Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method

Round 1

Reviewer 1 Report

The manuscript can be accepted for publication should the author address the following comments:

1. Framed abstract, conclusion does not define properly the problem being addressed, need for the work or the outcomes achieved.

2. The scientific value, research gap, novelty, originality, and contribution of the paper is not clear.

3. A flowchart explaining how these considered methods are applied to this research will be very helpful.

4. A response table has to be developed to find the ultimate optimal parametric combination for all methods considered. Pl. refer the following articles.

5. Why the adopted methods are considered for this research? Why not any other?

6. Survey of literature need inclusion of latest work. The following may be useful.

7. Axes of Figure 3 should be included.

Author Response

Dear reviewer,

We appreciate your careful consideration of this paper, as well as your detailed suggestions, which have been extremely helpful in improving the manuscript. We revised the manuscript in response to the comments. Our responses to each of your comments are listed below:

Question 1: Framed abstract, conclusion does not define properly the problem being addressed, need for the work or the outcomes achieved.

Answer 1: Many thanks for your comments. We have revised the Abstract and Conclusions of the manuscrtip (please see the revised manuscript).

Question 2: The scientific value, research gap, novelty, originality, and contribution of the paper is not clear.

Answer 2: Many thanks for the comments. The scientific feature of the article is that it has applied a scientific method - the MABAC method to solve the MCDM problem. In addition, it used the Taguchi method for experimental design and analysis. In particular, thanks to the application of scientific research methods, it has found the best dressing mode for external grinding process.

To clarify the novelty of the manuscript, and also according to your comments, a table (Table 1) that outlines several previous studies on the dressing process for different types of grinding was added. This table helps to determine the ultimate optimal parametric combination for all methods considered as well as contributes to clarifying the novelty of this manuscript.

To clarify the novelty of the manuscript, and also according to your comments (Question 4), a table (Table 1) that outlines several previous studies on the dressing process for different types of grinding was added. This table helps to determine the ultimate optimal parametric combination for all methods considered as well as to clarifying the novelty of this manuscript. From the analysis of previous studies and this table it has been shown that to date, there has been no research on determining the best dressing mode among many different dressing setups for external grinding SKD11 tool steel using a MCDM method. And this is also a new point and contribution of the manuscript compared to previous studies. These points have also been added to the Introduction of the manuscript. In addition, this study identifies the best dressing mode for getting three criteria including minimal surface roughness, maximum wheel life, and minimal roundness. It also adds to the novelty of this study.

Question 3: A flowchart explaining how these considered methods are applied to this research will be very helpful.

Answer 3: Many thanks for your suggestion. However, the MABAC method is one of the simple and easy to apply to solve the MCDM problem. So we would like to only state the steps as outlined in section 2.1 of the article. In this work, in our opinion, the dressing process and grinding process is more complicated. Therefore, we have used the model in Figure 1 to describe. For the reasons mentioned above, let we should not use one more flowchart to describe the process.

Question 4: A response table has to be developed to find the ultimate optimal parametric combination for all methods considered. Pl. refer the following articles.

Answer 4: Thank you very much for your comment. We have added a table (Table 1) that outlines some of the previous studies on the dressing process of different types of grinding. In this table, the specifications of grinding type, wheels, dresser, workpiece material, the objective of study, and the output optimum factors presented in these studies  are indicated. This table helps to determine the ultimate optimal parametric combination for all methods considered. In addition, it also contributes to clarifying the novelty of this manuscript.

