Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network
Round 1
Reviewer 1 Report
The article is very interesting and deals with current research topics. The results are important and useful to other scientists. The article after the corrections can be printed.
Authors should read the detailed notes.
First of all, the authors must refer to the works of other authors in the analysis of the results and the literature review in the introduction should be extended to include research by scientists other than only from Asia.
Detailed comments and suggestions for extending the work.
“Timely replacement of worn tools can improve product quality and reduce production costs [6].”
The impact of the blade wear of the woodworking tool was also shown by:
Spinelli, Raffaele, Sotir Glushkov, and Ivailo Markov. "Managing chipper knife wear to increase chip quality and reduce chipping cost." Biomass and Bioenergy 62 (2014): 117-122.
Facello, Alessio, et al. "The effect of knife wear on chip quality and processing cost of chestnut and locust fuel wood." Biomass and Bioenergy 59 (2013): 468-476.
„The raw materials of furniture have the properties of anisotropy and porosity [7-9]…”
The anisotropic properties of wood are also described in the following work:
Warguła, Ł.; Wojtkowiak, D.; Kukla, M.; Talaśka, K. Symmetric Nature of Stress Distribution in the Elastic-Plastic Range of Pinus L. Pine Wood Samples Determined Experimentally and Using the Finite Element Method (FEM). Symmetry 2021, 13, 39. https://doi.org/10.3390/sym13010039
„Therefore, the monitoring signals collected during the cutting process of woodworking tools contain various interference[10]..”
Cutting force signals and their analysis were also presented in the research:
- for wood-based materials: Warguła, Ł., & Kukla, M. (2020). Determination of maximum torque during carpentry waste comminution. Wood Res, 65, 771-784.
- for wood: https://doi.org/10.1051/matecconf/201815701021, doi:10.1088/1757-899X/776/1/012012., https://doi.org/10.3390/en13246709.
“The monitoring method of tool wear conditions is mainly based on the cutting force signal [11].”
Authors should extend the issue of tool wear monitoring.
Saglam, H. (2011). Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments. The International Journal of Advanced Manufacturing Technology, 55(9-12), 969-982.
Lee, Daeul. "On-machine measurement technique for dicing blade wear monitoring." Proceedings of Student Research and Creative Inquiry Day 1 (2017).
Wang, Y., Jia, X., Li, X., Yang, S., Zhao, H., & Lee, J. (2020). A machine vision based monitoring system for the LCD panel cutting wheel degradation. Procedia Manufacturing, 48, 49-53.
Jaworski, J., Kluz, R., & Trzepieciński, T. (2016). Operational tests of wear dynamics of drills made of low-alloy high-speed HS2-5-1 steel. Eksploatacja i Niezawodność, 18(2).
Dou, Jianming, et al. "An unsupervised online monitoring method for tool wear using a sparse auto-encoder." The International Journal of Advanced Manufacturing Technology 106.5 (2020): 2493-2507.
Each scientific work in its analysis of the results should refer to the work of other authors. It is a mandatory and necessary element.
Author Response
Response to Reviewer 1 Comments
Dear Editors and Reviewer:
Thank you for your letter and for the reviewers' comments concerning our manuscript entitled “A Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and Particle Swarm Optimization-Back Propagation Neural Network” (ID: applsci-1384405). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied the comments carefully and have made correction which we hope meet with approval. Revised portion are marked in yellow in the manuscript, and the main corrections in the paper and the responds to the reviewers’ comments are as flowing:
The article is very interesting and deals with current research topics. The results are important and useful to other scientists. The article after the corrections can be printed.
Authors should read the detailed notes.
First of all, the authors must refer to the works of other authors in the analysis of the results and the literature review in the introduction should be extended to include research by scientists other than only from Asia.
Detailed comments and suggestions for extending the work.
Q1: Timely replacement of worn tools can improve product quality and reduce production costs [6].”
The impact of the blade wear of the woodworking tool was also shown by:
Spinelli, Raffaele, Sotir Glushkov, and Ivailo Markov. "Managing chipper knife wear to increase chip quality and reduce chipping cost." Biomass and Bioenergy 62 (2014): 117-122.
