Optimizing Casting Hardness: A Taguchi Method Case Study for LM-6 Aluminum

This technical summary is based on the academic paper "Improvement in Hardness of LM-6 Aluminum Alloy Green Sand Castings by Taguchi Method" by Ravneet Kumar and Chandandeep Grewal, published in Asian Journal of Engineering and Applied Technology (2013).

Keywords

• Primary Keyword: Casting Process Optimization
• Secondary Keywords: LM-6 Aluminum, Rockwell Hardness, Green Sand Casting, Process Parameters, Bentonite Clay, Pouring Temperature, Taguchi L8 Orthogonal Array

Executive Summary

• The Challenge: Achieving consistent, high hardness in LM-6 aluminum alloy castings is a significant challenge due to the complex interaction of multiple process parameters in green sand casting.
• The Method: The study applied the Taguchi L8(2^7) orthogonal array method to systematically analyze the effects of five key process parameters on Rockwell hardness.
• The Key Breakthrough: Pouring temperature and the interactions between bentonite clay/moisture and bentonite clay/grain fineness were identified as the most statistically significant factors affecting the final hardness of the castings.
• The Bottom Line: By implementing an optimized set of parameters, the study achieved a validated 6.9% improvement in Rockwell hardness, demonstrating a clear path to enhanced mechanical properties through statistical process control.

The Challenge: Why This Research Matters for HPDC Professionals

In today's competitive global market, producing high-quality castings with close tolerances and minimal rejections is paramount. For versatile materials like the LM-6 aluminum alloy—valued for its excellent fluidity and corrosion resistance—achieving consistent mechanical properties like hardness is critical for performance in applications ranging from motor casings to marine components.

However, the green sand casting process, while economical, involves numerous variables (sand composition, moisture, temperature) that interact in complex ways. Controlling these parameters to consistently produce parts with optimal hardness is a persistent industry challenge. This research addresses the core problem of moving from trial-and-error adjustments to a data-driven, systematic approach for process control and quality improvement.

The Approach: Unpacking the Methodology

To identify the optimal process settings, the researchers employed the Taguchi method, a powerful statistical tool for robust engineering design. This approach dramatically reduces the number of required experiments compared to a full factorial study (from 128 to just 8), saving significant time and resources.

Method 1: Parameter and Level Selection
The study focused on five critical control factors in the green sand process, each tested at two distinct levels:
- Bentonite Clay: 4% (Level 1) vs. 6% (Level 2)
- Grain Fineness No.: 80 (Level 1) vs. 100 (Level 2)
- Moisture: 3% (Level 1) vs. 4% (Level 2)
- Pouring Temperature: 710°C (Level 1) vs. 730°C (Level 2)
- Coal Dust: 1% (Level 1) vs. 1.5% (Level 2)

Method 2: Experimental Design and Analysis
An L8(2^7) orthogonal array was used to structure the eight experimental runs. After casting the LM-6 aluminum samples according to the array, Rockwell hardness (HRB) was measured as the primary quality characteristic. The results were then analyzed using Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA) to determine which factors and interactions had the most significant statistical impact on hardness.

The Breakthrough: Key Findings & Data

The analysis revealed that not all parameters are created equal. Certain factors, and their interactions, have a disproportionately large effect on the final hardness of LM-6 castings.

Finding 1: Pouring Temperature is the Dominant Factor in Process Variability

The ANOVA results (Table V) clearly identified Pouring Temperature (Factor D) as the single most influential parameter affecting the variability of hardness. It accounted for 33.16% of the total contribution. The data showed that a lower pouring temperature of 710°C resulted in higher and more consistent hardness, likely due to better diffusion of silicon and the formation of a fine eutectic structure.

Finding 2: Critical Interactions Between Sand Components Drive Hardness

The study underscored the importance of interactions between material inputs. The interaction between Bentonite Clay and Moisture (A x C) was the second most significant factor, contributing 28.42% to the variability. This is because the development of sand-clay bonds is directly dependent on water. Similarly, the interaction between Bentonite Clay and Grain Fineness (A x B) was also highly significant (24.84% contribution), as finer grains require different binder levels for optimal coating. These findings show that adjusting one parameter in isolation is insufficient; their interplay must be managed.