Table 1: Several previous research results on optimization of dressing process

Grinding type	Wheel type	Dressing tool	Workpiece material	Method	Objective	Optimum parameters	Reference
Surface grinding	Alumina	Single point diamond dresser	Ti–6Al–4V	- Experimental	- Grinding ratio - Grinding surface	- Dressing infeed	[1], [2], [3]
Surface grinding	Silicon carbide	Multi-point diamond dresser	90CrSi	- Experimental - Taguchi and GRA	- Surface roughness - Flatness tolerence	- Dressing feed rate - Coarse dressing depth - Coarse dressing times - Fine dressing depth - Fine dressing times - Non-feeding dressing	[4]
Cylindrical plunge grinding	Bronze-bonded diamond	Laser tool	Alumina ceramic	- Experimental	- Wheel surface - Grinding force	-Laser power density - Pulse overlap ratio -Scanning cycle number	[5]
Cylindrical plunge grinding	Aluminium oxide	Unclear	AISI 52100	- Experimental	- Production time	-Dressing interval	[6]
Surface grinding	Silicon carbide	Multi-point diamond dresser	SKD11	- Experimental - Taguchi and GRA	- Material removal rate - Flatness tolerence	- Dressing feed rate - Coarse dressing depth - Coarse dressing times - Fine dressing depth - Fine dressing times - Non-feeding dressing	[7]
Cylindrical plunge grinding		Multi-point diamond dresser	EN-31	- Experimental - TOPSIS and AHP	- Surface finish	- Dressing feed rate - Dressing depth - Width of diamond dresser - Dresser drag angle	[8]
Plunge face grinding	Diamond cup	Vitrified cup dresser, white corundum	PCBN	- Experimental	- Wheel wear - Grinding power	- Dressing feed rate - Dressing depth	[9]
Cylindrical plunge grinding	Vitrified CBN	Synthetic diamond disc	AISI 52100		- Surface roughness - Grinding force	- Number of dressing passes	[10]
Cylindrical plunge grinding	Aluminium oxide	Diamond roller	150Cr14	- Experimental - Multi-objective optimization	-Production rate	- Dressing feed ratio - Radial infeed	[11]

Question 5: Why the adopted methods are considered for this research? Why not any other?

Answer 5: Thank you very much for your comment. In fact, there are many MCDM methods which have been used for mechanical processes such as TOPSIS, MARCOS, MOORA etc.  The MABAC method is a relatively new MCDM method (it was only proposed in 2015 [12]). This method can be applied to both the qualitative attributes and the quantitative attributes [12]. It is therefore applicable to mechanical processing processes. In practice, it has been used for several mechanical processes such as milling [13], wire electrical discharge machining [14], etc. We would like to apply it to the dressing process to determine the best dressing parameters for external grinding SKD11 tool steel. This is both to use a new MCDM method for a machining process and to compare it with other MCDM methods such as TOPSIS and MARCOS.

Question 6: Survey of literature need inclusion of latest work. The following may be useful.

Answer 6: Thank you very much for your comment. Based on your comments, we have done it as stated in Answer 4.

Question 7: Axes of Figure 3 should be included.

Answer 7: Thank you very much for your comment. The axes of Figure 3 have been added with the addition of an extra drawing to help understand the position of the axes on the grinding wheel.

a)

b)

c)

Figure 3: Wheel topography after dressing [15].a) After rough dressing; (b) after fine dressing; c) after non-feeding dressing

We hope that these changes will meet your requirements.

Thank you for your consideration.

 

Sincerely,

 

The authors,

 

 

References

 