Facello, Alessio, et al. "The effect of knife wear on chip quality and processing cost of chestnut and locust fuel wood." Biomass and Bioenergy 59 (2013): 468-476.
R: Many thanks for your valuable suggestion. As you suggested, the literature review has been extended, and the related references have been cited as shown in L31, 351-354.
Q: The raw materials of furniture have the properties of anisotropy and porosity [7-9]…”
The anisotropic properties of wood are also described in the following work:
Warguła, Ł.; Wojtkowiak, D.; Kukla, M.; Talaśka, K. Symmetric Nature of Stress Distribution in the Elastic-Plastic Range of Pinus L. Pine Wood Samples Determined Experimentally and Using the Finite Element Method (FEM). Symmetry 2021, 13, 39. https://doi.org/10.3390/sym13010039
R: Based on your comments, those information has been supplemented as shown in L42, 359-361.
Q3: Therefore, the monitoring signals collected during the cutting process of woodworking tools contain various interference[10]..”
Cutting force signals and their analysis were also presented in the research: for wood-based materials: Warguła, Ł., & Kukla, M. (2020). Determination of maximum torque during carpentry waste comminution. Wood Res, 65, 771-784. for wood: https://doi.org/10.1051/matecconf/201815701021, doi:10.1088/1757-899X/776/1/012012., https://doi.org/10.3390/en13246709.
R: Yes, they all have been cited as shown in L46, 364-365.
Q4: “The monitoring method of tool wear conditions is mainly based on the cutting force signal [11].”
Authors should extend the issue of tool wear monitoring.
Saglam, H. (2011). Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments. The International Journal of Advanced Manufacturing Technology, 55(9-12), 969-982.
Lee, Daeul. "On-machine measurement technique for dicing blade wear monitoring." Proceedings of Student Research and Creative Inquiry Day 1 (2017).
Wang, Y., Jia, X., Li, X., Yang, S., Zhao, H., & Lee, J. (2020). A machine vision based monitoring system for the LCD panel cutting wheel degradation. Procedia Manufacturing, 48, 49-53.
Jaworski, J., Kluz, R., & Trzepieciński, T. (2016). Operational tests of wear dynamics of drills made of low-alloy high-speed HS2-5-1 steel. Eksploatacja i Niezawodność, 18(2).
Dou, Jianming, et al. "An unsupervised online monitoring method for tool wear using a sparse auto-encoder." The International Journal of Advanced Manufacturing Technology 106.5 (2020): 2493-2507.
R: Those related references have been cited as you suggested, L49, 366-371.
Q5: Each scientific work in its analysis of the results should refer to the work of other authors. It is a mandatory and necessary element.
R: Many thanks for your valuable suggestion. Both in sections of “Results and Discussion” and “Conclusions”, related work has been referred and compared, especially the GA-BP neural network algorithm (Dong et al. “Discrete Wavelet Transformation and Genetic Algorithm - Back Propagation Neural Network Applied in Monitoring Woodworking Tool Wear Conditions in the Milling Operation Spindle Power Signals” and Zhang et al. “Identification of Tool Wear Condition Based on Generalized Fractal Dimensions and BP Neural Network Optimized with Genetic Algorithm”.) and PSO-BP neural network algorithm, as shown in L280-291(green), 307-310.
We truly appreciate your work, and hope that our corrections will meet with your approval. Once again, thank you very much for your comments and suggestions.
Yours sincerely
Weihang Dong and Xianqing Xiong
09/13/2021
Reviewer 2 Report
Dear authors,
please consider my suggestions:
I suggest not use abbreviations in the title please consider changing it.
I suggest improving the Introduction part, it is very general. Please focus more deeply on the topic of your manuscript. In the last two decades, there is plenty of published material in this area.
2.1 this method is well known, no need to explain it so deeply.
Line 71: inhomogeneity vs anisotropy? what about other properties?
LIne 134: You have chosen WPC, why? No discussion about WPC cutting in the introduction. Information about WPC completely missing. MC?
Table 1: measured or from supplier, please?