Practical Implications for R&D and Operations

• For Process Engineers: This study provides a clear recipe for improving hardness: a lower pouring temperature (710°C) is critical. Furthermore, the results suggest that Bentonite clay and moisture levels must be managed in tandem, not as independent variables, to achieve optimal clay bonding and consistent mold properties.
• For Quality Control Teams: The data in Table V of the paper illustrates the powerful effect of pouring temperature and sand component interactions on hardness variability. This can inform the development of more targeted Statistical Process Control (SPC) charts, focusing on the most impactful parameters to reduce process variation and ensure parts meet mechanical specifications.
• For R&D and Metallurgy Teams: The finding that lower pouring temperatures improve hardness by influencing the eutectic structure provides a valuable insight for alloy development and solidification modeling. The significant parameter interactions (A x B, A x C) highlight the need for a holistic approach to sand system management.

Paper Details

Improvement in Hardness of LM-6 Aluminum Alloy Green Sand Castings by Taguchi Method

1. Overview:

• Title: Improvement in Hardness of LM-6 Aluminum Alloy Green Sand Castings by Taguchi Method
• Author: Ravneet Kumar and Chandandeep Grewal
• Year of publication: 2013
• Journal/academic society of publication: Asian Journal of Engineering and Applied Technology
• Keywords: Green sand Casting, LM-6 Aluminum alloy, Rockwell hardness, Taguchi L8(2^7) orthogonal arrays.

2. Abstract:

The green sand casting is most widely and economically used method for past years. The quality of castings and parameters control is very important. With increasing demand for high-quality castings with close tolerances, an attempt has been made in this study to get the optimal setting of the main parameters to improve the hardness of LM-6 Aluminum alloys castings in green sand casting. Five main parameters namely Bentonite clay, Grain fineness no., Moisture, Pouring temperature and Coal dust were identified. The effects of the selected process parameters on the hardness and the subsequent optimal settings of the parameters have been accomplished using Taguchi’s method. L8(2^7) orthogonal arrays have been selected and experiments were conducted as per experimental plan given in this array. The results indicate that all the parameters except grain fineness no and coal dust are affecting both the average and variability significantly in the hardness of LM-6 Aluminum alloys castings. The confirmatory experiments have shown improvement in Rockwell hardness to be 6.9%.

3. Introduction:

Casting is an established science for producing automotive and other components. Green sand moulding is a widely used and economical method, deriving its name from the presence of moisture. It utilizes a mixture of silica sand, bentonite clay, coal dust, and other additives. The quality of a casting is defined by its dimensional accuracy, surface finish, and soundness, which depend on the properties of the green sand and mould. The study focuses on LM-6 aluminum alloy (Al-12% Si), which possesses excellent fluidity, resistance to hot tears, and high corrosion resistance, making it suitable for thin, intricate, and pressure-tight castings. To remain competitive, foundries require close control over process parameters to ensure consistent quality at minimal cost and rejection rates.

4. Summary of the study:

Background of the research topic:

The research addresses the industrial need for consistent quality and high performance in aluminum castings produced via the green sand method. Achieving desired mechanical properties, such as hardness, requires precise control over multiple interacting process variables.

Status of previous research:

The paper acknowledges prior work in optimizing casting processes. Singh et. al. [4] studied aluminum blank sand casting using an L18 array. Syrcos [5] optimized die casting parameters for an AlSi9Cu3 alloy. Guharaja et. al. [6] used the Taguchi method to optimize green sand parameters for SG cast iron. These studies established the utility of statistical methods like Taguchi's for improving casting outcomes.

Purpose of the study:

The primary objective was to identify the optimal settings of five key green sand process parameters—Bentonite clay, Grain fineness number, Moisture, Pouring temperature, and Coal dust—to maximize the Rockwell hardness of LM-6 aluminum alloy castings.

Core study:

The study employed Taguchi's parameter design methodology. An L8(2^7) orthogonal array was selected to investigate the five main factors at two levels each, as well as two potential interactions (Bentonite clay × Grain fineness and Bentonite clay × Moisture). Experiments were conducted based on this array, and the Rockwell hardness of the resulting castings was measured. Signal-to-Noise ratio analysis and ANOVA were performed to quantify the effect of each parameter and determine the optimal combination for maximizing hardness. A confirmatory experiment was then conducted to validate the predicted improvement.