1. Mukhopadhyay, M., et al., Effect of Dressing Infeed on Alumina Wheel During Grinding Ti–6Al–4V Under Varying Depth of Cut, in Advances in Forming, Machining and Automation. 2019, Springer. p. 551-560.
2. Mukhopadhyay, M., et al., Impact of dressing infeed on SiC wheel for grinding Ti-6Al-4V. Materials and Manufacturing Processes, 2019. 34(1): p. 54-60.
3. Mukhopadhyay, M. and P.K. Kundu, Optimization of dressing infeed of alumina wheel for grinding Ti-6Al-4V. Materials and Manufacturing Processes, 2018. 33(13): p. 1453-1458.
4. Tung, L.A., et al. Optimization of dressing parameters of grinding wheel for 9CrSi tool steel using the taguchi method with grey relational analysis. in IOP Conference Series: Materials Science and Engineering. 2019. IOP Publishing.
5. Deng, H., et al., Processing parameter optimization for the laser dressing of bronze-bonded diamond wheels. Applied surface science, 2014. 290: p. 475-481.
6. Xiao, G. and S. Malkin, On-line optimization for internal plunge grinding. CIRP annals, 1996. 45(1): p. 287-292.
7. Hong, T.T., et al. Multi response optimization of dressing conditions for surface grinding SKD11 steel by HaiDuong grinding wheel using grey relational analysis in Taguchi method. in International Conference on Engineering Research and Applications. 2020. Springer.
8. Patil, S.S. and Y.J. Bhalerao. Selection of levels of dressing process parameters by using TOPSIS technique for surface roughness of en-31 work piece in CNC cylindrical grinding machine. in IOP Conference Series: Materials Science and Engineering. 2017. IOP Publishing.
9. Denkena, B., A. Krödel-Worbes, and D. Müller-Cramm, Wear-adaptive optimization of in-process conditioning parameters during face plunge grinding of PcBN. Scientific Reports, 2022. 12(1): p. 1-11.
10. Prusak, Z., J. Webster, and I. Marinescu, Influence of dressing parameters on grinding performance of CBN/Seeded Gel hybrid wheels in cylindrical grinding. International journal of production research, 1997. 35(10): p. 2899-2916.
11. Aleksandrova, I., Optimization of the dressing parameters in cylindrical grinding based on a generalized utility function. Chinese Journal of Mechanical Engineering, 2016. 29(1): p. 63-73.
12. Alinezhad, A., New methods and applications in multiple attribute decision making (MADM). 2019: Springer.
13. Lukic, D., et al., Multi-criteria selection of the optimal parameters for high-speed machining of aluminum alloy Al7075 thin-walled parts. Metals, 2020. 10(12): p. 1570.
14. Shivakoti, I., et al. Parametric optimization of WEDM using MABAC method. in 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO). 2019. IEEE.
15. Hung, L.X., Optimization on Determination of Dressing Parameters, Lubricant Conditions and Exchanged Grinding Wheel Diamters in Internal Cylindrical Grinding Process. 2019, Thai Nguyen University of Technology.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

# Review comments

The authors have found out the best dressing parameters for external cylindrical grinding using MABAC Method.

The authors have performed a considerable amount of work. However, there some serious issues with the manuscript.

Authors have claimed that “no studies have been conducted to determine the best dressing mode for external cylindrical grinding”. What is the difference in the concept of dressing of a wheel being used in surface grinding or cylindrical grinding?

There are other relevant papers that investigate the impact of dressing infeed during grinding. Do include these papers and enrich the quality of the discussion.

In L76, why is it “maximal tool wear rate”?

How do you measure the in situ SR? Moreover, wheel life is a vague term as there may be self-sharpening during the process. You must justify that. Or, in other case you may take the rate of wheel wear.

There are some typo errors: L19, L94: SR instead of RS; L76 is MRR not MMR. Please check the manuscript thoroughly.

Write the Materials and methods section in more progressive way keeping in mind your aim only.

Experimental section is not clear. You must not mix the dressing and the grinding. Process parameters for grinding should come in the next level of dressing parameters, as you are optimizing dressing parameters based on the grindability. Hence, Figure 1 is incorrect.  What do you want to mean by dressing parameters? Have you changed the feed rate of the dresser?

What are the grinding conditions/parameters you used to check the effectiveness of the differently dressed wheels?

Though the authors have done significant amount work, the paper is not arranged properly. Authors must emphasize the experimental part, which is not so clear. They should also highlight the novelty of the work citing complexities of the grinding, specially for difficult-to-cut materials. In order to do so you may refer and cite the following relevant papers.

Author Response

Dear reviewer,

We appreciate your careful consideration of this paper, as well as your detailed suggestions, which have been extremely helpful in improving the manuscript. We revised the manuscript in response to the comments. Our responses to each of your comments are listed below:

Question 1: Authors have claimed that “no studies have been conducted to determine the best dressing mode for external cylindrical grinding”. What is the difference in the concept of dressing of a wheel being used in surface grinding or cylindrical grinding?

Answer 1: Many thanks for your comments. You are right. Writing “no studies have been practiced to determine the best dressing mode for external cylindrical grinding” would be inaccurate. We therefore revised it as follows: "To date, there has been no research on determining the best dressing mode among many different dressing settings for external grinding SKD11 tool steel using a MCDM method". The difference compared with previous studies is that an MCDM method has been applied to determine the best dressing mode for external grinding SKD11 tool steel.

Question 2: There are other relevant papers that investigate the impact of dressing infeed during grinding. Do include these papers and enrich the quality of the discussion.