The tool was used or completely new?
You should start with Materials followed by tools and methods.
Results and discussion: there is a completely missing discussion with previous research. What about the novelty of your research? Limitations? Implications for practice?
There are some other minor issues, can be solved after major issues.
Author Response
Response to Reviewer 2 Comments
Dear Editors and Reviewer:
Thank you for your letter and for the reviewers' comments concerning our manuscript entitled “A Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and Particle Swarm Optimization - Back Propagation Neural Network” (ID: applsci-1384405). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied the comments carefully and have made correction which we hope meet with approval. Revised portion are marked in green in the manuscript, and the main corrections in the paper and the responds to the reviewers’ comments are as flowing:
Q1: I suggest not use abbreviations in the title please consider changing it.
.
R: Yes, thanks, the abbreviations in the title has been revised as you suggested (L2-3).
Q2: I suggest improving the Introduction part, it is very general. Please focus more deeply on the topic of your manuscript. In the last two decades, there is plenty of published material in this area.
R: Many thanks for your valuable suggestion. Based on your comments, the introduction has been improved as shown in L33-34, 35-45, 49-52, 55-59, 63-76, 81-84.
Q3: 2.1 this method is well known, no need to explain it so deeply.
R: Yes, as you suggested, the explanation of the method has been simplified as shown in L127-128.
Q4: Line 71: inhomogeneity vs anisotropy? what about other properties?
R: It should be inhomogeneity. The test material used in this paper is WPC. WPC is made of wood powder (fiber), which is an inhomogeneity material. Those text has been supplemented as shown in L42. In addition, other properties of WPC are listed in Table 1.
Q5: Line 134: You have chosen WPC, why? No discussion about WPC cutting in the introduction. Information about WPC completely missing. MC?
R: WPC is composite materials made of wood powder and thermoplastics, which is widely used in furniture manufacturing, such as decorative panels, railings, cladding, wall panels, windows and door frames, etc. WPC has extremely high corrosion resistance and low manufacturing cost, which is a recyclable environmentally friendly green material. With superior mechanical properties, the utilization rate and market share of WPC are gradually increasing. Therefore, WPC was selected as the test material. Those information about WPC in the “introduction” have been supplemented as shown in L35-45. Furthermore, MC of WPC was supplemented in Table 1.
Q6: Table 1: measured or from supplier, please?
Response 6: Workpiece material chemical composition and mechanical properties are from supplier, namely Kolo Material Co., Ltd. (L100)
Q7: The tool was used or completely new?
R: In this work, the effect of tool wear on cutting power was in focus. Thus, before testing, the tool is completely new. However, in order to ensure the cutting tools have different tool wear conditions, each tool was used to cut the WPC for a period of time before testing.
Q8: You should start with Materials followed by tools and methods.
R: Yes, the structure of this manuscript has been changed as you suggested. Section 2 (methods) and Section 3 (materials and tools) have been adjusted. Thank you very much.
Q9: Results and discussion: there is a completely missing discussion with previous research. What about the novelty of your research? Limitations? Implications for practice?
R: Many thanks for your valuable suggestion. As you suggested, related discussion has been added in the manuscript (L279-295), It contains the discussion with previous research (L285-291) and the novelty of research (L280-284). Meanwhile, the limitations of the proposed method (L314-317) and future research directions (L317-319) was focused in the conclusion.
Q10: There are some other minor issues, can be solved after major issues.
R: Many thanks for your valuable suggestion. Those issues have been modified as follows:
(1) “Conclusions” has been modified to make them more reasonable (L299-319).
(2) Some descriptions about comparison has been supplemented in the manuscript to make it more emphasis with the previous research (L250-251,273-277).
(3) We have revised the titles of some sections (Section 4 L182, Section 4.3 L245).
We truly appreciate your work, and hope that our corrections will meet with your approval. Once again, thank you very much for your comments and suggestions.
Yours sincerely
Weihang Dong and Xianqing Xiong
09/13/2021
Round 2
Reviewer 1 Report
The article has been corrected, I recommend it for printing
Reviewer 2 Report
The manuscript was significantly improved.