5. Research Methodology

Research Design:

The study utilized Taguchi's robust parameter design. The quality characteristic was Rockwell Hardness, with the objective function being "higher-the-better." An L8(2^7) orthogonal array was chosen as the experimental design, which is suitable for studying up to seven factors at two levels each. The total degrees of freedom required for five main factors and two interactions was 7, making the L8 array an efficient choice.

Data Collection and Analysis Methods:

Castings were produced according to the 8 experimental conditions defined by the L8 array. Rockwell Hardness (HRB) was measured at multiple locations on each casting. The collected data was analyzed using two primary methods:
1. S/N Ratio: The "higher-the-better" S/N ratio was calculated to measure the deviation from the desired hardness value.
2. Analysis of Variance (ANOVA): ANOVA was performed on both the raw hardness data and the S/N data to determine the statistical significance and percentage contribution of each factor and interaction on the response.

Research Topics and Scope:

The research was focused on the green sand casting of LM-6 aluminum alloy. The scope was limited to five selected process parameters and their effect on a single response variable: Rockwell hardness. The study also considered two specific two-factor interactions.

6. Key Results:

Key Results:

• The optimal parameter combination for maximizing hardness was determined to be: 4% Bentonite clay (A1), 80 Grain Fineness number (B1), 4% Moisture (C2), 710°C Pouring Temperature (D1), and 1.5% Coal dust (E2).
• ANOVA of the S/N data revealed that Pouring Temperature was the most significant factor influencing hardness variability, with a percentage contribution of 33.16%.
• The interaction between Bentonite clay and moisture (A×C) and the interaction between Bentonite clay and grain fineness (A×B) were also highly significant, contributing 28.42% and 24.84%, respectively.
• Grain fineness number and coal dust were found to affect the process average but not its variability.
• The confirmatory experiment, conducted at the optimal settings, yielded an average hardness of 88.667 HRB, representing a 6.9% improvement over the initial average. This result fell within the predicted 99% confidence interval (88.478 < µCE < 99.642 HRB), validating the experimental model.

Figure Name List:

• Fig 1: Linear graph of L8 (2^7).
• Fig 2: Dimensions of the pattern
• Fig 3: Photograph of Aluminum alloys sand castings
• Fig 4: Response plots for Rockwell hardness of significant parameters only
• Fig 5: Expected improvement after validation

7. Conclusion:

The study successfully optimized the green sand casting process for LM-6 aluminum alloy to improve hardness using the Taguchi method. The optimal combination of parameters was identified as 4% Bentonite clay, AFS 80 grain fineness number, 4% moisture, 710°C pouring temperature, and 1.5% coal dust. The pouring temperature was the most significant contributor (33.16%) to process variability, followed by the interactions of bentonite clay with moisture (28.42%) and grain fineness number (24.84%). A confirmation experiment validated the findings, demonstrating a 6.9% improvement in average hardness. The study concluded that pouring temperature and the key interactions affect both the average and variability of hardness, while grain fineness and coal dust affect the average only.

8. References:

• [1] Berth Mary and Pedecin L.J., (1990), "Sand handling systems affect casting produced in green sand," Modern casting, vol-II, pp. 42 to 44.
• [2] Morgen A.D., (1982) "Highest quality casting - Which moulding process?" Foundry Trade Journal, pp. 611 to 622.
• [3] Burns T.A. (1986), "The Foseco's foundryman's handbook, facts, figures and formulae" Pergamon press, New York, pp 128 & 147 to 148.
• [4] Singh Ajit Pal, and Nekere Mekonnen Liben, (2012), "optimization of aluminium blank sand casting process by using taguchi's robust design method" International Journal for Quality research, Vol.6, No.1, pp. 81 to 97.
• [5] Syrcos G. P. (2003), "Die casting process optimization using Taguchi methods" Journal of Materials Processing Technology, Volume 135, Issue 1, pp. 68 to 74.
• [6] Guharaja S., Haq A. Noorul and Karuppannan K.M., (2006), "Optimization of green sand casting process parameters by using Taguchi's method", The International Journal of Advanced Manufacturing Technology, Volume 30, Numbers 11-12, pp.1040-1048.
• [7] Barua, P.B., Kumar, P. & Gaindhar, J.L. (Jan., 1997), "Optimization of mechanical properties of V process casting by Taguchi method" Indian Foundry Journal, pp. 17-25.
• [8] Nazirudeen S. S. Mohamed and Nagasivamuni B. (Feb., 2012), "Improving the Quality of Green Sand Castings to Minimise the Defects Using Artificial Neural Network" Vol 58, No. 2, pp. 32-37
• [9] Kackar N. Raghu, (1985) "Off-line quality control, parameters design, and the Taguchi method" Journal of quality Technology, Vol. 17, No. 4, pp. 176 to 188.
• [10] Heine W.R., Looper R.C., Jr. Rosenthal, C.P., "Principles of metal castings" Tata-McGraw hill, 1988.
• [11] Ross, J.P., "Taguchi technique for quality engineering," New York: McGraw-Hill