Answer 2: Many thanks for the comments. We have added 8 references about determining optimum dressing parameters for different types of grinding and added Table 1 to the Introduction to clarify this issue.

Question 3: In L76, why is it “maximal tool wear rate”?

Answer 3: Thank you very much for your comment.  It was entirely our mistake. The phrase has been changed to "minimal tool wear rate."

Question 4: How do you measure the in situ SR? Moreover, wheel life is a vague term as there may be self-sharpening during the process. You must justify that. Or, in other case you may take the rate of wheel wear.

Answer 4: Thank you very much for your comment. We would like to explain that process as follows: The SR is determined not in situ, but immediately after a sample is ground. A researcher performs measurements while other samples are being machined by someone else. We were able to determine which samples had surface roughness that exceeded the allowable value (in this case, 0.5 m) in this manner. When the roughness of a sample exceeds the allowable value, the grinding wheel must be dressed. And the wheel life T with the given dressing mode is the total grinding time of all machined parts.

 

Question 5: Write the Materials and methods section in more progressive way keeping in mind your aim only.

Answer 5: Thank you very much for your comment. We have rewritten the Materials and method section. Specifically, the following part was added:

2.3 Method for determining the best dressing mode for external grinding SKD11 tool steel

2.3.1 Method for determining the best dressing mode for external grinding SKD11 tool steel

The process to determine the best dressing mode for external grinding SKD11 tool steel is as follows:  First, design and conduct an experiment to determine the best dressing mode for external grinding when processing SKD11 tool steel.  Because the dressing process has many input parameters, there will be many experiments to determine the best dressing mode. The experimental number is the number of dressing mode solutions. Then select the best dressing mode by applying an MCDM method (in this case - the MABAC method).

2.3.2 Experimental work

Question 6: Experimental section is not clear. You must not mix the dressing and the grinding. Process parameters for grinding should come in the next level of dressing parameters, as you are optimizing dressing parameters based on the grindability. Hence, Figure 1 is incorrect.  What do you want to mean by dressing parameters? Have you changed the feed rate of the dresser?

Answer 6: Thank you very much for your comments. True to your comments! The diagram in Figure 1 is incorrect. The input parameters must be the parameters of the dressing process. In this case 6 parameters, including the dressing feed rate (as given in Table 1). Figure 1 has also been redrawn as follows:

Figure 1: Experimental model

Question 7: What are the grinding conditions/parameters you used to check the effectiveness of the differently dressed wheels?

Answer 7: Thank you very much for your comment. The grinding parameters which were used to check the effectiveness of the differently dressed wheels were presented in the sub-section 2.3. These factors are the workpiece material: SKD11 tool steel; the grinding wheel speed: 29.3 (m/s); the longitudinal feed rate: 1.8 (m/min); the total depth of cut: 0.05 (mm), and the depth of cut: 0.005 (mm/ stroke).

Question 8: Though the authors have done significant amount work, the paper is not arranged properly. Authors must emphasize the experimental part, which is not so clear. They should also highlight the novelty of the work citing complexities of the grinding, specially for difficult-to-cut materials. In order to do so you may refer and cite the following relevant papers.

Answer 8: Thank you very much for your comments. Thank you very much for your comments and the references you provided. We have added those documents to the Introduction along with Table 1. These references and Table 1 aid in determining the ultimate best parametric combination for all methods under consideration. Furthermore, it contributes to clarifying the manuscript's novelty.

We hope that these changes will meet your requirements.

Thank you for your consideration.

 

Sincerely,

 

The authors,

Author Response File: Author Response.pdf

Reviewer 3 Report

 

Authors present the MABAC (Multi-Attributive Border Approximation Area Comparison) method to solve the MCDM problem in the dressing process for cylindrical external grinding.

1            To apply MABAC method authors presents, on short, in introduction the state of art in using MCDM problem in manufacturing, referring to the four MCDM methods, TOPSIS, MARCOS, EAMR and MAIRCA and to the two weight calculation methods, Entropy and MEREC. For MABAC method the result is verified by using TOPSIS and MARCOS methods and the MEREC and Entropy methods were used to determine the weights of criteria.

