Tool Wear Parameter Optimization in Machining a Squeeze-Cast Metal Matrix Composite (Al6061-SiC) ^†

Abstract

In this research work, machining operations on an aluminum matrix composite (AMC) were optimized for improving the wear of high-speed steel tools. The squeeze casting method was used to manufacture the AMC, which had Al-6061 as matrix material and silicon carbide (wt. 15%) microparticles used as reinforcement. Feed rate (Fr), cutting speed (Cs), and depth of cut (Dc) were selected to optimize HSS tool wear rate. Using the Box–Behnken design, seventeen experiments were performed to analyze the single-factor effects and interaction effects of the process parameters on HSS tool wear rate. Experimental results show that optimal tool wear (0.964) was achieved at a Cs of 80 m/min, Fr of 0.2 rev/min, and Dc of 0.8 mm.

1. Introduction

Metal matrix composites (MMC) are a novel type of material and a swift substitute for conventional materials in manufacturing applications such as the aerospace and automobile industries [1]. Machining is one of the most common manufacturing processes for metal matrix composites to achieve the desired shapes. However, researchers face challenges in performing machining operations on MMC materials due to hard abrasive reinforcement particles, which are harder than cutting tools [2,3]. Therefore, it is important to find the optimal combination of process variables while performing turning operations on MMCs in order to achieve the desired shape and improve tool life. In this regard, Seeman et al. [2] found that Cs and Fr had a more significant effect as compared to machining time and Dc on flank wear and surface roughness. R. Suresh et al. [3] concluded that cutting speed was inversely proportional to surface roughness, which was caused by less contact between the tool and workpiece.

It has been revealed that there is still a research gap in optimizing turning operation process variables in order to reduce high-speed steel (HSS) tool wear, because machine tools account for roughly 70% of active machining production costs [4]. Therefore, the aim of this project was to optimize the variables of turning operations on Al6061-SiC composites, such as Cs, Fr, and Dc, in order to reduce HSS tool wear.

2. Materials and Methods

The aluminum matrix alloy used in this research work was a wrought 6061 aluminum alloy. A spectrometry test was performed to check the chemical composition of the 6061 Al alloy as shown in

Table 1. Spectrometry results of aluminum 6061 alloy.

Elements	Al	Mg	Fe	Si	Cu	Ti	Mn	Cr	Zn
Weight Percent. (Al)	97.05	0.14	0.7	0.43	0.24	0.15	0.24	0.8	0.25

. Silicon carbide (SiC) particles were used as reinforcement in this study. Squeeze casting was used to incorporate the SiC particles in two distinct weight percentages, 7.5% and 15%, respectively. The sample was then constructed by cutting a billet of composite squeeze-cast aluminum to prepare a turned 20 × 150 mm specimen billet.

3. Experiment Design & Setup

The ranges of selected process parameters such as Cs (40 to 120 m/min), Fr (0.1 to 0.3 mm/rev) and Dc (0.4 to 1.2 mm) have been selected based on the literature review [2,3,5] and after performing the trial experiment. Three process parameters (k) and five central points (c) have been selected to design experiments using Equation (1) [6].

n = 2 × k × (k − 1) + c

(1)

A total of 17 runs for each billet were performed using the Box–Behnken design. The TX-75Y model turning center with a soluble oil-based coolant was used for the experimental process as shown in Figure 1b. Wear was measured using the tool–workpiece distance method. In this method, a micrometer, digital Vernier caliper, and electron microscope were used to measure the flank wear of the tool. Three experiments were performed under each experimental condition to ensure the accuracy of the output response.

Table 2. Experimentation using the Box–Behnken design.