Expert Q&A: Your Top Questions Answered

Q1: Why was the Taguchi L8 orthogonal array chosen for this experiment instead of a full factorial design?

A1: The Taguchi method was chosen for its efficiency. A full factorial experiment with five factors at two levels would require 2^5 = 32 runs, without even considering interactions. The L8 orthogonal array allowed the researchers to gather robust data on the main factors and key interactions with only 8 experimental runs, significantly reducing the time, material cost, and labor required for the study.

Q2: Which single process parameter had the most significant impact on the hardness of the LM-6 castings?

A2: According to the ANOVA on the S/N data (Table V), Pouring Temperature (Factor D) was the single most influential factor. It accounted for 33.16% of the percentage contribution to variability. The study found that the lower temperature of 710°C produced superior hardness results.

Q3: The paper highlights the importance of parameter interactions. Which interactions were most critical?

A3: The two most critical interactions were Bentonite clay × Moisture (A×C) and Bentonite clay × Grain Fineness (A×B). They contributed 28.42% and 24.84% to the variability, respectively. This shows that the effectiveness of the bentonite binder is highly dependent on both the water content, which activates it, and the surface area of the sand grains it must coat.

Q4: What were the specific optimal parameter settings recommended by the study to achieve the highest hardness?

A4: The study identified the optimal combination as A1B1C2D1E2. This corresponds to: 4% Bentonite clay, 80 Grain Fineness Number, 4% Moisture, 710°C Pouring Temperature, and 1.5% Coal dust. This specific recipe was predicted to yield the best hardness performance.

Q5: How confident are the researchers in these findings? Was the 6.9% improvement validated?

A5: The findings were validated with a high degree of confidence. After determining the optimal parameters, the researchers ran a confirmation experiment. The average hardness achieved (88.667 HRB) represented a 6.9% improvement and, crucially, fell within the calculated 99% confidence interval (88.478 to 99.642 HRB). This confirms that the model is reliable and the improvement is statistically significant.

Q6: Why does a lower pouring temperature (710°C) lead to higher hardness in LM-6 alloy?

A6: The paper suggests this is due to metallurgical reasons. A lower pouring temperature may promote better diffusion of silicon within the aluminum matrix, leading to the formation of a fine eutectic structure during solidification. Finer microstructures generally correspond to higher hardness and improved mechanical properties.

Q7: Were any parameters found to be insignificant?

A7: While all parameters had some effect, the ANOVA showed that Grain Fineness Number (B) and Coal Dust (E) on their own were less significant in affecting the variability of the process compared to temperature and the interactions. However, they were still found to affect the average hardness, which is why they are included in the final optimal setting recommendation.

Conclusion: Paving the Way for Higher Quality and Productivity

This study provides a powerful demonstration of how Casting Process Optimization using statistical methods like the Taguchi design can yield significant and predictable improvements in product quality. By moving beyond isolated parameter adjustments and analyzing the system holistically, the researchers identified not only the most critical factors—like pouring temperature—but also the crucial interactions that govern the final hardness of LM-6 aluminum castings. The resulting 6.9% increase in Rockwell hardness is a tangible outcome that translates directly to enhanced component performance and reliability.

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 "Improvement in Hardness of LM-6 Aluminum Alloy Green Sand Castings by Taguchi Method" by "Ravneet Kumar and Chandandeep Grewal".

Source: https://doi.org/10.51983/ajeat-2013.2.2.688

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