2            Based on these data, authors develop MABAC method for MCDM in the dressing process for cylindrical external grinding. The method is clearly presented and involved 7 steps. For calculation the criterion weight in MABAC method, the authors presented, also in steps, the Entropy and MEREC methods. Authors developed an experimental plan by establishing the input and the output parameters. Six process factors, including the coarse dressing depth, the coarse dressing -passes, the fine dressing depth, the fine dressing -passes, the non-feeding dressing, and the dressing feed rate were chosen for the exploration. The experiments were designed according to the Taguchi method with the design L16 orthogonal array.

3            Based on these experimental data, using MABAC method with the Entropy and MEREC criterions weights, authors determine the best alternative of input parameters for obtaining the best results for output parameters. Authors prove the ability to analyze and interpret the data obtained through developed research.

4            The optimal values for output parameters were obtained. The results are accomplished by wheel topography after dressing. To verify the results of applying the MABAC method, two methods TOPSIS and MARCOS were applied. The comparison shows good agreement between them and validate the MABAC method. From their study, the optimal wheel dressing parameters to achieve the minimum surface roughness, the maximum wheel life and the minimum roundness simultaneously are: nr=3, ar=0.04 (mm), af=0.005 (mm), nf=2, n0=2, and Sd=1 (m/min.).

The authors investigate for the first time the MABAC method to solve the MCDM problem in dressing process for external grinding. The results are accurate and contribute to the increasing of knowledge in the field. The quotations of the previous work are well placed in the text and contribute to highlighting the authors' own contributions. The figures and table are in accordance with the paper format. The conclusions are clear, so the paper could be published in the current form. No future directions of the research are presented.

Author Response

Dear Editors,

 

We are grateful for your careful consideration of this paper, and we also very much appreciate your detailed suggestions, which have been very helpful in improving the manuscript. We have revised the paper based on the comments. First, the abstract and the conclusions of the manuscript have been improved. In addition, 8 references about determining optimum dressing parameters for different types of grinding and Table 1 were added into the Introduction to clarify the knowledge gap and the contributions of the manuscript. The section "Materials and methods " has also been improved to clarify method for determining the best dressing mode for external grinding SKD11 tool steel. Figure 1 has also been changed to reflect the nature of the experiment. Besides, the axes of Figure 3 were added added with the addition of an extra drawing to help understand the position of the axes on the grinding wheel. Several improvements in writing as well as in the correction in grammar, spelling and punctuation have been made with the help of a native English speaker. Moreover, reviewers' questions were also answered individually.

 

We hope that these changes will meet your requirements.

Thank you for your consideration.

 

Sincerely,

 

The authors,

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have remarkably improved the content of the manuscript; however, the following comments are still not addressed properly:

1. Response tables has to be developed to find the global optimal parametric combination for all methods considered. Pl. refer the said tables of the following articles.

a.       https://doi.org/10.1186/s44147-022-00087-3 (Table 8 or 9 or 10)

b.       https://doi.org/10.1007/s12008-022-00842-z[P1]  (Table 6 or 12 or 17)

2. Survey of literature need inclusion of latest work. Inclusion of following articles may improve the quality of the manuscript.

a.       https://doi.org/10.1007/s11831-022-09731-w

b.      https://doi.org/10.31181/oresta190222046c

Author Response

Dear reviewer,

We appreciate your careful consideration of this paper, as well as your detailed suggestions, which have been extremely helpful in improving the manuscript. We revised the manuscript in response to the comments. Our responses to each of your comments are listed below:

Question 1: Response tables has to be developed to find the global optimal parametric combination for all methods considered. Pl. refer the said tables of the following articles.

10. https://doi.org/10.1186/s44147-022-00087-3 (Table 8 or 9 or 10)
11. https://doi.org/10.1007/s12008-022-00842-z[P1] (Table 6 or 12 or 17).

