improve fatigue life. A surface smoothness of around 2 µm is highly probable using the ceramic mold casting method compared to 10-50 µm obtained by sand mold casting methods. The objective of the present work was to investigate and optimize the process parameters of ceramic shell investment casting process, which affect the quality (surface roughness) of Al-7%Si alloy castings using the Taguchi parameter design and optimization approach.
4. Summary of the study:
Background of the research topic:
The study addresses the need for high-quality surface finishes in aluminum alloy investment castings for industries like aerospace and automotive. Achieving a smooth, near-net-shape casting reduces post-processing costs and improves mechanical properties such as fatigue life.
Status of previous research:
Previous studies had identified that factors like shell and pouring temperatures affect mechanical properties, and that firing is a critical step. Research by Li Y.M. and Li R.D. noted that low shell and pouring temperatures produced high mechanical properties. Michael J Hendricks reported firing as a most important operation. However, a systematic, multi-variable optimization of the key parameters affecting surface roughness specifically for Al-7%Si alloy was needed.
Purpose of the study:
The objective was to investigate and optimize the process parameters of ceramic shell investment casting to improve the surface roughness of Al-7%Si alloy castings. The study aimed to identify the most influential control factors and determine their optimal levels to produce a better and more consistent surface finish.
Core study:
The core of the study involved using the Taguchi L9 orthogonal array to design an experiment with four process parameters (mould preheat temperature, grainfiness number, firing temperature, pouring temperature) at three levels each. The surface roughness (Ra) of the resulting Al-7%Si castings was measured, and statistical analysis (S/N ratio and ANOVA) was performed to identify significant parameters, determine optimal levels, and predict the best achievable surface roughness.
5. Research Methodology
Research Design:
The study used a Taguchi parameter design approach with an L9 orthogonal array. This design was chosen to efficiently study the non-linear behavior of four parameters, each at three levels, while minimizing the number of experimental runs. The quality characteristic for surface roughness was defined as "lower-the-better."
Data Collection and Analysis Methods:
Wax patterns were created and used to produce ceramic shell moulds. Al-7%Si alloy was poured into these moulds under nine different experimental conditions defined by the L9 array. Each trial was replicated three times. The surface roughness (Ra) of the castings was measured using an Optical Profilometer. The collected data was then transformed into a signal-to-noise (S/N) ratio, and an analysis of variance (ANOVA) was performed to determine the statistical significance and percent contribution of each parameter.
Research Topics and Scope:
The research focused on the parametric optimization of the ceramic shell investment casting process for Al-7%Si alloy. The scope was limited to four process parameters: mould preheat temperature (150-350°C), grainfiness number (25-65 AFS No.), firing temperature (800-1000°C), and pouring temperature (650-750°C). The response variable was surface roughness (Ra). Other parameters like wax composition, slurry properties, and dewaxing temperature were kept constant.
6. Key Results:
Key Results:
- The mould preheat temperature, grainfiness number, and firing temperature were identified as significant parameters affecting the surface roughness of Al-7%Si alloy castings.
- Pouring temperature was found to be an insignificant parameter within the tested range.
- The optimal parameter levels for minimum surface roughness were determined to be: Mould preheat temperature at 350°C (Level 3), Grainfiness number at 65 AFS No. (Level 3), Firing temperature at 800°C (Level 1), and Pouring temperature at 650°C (Level 1).
- The predicted optimal value of surface roughness under these conditions was 2.09 µm.
- Confirmation experiments conducted at the optimal settings yielded an average surface roughness of 2.40 µm, which was within the 95% confidence interval of the predicted optimal range (1.60 < SR < 2.58).
Figure Name List:
- Fig. 1: Ishikawa Cause Effect Diagram of Ceramic Shell Investment Casting Process
- Fig. 2: Wax Expandable Pattern
- Fig. 3: Ceramic Shell Moulds Castings of Al-7%Si Alloy
- Fig. 4: Castings of Al-7%Si Alloy
- Fig. 5: (a, b, c, d). Effects of Process Parameters on Surface Roughness (Raw Data) and S/N Ratio (Main Effects)
- Fig. 6 (a, b): Two Dimensional Plots of Surface Roughness of the Al-7%Si Alloy Castings at Mould Preheat Temperature 150°C and 350°C Respectively
7. Conclusion:
- The Mould preheat temperature, Grainfiness number and Firing temperature significantly affect the surface roughness. The higher mould preheat temperatures resulted in smooth surfaces without any amounts of mould-metal reactions.
- The optimal levels for minimum surface roughness are Mould preheat temperature (A3), Grainfineness number (B3), Firing temperature (C1) and pouring temperature (D1).
- The predicted optimal range for surface roughness is 1.60 < SR < 2.58.
- The 95% confidence interval of the predicted mean for surface roughness is 1.76 < SR < 2.42.
8. References:
- [6] Li, Y.M. and Li, R.D. (2001), "Effect of Process Variables on Micro Porosity and Mechanical Properties in an Investment Cast Aluminium Alloy', Science and Technology of Advanced Materials, 2, pp. 277-280.
- [7] Pearce, J. (2001), Progress in Aluminium Base Castings, Metal Casting Technol (Australia), Vol. 47(4), pp. 29-32.
- [8] Stefanescu, D.M., Delannoy, P., Piwonka, T.S. and Kacar, S. (1991), "An Investigation on the Role of Sand-Metal Contact Angle in the Formation of Casting Penetration Defects", AFS Transactions, Vol. 99, p. 761
- [9] Puertas Arbizu, I. and Luis Perez, C.J. (2003), "Surface Roughness Prediction by Factorial Design of Experiments in Turning Processes, J. Mater. Process. Technol, 143-144, pp. 390-396.
