A Study on the Beech Wood Machining Parameters Optimization Using Response Surface Methodology

Round 1

Reviewer 1 Report

This research presents a study on optimization of machining parameters for beech wood via RSM with focus on the surface quality of the machined beech wood. The optimal machining parameters have been obtained to reduce the surface roughness and to achieve the best quality of the machining surface. It is also noted that the feed rate has the greatest effect on the surface roughness.

 

·       Introduction section is clear enough as the main contributions of the work are optimization and surface quality. The subsections for the wood itself and the experimental design are satisfying. References are also up to date, and the gap existing in the previously-published literature for the subject shows the novelty of the present research work.

·       Figures are clear and readable.

 

·       Results are supported/justified scientifically, and the quality of presentation is acceptable.

Author Response

The authors would like to appreciate the reviewer for the good comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

In this paper, response surface methodology (RSM) has been used to design experiments. The experiments design has been performed using the central composite model in order to investigate the effect of different machining parameters as feed rate, spindle speed, step over, and depth of cut on surface quality of the beech wood. The mathematical model of the examined parameters and the surface roughness has also been obtained by the method. Finally, the optimal machining parameters have been obtained to reduce the surface roughness and to achieve the best quality of the machining surface. It is also noted that the feed rate has the greatest effect on the surface roughness. This manuscript is well organized and covers timely subject in the field. This manuscript could be accepted after incorporation of comprehensive proofreading to rectify the typo/grammatical errors. 

Author Response

The authors would like to appreciate the reviewer for the good comments and suggestions. According to the point, the authors proofread the paper and the grammar of the paper has also been rechecked.

Author Response File: Author Response.pdf

Reviewer 3 Report

1. Abstract should be given as more interesting. Express at least one of the main aspects and features of the paper.
2. Research gap studies are uncleared.
3. Response surface methodology has been adopted in several research works also. Authors are invited to cite recent published works:
4. There is no scientific justification for the selection of cutting parameters considered.
5. How many times was the surface roughness measured in each workpiece?
6. Include the photograph of surface roughness measurement setup.
7. The discussion related to various diagnostic tests are essential for the validation of the proposed RSM model.
8. Further, results and analysis of experiments should be compared with previous researchers by citing references.
9. Provide the information of RSM optimization results by RAMP function plot and bar plot.
10. Improve the conclusion with scope for future work.
11. Manuscript must be presented in highlighting the contribution, and applicability of the work.
12. Please check the manuscript for wrong choice of words, grammatical errors and incoherent sentence structure.

Author Response

Response to Reviewer 3 Comments

Point 1: Abstract should be given as more interesting. Express at least one of the main aspects and features of the paper.

Response 1: Abstract is modified to provide one of the main aspects and features of the paper, which are machining parameters optimization for the best surface quality of the beech wood, based on the suggestion of Reviewer 3.

 

Point 2:  Research gap studies are uncleared.

Response 2: Beech wood has common and vast use in wood production industries and the physical and mechanical properties of this type of wood family have been studied in recently-published literature [20-22 of paper]. However, as the machining of woods is of importance in production industry, and in the previously-published literature there is a gap for not only the optimal machining parameters and the best surface quality for the beech wood, but the influence of different effective machining parameters on the finished surface of this type of wood have not been studied as well. Therefore, this papers aims to fill this gap by envestigating the effects of feed rate, spindle speed, step over, and depth of cut on surface quality of the beech wood, and the study mainly focuses on parameters investigation and optimization to achieve the best surface quality.

20. Skarvelis, M., Mantanis, G.I., Physical and mechanical properties of beech wood harvested in the Greek public forests, Wood Research, 58 (1), 2013.
21. Najafian Ashrafi, Shaabani Asrami, H., Vosoughi Rudgar, Z., Ghorbanian Far, M., Heidari, A., Rastbod, E., Jafarzadeh, H., Salehi, M., Bari, E., Ribera, J. Comparison of Physical and Mechanical Properties of Beech and Walnut Wood from Iran and Georgian Beech, Forests, 12, pp. 123-129, 801-809, 2021.
22. Purba, C.Y.C., Dlouha, J., Ruelle, J., Fournier, M. Mechanical properties of secondary quality beech (Fagus sylvatica L.) and oak (Quercus petraea (Matt.) Liebl.) obtained from thinning, and their relationship to structural parameters, Annals of Forest Science, 78 (81), pp. 1-11, 2021.

 

Point 3: Response surface methodology has been adopted in several research works also. Authors are invited to cite recent published works:

Response 3: The text has been modified and most recent research works related to use of RSM in the field have been cited [29-38 of the paper] based on the comment of Reviewer 3.