Answer 1: Many thanks for your comments. In the documents which you have stated, the response tables have developed to find the global optimal parametric combination for all methods considered. That is very necessary. However, with the dressing process for external grinding as stated in this study, the input parameters were investigated at all levels and they can be considered as global parameters. For example, for the coarse dressing process, the dressing depth levels are 0.03 and 0.04 (mm) and the levels of the dressing passes are 1, 2, 3, and 4. For the fine dressing process, the dressing depth levels are 0.005, 0.01, 0.0015, and 0.02 (mm) and the levels of the dressing passes are 0, 1, 2, and 3 (Table 1)... Therefore, the response table (Table 2 in the manuscript) also gives results that can be considered to find the global optimal parameters. As a result, please allow us to continue using the tables as they appear in the manuscript.

Table 1. Input factors.

No.	Input factors	symbol	unit	levels
				1	2	3	4
1	Coarse dressing depth	ar	mm	0.03	0.04	-	-
2	Coarse dressing passes	nr	-	1	2	3	4
3	Fine dressing depth	af	mm	0.005	0.01	0.015	0.02
4	Fine dressing passes	nf	times	0	1	2	3
5	Non-feeding dressing	n0	-	0	1	2	3
6	Dressing feed rate	Sd	m/min	1	1.5	-	-

Table 2. Experimental matrix and output results.

No.	Input parameters							Output results
	ar	nr	af	nf	n0	Sd	Ra (μm)		T (min)	Rn (μm)
1	0.025	1	0.005	0	0	1	0.3652		0.9186	0.0127
2	0.03	1	0.01	1	1	1	0.2137		1.0413	0.0087
3	0.025	1	0.015	2	2	1.2	0.1948		1.1215	0.0037
4	0.03	1	0.02	3	3	1.2	0.2417		1.1111	0.0050
5	0.03	2	0.005	1	2	1.2	0.1850		1.2459	0.0090
6	0.025	2	0.01	0	3	1.2	0.2477		1.2667	0.0090
7	0.03	2	0.015	3	0	1	0.2520		1.1782	0.0023
8	0.025	2	0.02	2	1	1	0.2167		1.2251	0.0043
9	0.03	3	0.005	2	3	1	0.3064		1.4878	0.0093
10	0.025	3	0.01	3	2	1	0.3239		1.3724	0.0057
11	0.03	3	0.015	0	1	1.2	0.3406		1.2709	0.0080
12	0.025	3	0.02	1	0	1.2	0.3541		1.1988	0.0050
13	0.025	4	0.005	3	1	1.2	0.3179		1.2273	0.0107
14	0.03	4	0.01	2	0	1.2	0.3126		1.2647	0.0110
15	0.025	4	0.015	1	3	1	0.3259		1.1247	0.0050
16	0.03	4	0.02	0	2	1	0.3634		1.1898	0.0080

Question 2: Survey of literature need inclusion of latest work. Inclusion of following articles may improve the quality of the manuscript.

10. https://doi.org/10.1007/s11831-022-09731-w
11. https://doi.org/10.31181/oresta190222046c

Answer 2: Many thanks for the comments. We have added those new documents to the Introduction section (refrences [35] and [36]).

35. Chakraborty, S. and S. Chakraborty, A scoping review on the applications of MCDM techniques for parametric optimization of machining processes. Archives of Computational Methods in Engineering, 2022: p. 1-22.
36. Chattopadhyay, R., P.P. Das, and S. Chakraborty, Development of a rough-MABAC-DoE-based metamodel for supplier selection in an iron and steel industry. Operational Research in Engineering Sciences: Theory and Applications, 2022. 5(1): p. 20-40.

We hope that these changes will meet your requirements.

Thank you for your consideration.

 

Sincerely,

 

 

The authors,

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors have incorporated most of the suggestions in the revised manuscript. However, the discussion on the importance of wheel dressing for grinding difficult-to-cut materials was missing. They should complete that part to augment the quality of the paper. They may use the following references for doing the same.

10.1504/IJMMM.2018.10013115, doi.org/10.1080/10426914.2018.1476759, 10.1080/10426914.2019.1615086, 10.1007/s42452-019-0616-z, 10.1016/j.matpr.2019.09.072, 10.1007/s12046-021-01732-y, 10.1038/s41598-022-05066-5, 10.36897/jme/141607, 10.1007/s00170-022-09499-0, 10.1080/10426914.2022.2039696, 10.1016/S0007-8506(07)63065-0 

Author Response

Dear reviewer,

We appreciate your careful consideration of this paper, as well as your detailed suggestions, which have been extremely helpful in improving the manuscript. We revised the manuscript in response to the comments. Our responses to each of your comments are listed below:

Question: The authors have incorporated most of the suggestions in the revised manuscript. However, the discussion on the importance of wheel dressing for grinding difficult-to-cut materials was missing. They should complete that part to augment the quality of the paper. They may use the following references for doing the same.