Experiment No.	Input Variables			Output Response (Tool Wear)
	Cs	Fr	Dc	Average Value (mm)
1	40	0.1	0.8	1.884
2	120	0.1	0.8	1.956
3	40	0.3	0.8	1.436
4	120	0.3	0.8	1.448
5	40	0.2	0.4	1.4592
6	120	0.2	0.4	1.452
7	40	0.2	1.2	1.456
8	120	0.2	1.2	1.456
9	80	0.1	0.4	1.428
10	80	0.3	0.4	1.028
11	80	0.1	1.2	1.924
12	80	0.3	1.2	1.444
13	80	0.2	0.8	0.964
14	80	0.2	0.8	1.456
15	80	0.2	0.8	1.468
16	80	0.2	0.8	0.968
17	80	0.2	0.8	1.444

displays the average of three responses.

4. Results and Discussion

The experimental results showed that the minimum mean value of tool wear rate (0.964) was achieved at the Cs of 80 m/min, Fr of 0.2 rev/min, and Dc of 0.8 mm. Furthermore, standard deviation (0.019) and R-squared (0.9966) demonstrated that the variation of replicated mean values was lesser than the variation of the values anticipated or predicted in the design; therefore, the developed model was a good predictor [7,8]. At a 95% confidence level, an analysis of variance (ANOVA) table showed that the mathematical model was significant, and all the selected process parameters had a significant effect on tool wear because the p value was less than 0.05, as shown in

Table 3. ANOVA table for wear rate.

Source	SS		Adj SS.	Adj MS.	F	p
Model	1.31	3	0.44	1267.99	<0.0001	significant
Cs	0.45	1	0.45	1302.4	<0.001	significant
Fr	0.47	1	0.47	1368.05	<0.0001	significant
Dc	0.39	1	0.39	1133.51	<0.0001	significant
Residual	4.85 × 10−03	13	3.45 × 10−04
Lack of Fit	3.88 × 10−03	9	4.31 × 10−04	2.83	0.1178	not significant
Pure Error	6.08 × 10−03	4	1.52 × 10−04
Cor Total	1.32	16

Standard deviation (0.019); R square (0.9966); Adj. R square (0.9958); and Pred. R square (0.9933).

.

A single factor plot showed that tool wear was reduced when Cs increased from 40 to 120 m/min, as shown in Figure 1a. It was also revealed that tool wear was minimized when Fr was reduced from 0.1 to 0.3 rev/min. Similarly, tool wear linearly increased by increasing Dc from 0.4 to 1.2 mm. Furthermore, 3D Mesh plots created in our study analyzed the effects of two parameters at a time. The Cs vs. Fr plot showed that tool wear was linearly minimized by reducing Fr from 0.1 to 0.3 mm and increasing Cs from 40 to 120 m/min as shown in Figure 2a. The Fr vs. Dc plot showed that tool wear reduced by minimizing the Dc value from 0.4 mm to 1.2 mm and increasing Fr from 0.1 to 0.3 mm/rev as depicted in Figure 2b. Finally, the Cs vs. Dc plot showed that tool wear was gradually reduced by increasing the Dc from high level to low level and Cs from low level to high level as shown in Figure 2c. It is also evident from the studies of R. Suresh et al. [5] that tool wear is reduced when Cs is increased, Fr is reduced, and Dc is increased, because the combined impact of Fr and Cs in single point cutting tools makes them prone to flank wear.

5. Validations

In order to validate the model achieved in the experiment, as shown in Equation (2), a 1.67 mm wear rate was observed under the first experimental validation condition, namely a Cs of 50 m/min, Fr of 0.15 rev/min, and Dc of 0.5 mm. A 1.47 mm wear rate was calculated using Equation (2). The percentage change in the actual and calculated values was computed as 11.88%, which shows the validity of the developed mathematical model of wear rate.

Tool Wear = 1.663 + 0.00024 × (Cs) − 2.295 × (Fr) + 0.285 × (Dc)

(2)

6. Conclusions

It was concluded that the optimal value of HSS tool wear (0.964) during CNC turning operations of Al 6061 SiC composites was achieved using the following experimental settings: Cs of 80 m/min, Fr of 0.2 rev/min, and Dc of 0.8 mm. The ANOVA table showed that Fr had the most significant effect, compared to Cs and Dc. The low value of the percentage change in values validated the mathematical model.