29. Bakhaidar, R. B., Naveen, N. R., Basim, P., Murshid, S. S., Kurakula, M., Alamoudi, A. J., Bukhary, D. M., Jali, A. M., Majrashi, M. A., Alshehri, S., Alissa, M., Ahmed, R. A. Response Surface Methodology (RSM) Powered Formulation Development, Opti-mization and Evaluation of Thiolated Based Mucoadhesive Nanocrystals for Local Delivery of Simvastatin, Polymers, 14, 5184, 2022.
30. Gutema, E. M., Gopal, M., Lemu, H. G. Minimization of surface roughness and temperature during turning of Aluminum 6061 using Response Surface Methodology and Desirability Function Analysis, Materials, 15, pp. 7638, 2022.
31. Chen, C.-P., Su, H.-Z., Shih, J.-K., Huang, C.-F., Ku, H.-Y., Chan, C.-W., Li, T.-T., Fuh, Y.-K. A Comparison and Analysis of Three Methods of Aluminum Crown Forgings in Processing Optimization, Materials, 15, pp. 8400, 2022.
32. Oniszczuk-Swiercz, D., Swiercz, R., Michna, S. Evaluation of Prediction Models of the Microwire EDM Process of Inconel 718 Using ANN and RSM Methods, Materials, 15, pp. 8317, 2022.
33. Han Kang, H., Liu, Y., Li, D., Xu, L. Study on the Removal of Iron and Manganese from Groundwater Using Modified Man-ganese Sand Based on Response Surface Methodology, Appl. Sci. 12, pp. 11798, 2022.
34. Najiyah Safwa Khashi’ie, N. S., Waini, I., Mukhtar, M. F., Zainal, N. A., Hamzah, K. B., Arifin, N. M., Pop, I. Response Surface Methodology (RSM) on the Hybrid Nanofluid Flow Subject to a Vertical and Permeable Wedge, Nanomaterials, 12, pp. 4016, 2022.
35. Equbal, A., Equbal, M. A., Equbal, M. I., Ravindrannair, P., Khan, Z. A., Badruddin, I. A., Kamangar, S., Tirth, V., Javed, S., Kittur, M. I. Evaluating CNC Milling Performance for Machining AISI 316 Stainless Steel with Carbide Cutting Tool Insert, Materials, 15, pp. 8051, 2022.
36. Majed O. Alawad, M. O., Alateyah, A. I., El-Garaihy, W. H., BaQais, A., Elkatatny, S., Kouta, H. Kamel, M., El-Sanabary, S. Optimizing the ECAP Parameters of Biodegradable Mg-Zn-Zr Alloy Based on Experimental, Mathematical Empirical, and Response Surface Methodology, Materials, 15, pp. 7719, 2022.
37. Yanis, M., Mohruni, A. S., Sharif, S., Yani, I., Arifin, A., Khona'ah, B. Application of RSM and ANN in Predicting Surface Roughness for Side Milling Process under Environmentally Friendly Cutting Fluid, J. Phys.: Conf. Ser., 1198, pp. 042016, 2019.
38. Abderrahmen Zerti, A., Yallese, M. A., Khettabi, R. Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420, Proc. Inst. Mech. Eng. Pt. C J. Mechan. Eng., 223, pp. 4439-4462, 2019.

 

Point 4: There is no scientific justification for the selection of cutting parameters considered.

Response 4: Machining parameters that can be varied during the milling operation include geometric factors, machine tool factors and work piece material [24 of the paper]. By using appropriate machining parameters, required surface finish can be attained. However, in a milling condition with certain machine tool factors and work piece material, surface finish can be adjusted, improved and predicted depending upon the selection of geometric factors which includes cutting speed, feed per tooth, depth of cut, width of cut [25-27 of the paper]. In the current study, the four effective machining parameters on surface quality when milling have been selected which are feed rate, cutting speed, step over, and depth of cut.

24. Groover, M., P. Fundamentals of Modern Manufacturing Materials, Processes, and Systems, Fourth Edition, 2010.
25. Sharma, A., Dwivedi, V.K. Effect of milling parameters on surface roughness: An experimental investigation, Mater. Today Proc., 25, pp. 868–871, 2019.
26. Premnath, A. A., Alwarsamy, T., Abhinav, T., Krishnakant, C. A. Surface roughness prediction by response surface methodology in milling of hybrid aluminium composites, Procedia Eng., 38, pp. 745–752, 2012.
27. Sanjeevi, R., Nagaraja, R., Radha Krishnan, B. Vision-based surface roughness accuracy prediction in the CNC milling process (Al6061) using ANN, Mater. Today Proc., 37, pp. 245-247, 2021.

The text has also been improved as:

“Work piece material, machine tool type, and geometric factors may be varied during machining [24]. Required surface quality can be attained by proper machining parameters selection. Here, in a milling condition with the given factors for the machine tool and work piece material, surface quality can be determined and improved depending upon the geometric factors’ selection which includes feed rate, cutting speed, step over, and depth of cut [25-27]. The effective variables considered on the surface roughness method and their minimum and maximum values have been presented in Table 2.”

 

Point 5: How many times was the surface roughness measured in each workpiece?

Response 5: Surface roughness is measured by tracing the probe across the surface of the workpiece. Ra is a vertical parameter and shows the average roughness of a surface. There are different standards for measuring Ra. In the current study the authors have measured each surface in five sampling lengths. ISO 4287 standard [28 of the paper]

28. ISO/TC 213 Dimensional and geometrical product specifications and verification, ISO 4287:1997 Geometrical Product Specifications (GPS) — Surface texture: Profile method — Terms, definitions and surface texture parameters, pp. 1-25, 1997.