10.1504/IJMMM.2018.10013115, doi.org/10.1080/10426914.2018.1476759, 10.1080/10426914.2019.1615086, 10.1007/s42452-019-0616-z, 10.1016/j.matpr.2019.09.072, 10.1007/s12046-021-01732-y, 10.1038/s41598-022-05066-5, 10.36897/jme/141607, 10.1007/s00170-022-09499-0, 10.1080/10426914.2022.2039696, 10.1016/S0007-8506(07)63065-0 

Answer: Many thanks for your comments. These references have been added to the Introduction section as follows:

Difficult-to-cut materials are becoming more popular as materials engineering advances. As a result, many researchers are interested in grinding these materials. Manish Mukhopadhyay and Pranab Kumar Kundu [1] conducted an experimental study of grinding Ti-6Al-4V using alumina wheel. In their study, the simple and inexpensive RQL (restricted quantity lubrication) technique has been shown to be significantly efficient in expanding Ti-6Al-4V grindability than both dry and flood cooling. It also greatly reduces coolant consumption when compared to flood cooling. Manish Mukhopadhyay et al. [2] noted that using grinding fluid via the SQL (small quantity cooling and lubrication) technique results in better grinding results. In this study, alkaline soap water is discovered to be more efficient than conventional grinding fluid in the grinding of titanium alloy Ti-6Al-4V. Manish Mukhopadhyay and Pranab Kumar Kundu [3] introduced an economical and environmentally friendly drop by drop delivery technique for improving the grindability of Ti-6Al-4V. Effective use of the environment-friendly unconventional fluids when machining Ti-6Al-4V has also been reported when using alumina wheel [4, 5], and SiC wheel [6]. Berend Denkena et al. [7] investigated the wear-adaptive optimization of in-process conditioning factors during face plunge grinding of PcBN.  It was noted that the optimal input factors allow the process of inprocess conditioning to dramatically reduce grinding tool wear without increasing process time or non-productive time. Dung Hoang Tien et al. [8] presented the results of a multi-target  optimization study to find the optimum input factors for getting the minimum surface roughness and maximum material removal rate when external grinding SCM440 steel. When grinding GH4169 alloy with a ceramic bonded CBN grinding wheel, a multi-information fusion recognition model and experimental study of grinding wheel wear status were reported in [9]. G. Xiao and S. Malkin [10] conducted online-optimizing the internal grinding process and the dressing parameters to reduce production time while maintaining part quality requirements. The optimum process parameters when grinding Ti-6Al-4 V using a CBN grinding wheel was introduced in [11]. The Taguchi method and gray relational analysis (GRA) were applied to find the optimum dressing parameters when processing SKD11 tool steel in surface grinding to increase the materrial removal rate and to reduce the roundness tolerance of the ground parts [12].

 

 

We hope that these changes will meet your requirements.

Thank you for your consideration.

 

Sincerely,

 

References

 

1. Mukhopadhyay, M. and P.K. Kundu, Development of a simple and efficient delivery technique for grinding Ti-6Al-4V. International Journal of Machining and Machinability of Materials, 2018. 20(4): p. 345-357.
2. Mukhopadhyay, M., P.K. Kundu, and S. Das, Experimental investigation on enhancing grindability using alkaline-based fluid for grinding Ti-6Al-4V. Materials and Manufacturing Processes, 2018. 33(16): p. 1775-1781.
3. Mukhopadhyay, M. and P.K. Kundu, Improving grindability of Ti-6Al-4V using an economic and environmental friendly drop by drop delivery technique. Materials Today: Proceedings, 2020. 27: p. 2081-2085.
4. Mukhopadhyay, M. and P.K. Kundu, Evaluating application potentiality of unconventional fluids for grinding Ti-6Al-4V using alumina wheel. Materials and Manufacturing Processes, 2019. 34(10): p. 1151-1159.
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Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

The authors have done almost all the required changes. The paper may be accepted with the following minor changes.

