This paper presents the performance of ductile cast iron grinding machining using water-based zinc oxide nanoparticles as a coolant. The experimental data was utilized to develop the mathematical model for first- and second-order models. The second order gives worthy performance of the grinding. The results indicate that the optimum parameters for the grinding model are 20 m/min table speed and 42.43
The automotive industry is one of the main users of ground components. Many solutions for grinding problems come from classical operations related to engine or transmission components. Classical examples are crankshaft grinding and camshaft grinding. Since the automotive industry is one of the major drivers for grinding development, it was chosen to be the focus of this study. Energy consumption by machining and grinding processes has not been a concern for industry because the energy cost is much lower than the other costs, such as materials, labor, and tooling [
Sustainability in abrasive machining is a growing concern that has been recognized by both academia and industry [
Zinc oxide nanoparticle materials were selected because zinc is commonly added to the primary coolant to prevent corrosion. A two-step method was used to prepare the nanofluid. Basically nanoparticles are first produced as a dry powder, typically by inert-gas condensation, which involves the vaporization of a source material in a vacuum chamber and subsequent condensation of the vapor into nanoparticles through collisions with the controlled pressure of an inert gas such as helium. The resulting nanoparticles are then dispersed into a fluid in a second processing step. An advantage of this technique in terms of eventual commercialization of nanofluids is that the inert-gas condensation technique has already been scaled up to economically produce tonnage quantities of nanopowders. Thus, the dispersed nanoparticles which come in liquid form with a volume of one liter have 20% weight concentration with a 30–40 nm particle size, an 8.9 pH level, and density equal to 5600 kg/m³. It is diluted to a 0.15% volume concentration. The conversion of the weight percent concentration to volume concentration is expressed in (
For a two-phase system, some important issues have to be faced. One of the most important issues is the stability of the nanofluids, and it remains a considerable challenge to achieve the desired stability of the nanofluids. To achieve stability in the dilution, the solution needs to be stirred continuously for one hour with the mixture set to 1000 rpm. Nanoparticles have a tendency to be aggregated. The use of surfactants is an important technique in enhancing the stability of nanoparticles in fluids. However, the functionality of the surfactants under high temperature is also a major concern, especially for high-temperature applications. Therefore, no surfactant is applied in this study.
The design of experiments (DOE) techniques enable designers to determine simultaneously the individual and interactive effects of many factors that could affect the output results. The statistical experimental designs (response-surface designs (RSM)) are most widely used in optimization experiments. The central composite design (CCD) is the most popular of the many classes of RSM designs due to the properties listed in Table
Design of experiment.
Specimen | Table speed (m/min) | Depth of cut ( |
---|---|---|
A | 20 | 20 |
B | 20 | 40 |
C | 20 | 60 |
D | 30 | 20 |
E | 30 | 40 |
F | 30 | 60 |
G | 40 | 20 |
H | 40 | 40 |
I | 40 | 60 |
A CCD can run sequentially. It can be naturally partitioned into two subsets of points: the first subset estimates linear and two-factor interaction effects while the second estimates curvature effects. The second subset need not be run when analysis of the data from the first subset indicates the absence of significant curvature effects. CCDs are also very efficient, providing much information on experimental variable effects and the overall experimental errors in a minimum number of required runs. They are very flexible. There is good commercial software available to help with designing and analyzing response-surface experiments. Table
The workpiece and its different isometric views.
The grinding process was undertaken using a Supertec precision grinding machine, model STP-102ADCII. A vitrified bond aluminum oxide grinding wheel (PSA-60JBV) with an average abrasive size of 60 grains was used. The workpiece material was block ductile iron with a carbon content of 3.5–3.9% and average hardness of 110-Rockwell C. The width and length of the workpiece surface for grinding were 35 mm and 80 mm, respectively. First, the workpiece was clamped onto a clamper jaw since cast iron is not attracted to the magnet field. Then the zero point of the
This section presents the performance characteristics of ductile cast iron grinding with a conventional coolant and a water-based zinc oxide nanocoolant. The mathematical models for the prediction of the material removal rate and tool wear rates are presented in this section. These models were developed using the accumulated data obtained from experiments using a conventional soluble oil coolant and a zinc oxide nanocoolant. The significance and adequacy of these models are verified by analysis of variance using the response-surface method.
The material removal rate (MRR) for conventional coolant and nanocoolant, as well as for single and multipass grinding processes, is represented in Table
Material removal rate for each coolant and type of grinding.
