Optimizing Wood Composite Drilling with Artificial Neural Network and Response Surface Methodology

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The work submitted for review is 70% consistent with the materials already published by the authors of the article "Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood". Some sections of the article are completely identical. In general, it seems that the article "Predicting and optimizing the delamination factor and selected dynamic parameters during the drilling of wood-based composites by means of Artificial Neural Networks and Response Surface Methodology" submitted for review is a more expanded version of the previous work of the authors. In terms of formulas and figures, the coincidence is almost 100%, in terms of tabular data, there are some differences.

Author Response

Dear reviewer,

Thank you for your time to evaluate our work.

Please see the attachment.

On behalf of all authors,

Bogdan Bedelean

Faculty of Furniture Design and Wood Engineering,

Transilvania University of Brasov, Romania

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors, and Editors

The reviewed study entitled “Predicting and optimizing the delamination factor and selected dynamic parameters during the drilling of wood-based composites by means of Artificial Neural Networks and Response Surface Methodology” aimed to optimize the drilling process of wood-based composites by analyzing the impact of various factors on drilling efficiency. After reading the manuscript, I offer comments for the authors’ consideration:

1.       The title conveys the main topic of the research, but I consider it too lengthy. At least some words like “during” or “by means of” are redundant. However, please consider shortening the tile more. Will “Predicting Delamination based on Drilling Dynamics in Wood Composites” be sufficient? (or “Optimizing Wood Composite Drilling with ANN and RSM”?).

2.       The abstract is well-written and provides an overview of the research, but the first sentence is a bit disorderly. The authors wrote, “Many factors (like drill bit diameter, spindle rotation speed, tip angle of the drill bit, feed rate, type of the drill, material properties, machine-tool characteristics) could affect the efficiency of drilling process, which could be quantified through delamination factor, trust force and drilling torque”. Please put in order the factors listed in brackets. I suggest order: "material properties, drill bit type and size, drill bit wear, drilling parameters used and machine-tool characteristics". Additionally, the word “could” is redundant (please delete) and consider adding “the” before “drilling process”. (“Many factors (like material properties, drill bit type and size, drill bit wear, drilling parameters used and machine-tool characteristics) affect the efficiency of the drilling process, which could be quantified through delamination factor, trust force, and drilling torque”).

3.       The literature review in the “introduction” section seems incomplete. The authors omitted some specific material aspects of efficiency during drilling holes in wood-based materials studied (plywood, MDF). For example, in the study from 2020 (doi: 10.15376/biores.15.1.117-129), the authors revealed that drilling holes through the adhesive layer of plywood resulted in significantly less accurate dimensions compared to holes drilled through the veneer layers. Additionally, the dimensional inaccuracy of holes affects the strength of wood-made products. In the study from 2024 (doi: 10.1007/s00107-023-01972-1), the authors found that the feed rate values used during drilling pilot holes significantly affects the withdrawal force of screws

4.       The authors wrote in line “. The neural network system predicted tool wear for all tools except the first one, using data from previous trials”. Please consider writing, “The neural network system used data from previous trials to predict tool wear for all tools except the first one”.

5.       The study aim is missing at the end of the introduction section. Please add a clear statement with the aim. For example, please consider writing “The primary aim of the study was to optimize the drilling process of wood-based composites by:1) Predicting the delamination factor, thrust force, and drilling torque; 2) Identifying the optimal combination of factors (drill bit type, drill bit diameter, spindle rotation speed, tip angle, feed rate, and material properties) that influence these parameters. The study employed artificial neural networks and response surface methodology to achieve these goals.”

6.       The authors use the term “tooth bite”. I consider it as incorrect. It is probably “feed value per revolution of the every cutting edge” (f_z). Please consider naming it “chipload” and please revise the whole manuscript. The authors wrote “The value of the feed speed was (v_f) = 0.6, 1.8, 3.0 and 4.2 m/min (as the rotation speed was the same for all specimens, n=3000 rot/min, the tooth bite (f_z) had the values of 0.1, 0.3, 0.5 and 0.7 mm)”. The right unit of f_z is mm/rev., not “mm” (f_r = v_f/n; if v_f is in mm/min and n in rev./min, the f_r is in mm/rev; f_z is a ½ f_z and is also in mm/rev.). The authos should wrote: "The values of the feed speed (v_f) were 0.6, 1.8, 3.0 and 4.2 m/min, the rotation speed was the same for all specimens, n = 3000 rev./min, the chipload (f_z) had the values of 0.1, 0.3, 0.5 and 0.7 mm/rev.)".

7.       The authors used the atypical term “helical drill”. Please consider changing it into “twist drill”. Please revise the whole manuscript.

8.       The authors used another atypical term “flat drill”. However, the spade bits are flat, but please consider changing “flat drill” to “spade drill”. Please revise the whole manuscript.

9.       In the figure 2 caption the authors wrote “wood particleboard“. Please write “particleboard”.

10.   Please ensure that the variables, units, and numbers are written correctly and consistently. All variables should be presented in italics, while unit abbreviations should be written in plain text. Always maintain a space between the numerical value and the unit abbreviation. The space is also needed between mathematila symbols (f.e. equal sign) and number. Please revise the whole manusctript.