1. Difficult-to-cut materials are required to go through the grinding process as per their application fields are concerned. However, while grinding these material, several challenges are observed due to the complexity of the grinding process. These challenges can be reduced by selecting appropriate process parameters and applying suitable cutting fluid through effecting delivery techniques. Additionally, proper dressing of the wheel also ensures reduction in these challenges during grinding operation.

L-66: Authors have mentioned that "Difficult-to-cut materials are becoming more popular as materials engineering" and started writing what literature says. Please write here a couple of words on the importance of grinding of difficult-to-cut materials, and state the challenges faced while the grinding is being operated on the difficult-to-cut materials. Then the novelty of your work ON THE DRESSING PARAMETER OPTIMIZATION will be highlighted in better way.

2. To increase the clarity of figure4, please use different markers for the different methods represented. It will also help in distinguishing the plots even on black & white printout.

3. Please make the reference styles uniform throughout the text and also in the list as per the journal's requirements. 

4. Place a nomenclature for the list of symbols & abbreviations used herein

Author Response

Dear reviewer,

We appreciate your careful consideration of this paper, as well as your detailed suggestions, which have been extremely helpful in improving the manuscript. We revised the manuscript in response to the comments. Our responses to each of your comments are listed below:

The authors have done almost all the required changes. The paper may be accepted with the following minor changes.

Question 1: Difficult-to-cut materials are required to go through the grinding process as per their application fields are concerned. However, while grinding these material, several challenges are observed due to the complexity of the grinding process. These challenges can be reduced by selecting appropriate process parameters and applying suitable cutting fluid through effecting delivery techniques. Additionally, proper dressing of the wheel also ensures reduction in these challenges during grinding operation.

L-66: Authors have mentioned that "Difficult-to-cut materials are becoming more popular as materials engineering" and started writing what literature says. Please write here a couple of words on the importance of grinding of difficult-to-cut materials, and state the challenges faced while the grinding is being operated on the difficult-to-cut materials. Then the novelty of your work ON THE DRESSING PARAMETER OPTIMIZATION will be highlighted in better way.

Answer 1: Many thanks for your comments. Several sentences have been added to the L-66 as follows:

According to their application fields, difficult-to-cut materials are required to go through the grinding process. However, due to the complexity of the grinding process, several challenges are encountered while grinding these materials. These difficulties can be mitigated by employing effective lubricating techniques and selecting appropriate process parameters. Furthermore, proper wheel dressing mode guarantees a reduction in these challenges during the grinding operation.

Question 2: To increase the clarity of Figure 4, please use different markers for the different methods represented. It will also help in distinguishing the plots even on black & white printout.

Answer 2: Thank you very much for the comments. Figure 4 has been replaced with a new one with different markers for the different methods represented.

Question 3: Please make the reference styles uniform throughout the text and also in the list as per the journal's requirements.

Answer 3: Thank you very much for the comments. We have done it.

Question 4: Place a nomenclature for the list of symbols & abbreviations used herein

Answer 4: Thank you very much for the comments. We have done the list of symbols & abbreviations as follows:

List of symbols & abbreviations:

MCDM - Multi-criteria decision making

MABAC - Multi-Attributive Border Approximation Area Comparison

SR - surface roughness

T - wheel life

R - roundness

MEREC - Method based on the Removal Effects of Criteria

TOPSIS - Technique for Order of Preference by Similarity to Ideal Solution

MARCOS - Measurement of Alternatives and Ranking according to COmpromise Solution

RQL - restricted quantity lubrication

EDM - electrical discharge machining

MRR - material removal rate

DoE - design of experiments

EAMR - Evaluation by an Area-based Method of Ranking

MAIRCA - Multi-Attributive Ideal–Real Comparative Analysis

a_r - coarse dressing depth

n_r - coarse dressing passes

a_r - fine dressing depth

n_f - fine dressing passes

n₀ - non-feeding dressing

S_d - dressing feed rate

 

We hope that these changes will meet your requirements.

Thank you for your consideration.

 

Sincerely,

Author Response File: Author Response.pdf