Specimen | Table speed (m/s) | Depth of cut ( |
Material removal rate (cm³/s) | |||
---|---|---|---|---|---|---|
Single pass | Multiple pass | |||||
Conventional coolant | Nanocoolant | Conventional coolant | Nanocoolant | |||
A | 20 | 20 | 0.024 | 0.020 | 0.032 | 0.023 |
B | 20 | 40 | 0.049 | 0.041 | 0.056 | 0.045 |
C | 20 | 60 | 0.072 | 0.061 | 0.081 | 0.071 |
D | 30 | 20 | 0.031 | 0.025 | 0.041 | 0.031 |
E | 30 | 40 | 0.065 | 0.053 | 0.073 | 0.063 |
F | 30 | 60 | 0.096 | 0.081 | 0.105 | 0.093 |
G | 40 | 20 | 0.045 | 0.037 | 0.063 | 0.046 |
H | 40 | 40 | 0.096 | 0.079 | 0.112 | 0.095 |
I | 40 | 60 | 0.155 | 0.122 | 0.159 | 0.156 |
Material removal rate for each coolant and type of grinding.
However, when using zinc oxide nanocoolant, the MRR was slightly lower than that of the conventional coolant. This is due to the nanoparticle having exceptional tribological properties, which can reduce friction under extreme pressure conditions. This is supported by the findings from Wu et al. [
ANOVA results for first-order and water-based zinc oxide nanocoolant.
Source | Degree of freedom | Sum of sq. |
|
|
---|---|---|---|---|
Single-pass grinding | ||||
Model | 3 | 0.00824733 | 98.4364 | <.0001 |
Error | 6 | 0.00016757 | ||
C. total |
|
|
||
Interaction | 2 | |||
Lack-of-fit | 5 | 0.00016307 | 7.2474 | 0.2745 |
Pure error | 1 | 0.00000450 | ||
Total |
|
|
||
|
||||
Multiple-pass grinding | ||||
Model | 5 | 19.60468930 | 262.3551 | <.0001 |
Error | 4 | 0.14945158 | ||
C. total |
|
|
||
Interaction | 2 | |||
Lack-of-fit | 3 | 0.14878180 | 44.4271 | 0.1134 |
Pure error | 1 | 0.00066978 | ||
Total |
|
|
Even though the first-order model was found to be adequate, the second-order model was postulated to extend the variables’ range in obtaining the relationship between the MRR and the machining independent variables. The adequacy of the first-order model is verified using the
ANOVA results for second-order and water-based zinc oxide nanocoolant.
Source | Degree of freedom | Sum of sq. |
|
|
---|---|---|---|---|
Single-pass grinding | ||||
Model | 5 | 0.00839245 | 291.6117 | <.0001 |
Error | 4 | 0.00002245 | ||
C. total |
|
|
||
Interaction | 2 | |||
Lack-of-fit | 3 | 0.00001795 | 1.3298 | 0.5504 |
Pure error | 1 | 0.00000450 | ||
Total |
|
|
||
|
||||
Multiple-pass grinding | ||||
Model | 5 | 19.69133675 | 250.8286 | <.0001 |
Error | 4 | 0.06280412 | ||
C. total |
|
|
||
Interaction | 2 | |||
Lack-of-fit | 3 | 0.06213434 | 30.9228 | 0.1313 |
Pure error | 1 | 0.00066978 | ||
Total |
|
|
To test whether the model is adequate and fit to predict the MRR in both single-pass and multiple-pass grinding, Figure
Comparison between the experimental and predicted results for both single- and multiple-pass grinding.
Tool wear is usually the most relevant parameter inspected, as it has direct influence on the final product quality, the machine tool performance, and tool lifetime. During grinding, cutting wheels remove material from the workpiece to achieve the required shape, dimension, and surface roughness (finish). However, wear occurs during the grinding action and will ultimately result in the failure of the cutting wheel. When the tool wear reaches a certain level (0.3 mm), the tool or active edge has to be replaced to guarantee the desired cutting action. The tool wear was measured in mm using a Taylorsurf profilometer. Several readings were taken and the average was calculated. The readings were taken at several points and the average was calculated. Figure
Tool wear for each coolant and type of grinding.
G-ratio is defined as the volume of work material removed divided by the volume of wheel wear. A high G-ratio indicates a low wheel wear rate [
G-ratio for different coolants and types of grinding.
The grinding of ductile cast iron using Al2O3 wheels under water-based zinc oxide nanocoolant and conventional coolant was studied. Compared to the water-based nanocoolant, tool wear could be substantial compared to conventional coolant. However, nanocoolant could achieve the same MRR without increasing the grinding forces. During nanocoolant grinding, a dense and hard slaggy layer was found on the wheel surface and could benefit the grinding performance. Nanoparticles reduce the friction of the grinding wheel and workpiece. Less friction leading to low heat density generates and minimizes the tool wear. Experimental results showed that the G-ratio could be improved with high concentrations of nanocoolant. Thus, the study of grinding using water-based nanocoolant focuses on advanced lubrication properties. Furthermore, forthcoming work will investigate the machining parameters necessary for optimal quality to determine the manufacturing resource costs required to maximize efficiency.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors would like to thank Universiti Malaysia Pahang for financial support under University Research Project no. RDU120310. The authors also thank Mr. Shabaruddin for his help and for preparing the workpiece during the experimental work.