- [10] Jiang, H., Bowen, P. and Knott, J.F. (1999), "Fatigue Performance of a Cast Aluminium Alloy Al-7Si-Mg With Surface Defects", Journal of Materials Science, 34, pp. 719-725
- [11] Clegg, A.J. (1980), The Shaw Process-a Review, Foundry Trade J., pp. 429-438
- [12] David Browne, J. and Denis, O. Mahoney (December 2001), "Interface Heat Transfer in Investment Casting of Aluminium Alloys", Metallurgical and Materials Transactions A, Vol. 32A, p. 3055.
- [13] Michael, J. Hendricks (June 1991), “Processing and Firing Influences on Ceramic Shell Materials", Foundry Trade Journal.
- [14] Beeley, P.R. and Smart, R.F. (1995), Investment Casting, 1st Edition, The University Press, Cambridge, UK, pp. 99–102.
- [15] Alauddin, M., El Baradie, M.A. and Hashmi, M.S.J. (1995), "Computer Aided Analysis of a Surface Roughness Model for End Milling", J. Mater. Process., Technol. 55, pp. 123-127.
- [16] Ross, P.J. (1988), Taguchi Techniques for Quality Engineering, McGraw-Hill Book Company, New York.
- [17] Roy, R.K. (1990), A Primer on Taguchi Method, Van Nostrand Reinhold, New York.
- [18] Singh, Balwinder, Kumar, Pradeep and Mishra, B.K. (2006), "Experimental Investigation of Wax Blends in Investment Casting Process", Indian Foundry Journal, Vol. 52, No. 3/ March, pp. 29-36
- [19] Singh, Balwinder, Kumar, Pradeep and Mishra, B.K. (2006), "Parametric Optimization of Slurry Composition used in Ceramic Shell Investment Casting Process through Taguchi Method", Indian Foundry Journal, Vol. 52, No. 10/Oct, pp. 25-33
- [20] Kumar, S., Kumar, P. and Shan, H.S. (2006), "Parametric Optimization of Surface Roughness Castings Produced by Evaporative Pattern Casting Process, Materials Letters, 60, pp. 3048-3053
- [21] Singh, S., Shan, H.S.and Kumar, P. (2002), "Parametric Optimization of Magnetic-Field-Assisted Abrasive Flow Machining by the Taguchi Method", QualityandReliabilityEngineeringInternational, Vol. 18, pp. 273-283.
Expert Q&A: Your Top Questions Answered
Q1: Why was the Taguchi method chosen for this experiment instead of a more traditional full factorial design?
A1: The Taguchi method was selected for its efficiency. A full factorial experiment with four parameters at three levels each would have required 3^4, or 81, individual experiments. As stated in the paper, by using an L9 orthogonal array, the number of experiments was "drastically reduced to nine," allowing the researchers to capture the influence of the key parameters with significantly fewer resources and trials.
Q2: The paper concludes that pouring temperature is insignificant. Could you elaborate on why that might be the case?
A2: The ANOVA results in Table 4 show that the F-Ratio for pouring temperature is only 0.187, and its S/N ratio analysis in Table 3 shows very little change across the three levels (-9.06, -9.20, -9.28 dB). This indicates that within the tested range of 650°C to 750°C, its effect on the final surface roughness was statistically negligible compared to the powerful influence of mould preheat, grainfiness, and firing temperature.
Q3: How did the final casting's surface roughness compare to the original wax pattern?
A3: The paper notes that "the surface roughness of the casting was greater than the roughness of the wax pattern used for shell making." It explains this is likely due to the particle size of the primary coating slurry, which was not fine enough to "reproduce the wax pattern exterior in excellent details." This highlights the importance of the primary slurry's composition in achieving a true replica of the master pattern.
Q4: What is the practical significance of using the S/N ratio in this analysis?
A4: The Signal-to-Noise (S/N) ratio is a core component of the Taguchi method used to measure quality. For surface roughness, a "lower-the-better" characteristic is desired. The S/N ratio analysis helps identify the parameter levels that not only produce a lower average roughness but also exhibit the least variation (i.e., are more robust against "noise" or uncontrollable factors). The optimal level for each parameter is the one with the highest S/N ratio, as seen in Figure 5, which corresponds to a more consistent and predictable process.
Q5: How were the specific ranges for the process parameters, like mould preheat from 150-350°C, chosen?
A5: According to the paper, "The range of the selected process parameters were decided by conducting the experiments with one variable at a time approach." This suggests that preliminary, single-factor experiments were performed to establish a working range for each parameter before they were combined in the final Taguchi-designed experiment.
Conclusion: Paving the Way for Higher Quality and Productivity
This study provides a clear and actionable roadmap for Casting Surface Roughness Optimization in the Al-7%Si investment casting process. By systematically isolating the most critical process variables, the research demonstrates that a superior and consistent surface finish is not a matter of chance, but a direct result of precise process control. The key takeaway is that focusing engineering efforts on tightly regulating mould preheat temperature, stucco grain size, and firing temperature will yield the greatest returns in surface quality, ultimately reducing costly secondary operations and enhancing part performance.
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 "Parametric Optimization of Casting Surface Roughness Produced by Ceramic Shell Investment Casting Process" by "Balwinder Singh, Pardeep Kumar and B.K. Mishra".
- Source: International Journal of Surface Engineering & Materials Technology, Vol. 1 No. 1 July-Dec. 2011, ISSN: 2249-7250
This material is for informational purposes only. Unauthorized commercial use is prohibited.
Copyright © 2025 CASTMAN. All rights reserved.