Multi-Objective Optimization of Torque Motor Structural Parameters in Direct-Drive Valves Based on Genetic Algorithm

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

There are some concerns must be fully addressed as follows:

1. The introduction should be significantly improved by incorporating recent and relevant studies in the field. Currently, it lists numerous references without providing sufficient context or critical discussion. A more analytical approach is needed to highlight gaps in existing research and justify the proposed study.
2. The implementation of the genetic algorithm is not sufficiently explained. Section 4.1 lacks clarity and depth, particularly in terms of algorithmic structure and operational flow. Including detailed flowcharts and referencing standard GA procedures would enhance understanding and reproducibility.
3. The influence of the optimization process on valve performance is presented in a general manner, primarily through plots. However, the section lacks analytical depth and supporting equations. A more rigorous analysis, including mathematical modeling or performance metrics, is recommended.
4. The experimental results section does not fully address the scope of the study. It should be expanded to cover a broader range of operational scenarios and validate the optimization outcomes under various conditions. This would strengthen the credibility and applicability of the findings.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper raises an interesting issue of applying GAs to optimise the design parameters of torque motors in direct-drive valves. GAs are gaining popularity in many areas of application. Other studies have shown that GAs are an effective tool for optimising the design of composite cylinder structures. I suggest mentioning this in the Introduction section. Furthermore, not all parameters are described in equation (1). The same applies to equation (5). Please explain the basis for the initial values in Table 1. In my opinion, there is no need to repeat the initial values in Table 2. The weight values (from equation 23) should be greater than 0. The experimental verification is definitely a strong point of the paper. Please consider presenting the initial range in Figure 17 on a larger scale (e.g. from 0 to 0.03A). Table A1 gives the density of hydraulic oil as 778 kg/m³. Typically, the density of hydraulic oil is significantly higher (e.g. for HL68, the density is approximately 880 kg/ m³). Why did the authors use oil with a reduced density? Detailed comments are provided in the attached manuscript file (pdf).

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for the revised manuscript. Most of my previous concerns have been adequately addressed. However, I recommend further enhancement of the methodology section. A more analytical and detailed explanation of the studied method—covering its rationale, implementation, and limitations—would strengthen the manuscript’s clarity and scientific rigor.

Author Response

Comments 1: Thank you for the revised manuscript. Most of my previous concerns have been adequately addressed. However, I recommend further enhancement of the methodology section. A more analytical and detailed explanation of the studied method—covering its rationale, implementation, and limitations—would strengthen the manuscript’s clarity and scientific rigor.

Response 1: Thank you for this valuable suggestion. We have revised the methodology section to provide a more detailed and analytical explanation of the genetic algorithm, with the following specific enhancements:

(1) Rationale: We have added a concise justification at the beginning of Section 4.1 for selecting the genetic algorithm, emphasizing its effectiveness in handling nonlinear and conflicting multi-objective optimization problems.

(2) Implementation: We have elaborated on key GA operational steps, including the implementation of real-number encoding, tournament selection, simulated binary crossover, polynomial mutation, and the role of the elitism strategy.

(3) Limitations: We have explicitly discussed the limitation of converting the multi-objective problem into a single-objective formulation using fixed weights. A direction for future work employing algorithms like NSGA-II to explore Pareto-optimal solutions has also been provided.

The revised text is highlighted in red in the manuscript for easy reference. The updated manuscript is attached to this letter.

Author Response File: Author Response.pdf