The text has also been improved as:

“Surface roughness can be measured by tracing the probe across the workpiece surface. The arithmetical mean of the absolute values of the profile deviations, Ra, is a vertical parameter and shows the average roughness of a surface. After performing 24 designed tests, the average roughness parameter (Ra) has been measured using TIME 3202 digital roughness meter according to ISO 4287 standard [28] which uses five sampling lengths for Ra measurement. Figure 1 shows the machining process and average surface roughness measurement.”

 

Point 6: Include the photograph of surface roughness measurement setup.

Response 6: The photograph of machining process and surface roughness measurement setup have been included in the revised version of the paper based on the suggestion of Reviewer 3.

 

(a)		(b)

Figure 1. (a) CNC machining of beech wood; (b) surface roughness measurement.

 

Point 7: The discussion related to various diagnostic tests are essential for the validation of the proposed RSM model.

Response 7: To validate the proposed model, the estimated surface roughness value and the value obtained from the model measurement have been compared. Table 7 of the paper shows the estimated and measured values in different model conditions. According to this table, it can be seen that the measured and estimated values are equal to each other or have a slight difference with each other. Therefore, the estimated model has enough accuracy to calculate surface roughness based on different machining parameters (feed rate, spindle speed, depth of cut, step over).

 

Point 8: Further, results and analysis of experiments should be compared with previous researchers by citing references.

Response 8: The effect of different machining parameters on surface roughness of other work-piece materials have been studied in several research works. In the literature [39-42 of the paper], it was reported that the control parameters having the most effect on surface quality are the spindle speed, feed rate and depth of cut rate and better surface quality was obtained at higher spindle speeds, lower feed rates and depth of cut. Here, in this study the similar results have been obtained for the effect of spindle speed, feed rate, depth of cut on surface quality of the beech wood. Particularly the step over effect on the surface roughness has been investigated in this study.

39. Ghazali, M. H. M., Mazlan, A. Z. A., Wei1, L. M., Tying, C. T., Sze, T. S., Jamil, N. I. M. Effect of Machining Parameters on the Surface Roughness for Different Type of Materials, IOP Conf. Ser.: Mater. Sci. Eng., 530, pp. 012008, 2019.
40. Zaidi, S. R., Khan, M., Jaffery, S. H. I., Warsi, S. Effect of Machining Parameters on Surface Roughness During Milling Operation, in Book: Advances in Manufacturing Technology, DOI:10.3233/ATDE210033, 2021.
41. Sharma, A., Dwivedi, V. K. Effect of milling parameters on surface roughness: An experimental investigation, Materials Today: Proceedings, 25, pp. 868-871, 2020.
42. Zhenchao, Y., Yang, X., Yan, L., Jin, X., Quandai, W. The effect of milling parameters on surface integrity in high-speed milling of ultrahigh strength steel, Procedia CIRP, 71, pp. 83-88, 2018.

 

Point 9: Provide the information of RSM optimization results by RAMP function plot and bar plot.

Response 9: The results for RSM have been provided as RAMP function plot and bar plot based on the suggestion of Reviewer 3.

Figure 4. Optimal parameter ramps function graphs and combined optimization.

Figure 5. Desirability bar graph for combined optimization.

 

Point 10: Improve the conclusion with scope for future work.

Response 10: In order to follow the comment of Reviewer 3, the following explanation is added to the end of the conclusion Section of the revised paper as follows:

“The results of the proposed model for the estimated surface roughness value evalu-ated by the value obtained from the model measurement. The measured and estimated values are equal to each other or have a slight difference. Finally, it can be concluded that the model has a good accuracy to predict surface roughness based on different machining parameters. As RSM allows investigating the influences of multiple factors and their in-teractions on one or more response variables, for future works this method can be applied on other factors influential on surface quality, even can be employed to investigate the ef-fects of the mentioned parameters on other response variables such as tool wear. This benefits a high precision machining and high-quality wooden products. The study can also be continued on other wood types to study the product cost and quality. This, moreover, clearly shows the applicability and significancy of the method in other studies in terms of economical cost, time, and any other limitations.”

 

Point 11: Manuscript must be presented in highlighting the contribution, and applicability of the work.

Response 11:

The main contribution of the current study is machining parameters investigation and optimization to achieve the best surface finish for the machined beech wood:

• The effects of feed rate, spindle speed, step over, and depth of cut on surface quality of the beech wood have been presented.
• The optimal machining parameters and the best surface quality for the machined beech wood have been envestigated under RSM.

The text has been modified to better highlight the contribution and applicability of the current work based on the suggestion of Reviewer 3.

 

Point 12: Please check the manuscript for wrong choice of words, grammatical errors and incoherent sentence structure.

Response 12: The current version of the paper has been thoroughly proofread by a native English speaker.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Authors have made significant changes in the revised manuscript. Hence, consider the manuscript for publication in its present form.