11.   Key findings from the study can be provided in points for better readability. I propose:

• Artificial neural networks (ANNs) and response surface methodology (RSM) were successfully applied to predict drilling parameters to optimize the delamination factor, thrust force, and drilling torque in particleboard, MDF, and plywood. Compared to experimental data, both the ANN and RSM models demonstrated reasonable accuracy, as evidenced by their high coefficient of determination (R2), which indicates their effectiveness in revealing the individual influence of factors on the drilling process.
• Material type was found to significantly impact the delamination factor.
• Drill type (twist vs. spade) primarily influenced thrust force.
• The chipload was identified as the most critical factor affecting drilling torque.

The proposed method can be used as an effective optimization tool for drilling wood and wood-based composites. Further research could explore additional factors influencing the drilling process and integrate online monitoring techniques for real-time process optimization.”

While subject to the aforementioned comments, the manuscript offers valuable insights into the drilling process of wood-based composites and contributes to a deeper understanding of the field.

Sincerely

(–)

Author Response

Dear reviewer,

Thank you for your time to evaluate our work.

Please see the attachment.

On behalf of all authors,

Bogdan Bedelean

Faculty of Furniture Design and Wood Engineering,

Transilvania University of Brasov, Romania

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

 

Review of the article: “Predicting and optimizing the delamination factor and selected

dynamic parameters during the drilling of wood-based composites by means of Artificial Neural Networks and Response Surface Methodology”

 

 

               The topic of this research and output might be useful for the Forests readers. The article is very interesting and presents a rarely discussed but valuable topic regarding the predicting and optimizing the delamination factor and selected dynamic parameters during the drilling of wood-based composites. 

 

Please follow the rule of giving the term first and then its abbreviation.

 

Abstract

§  The abstract is very general. The identified relationships should be presented by providing numerical data. This is a more interesting form of presenting results than in a descriptive way. This will increase the citation of the article.

Line 15: …we applied artificial neural network…

§  Personal forms should be avoided and the passive voice should be used. The entire article should be analyzed in this respect.

 

Introduction

Line 46: In their 2021 study, Agarwal and Mishra [6]…

§  Should be Agarwal and Mishra [6]…

 

Materials and Methods

Line 103: Please provide the manufacturer of the drill bits.

 

Lines 110 – 112: Please describe the boards precisely:

§  what was their thickness,

§  what was their density,

§  whether they were single or multi-layer boards (particleboards),

§  how many layers was the plywood made of and what veneers (thickness, type of wood, type of resin used).

 

Line 120: Please provide the manufacturer (city, country) of the CNC machine.

 

Results and Discussion

A valuable part of the work is the statistical analysis of research results. When analyzing in a broader context, it is necessary to refer to the current literature on the subject and research by other authors.

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Dear reviewer,

Thank you for your time to evaluate our work.

Please see the attachment.

On behalf of all authors,

Bogdan Bedelean

Faculty of Furniture Design and Wood Engineering,

Transilvania University of Brasov, Romania

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

In this work, the Authors used an artificial neural network and response surface methodology to predict and optimize the delamination factor at the inlet and outlet, thrust force and drilling torque during the drilling of prelaminated wood-based composites (particleboards, MDF and plywood). The topic is very interesting both from a scientific and practical point of view. Authors need to make additions to the manuscript.

1. The term "Wood-based composite" should be added to the keywords.

2. The Introduction section correctly presents the state of the art.

3. There is a lack of properties of wood-based composites used in experiments.

4. Authors could consider adding the mathematical formulas to the curves in Figure 6 (not only the R2 coefficient).

5. Table 2 is too big. Consider dividing it into 3 smaller (e.g. each per one wood-based composite).

6. The first 2 sentences in the Conclusions section are not necessary.

Author Response

Dear reviewer,

Thank you for your time to evaluate our work.

Please see the attachment.

On behalf of all authors,

Bogdan Bedelean

Faculty of Furniture Design and Wood Engineering,

Transilvania University of Brasov, Romania

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors should rewrite the part of the material that completely duplicates the previous article, as well as replace all the drawings that have already been published earlier.

Author Response

Dear Reviewer,

Please find attached the updated manuscript.

Best regards,

Bogdan Bedelean

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

Thank you for considering all my comments from the first round of review. I especially appreciate the new concise article title, 'Optimizing Wood Composite Drilling with ANN and RSM.' While 'ANN' and 'RSM' are widely known abbreviations in this field, I would recommend using their expanded forms (Artificial Neural Network and Response Surface Methodology) in the abstract or keywords for broader accessibility (and beter article "search-ability") Please use in the "TAK section" (Title, Abstrac, Keywords) at least once of both forms of these terms (abbreviated and extended "ANN" and "RSM"). 

Author Response

Dear Reviewer,

Please find attached the updated manuscript.

Best regards,

Bogdan Bedelean

Author Response File: Author Response.pdf