Computational Approach for Optimizing Resin Flow Behavior in Resin Transfer Molding with Variations in Injection Pressure, Fiber Permeability, and Resin Sorption

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript "Computational Approach for Optimizing Resin Flow Behavior in Resin Transfer Molding with Variations in Injection Pressure, Fiber Permeability, and Resin Sorption" presents a study on resin infiltration in biaxial noncrimp carbon fiber reinforcements using a bio-based FormuLITE 2500A/2401B epoxy resin. The research integrates numerical simulations based on Darcy’s law and resin sorption effects, alongside experimental validation, to optimize resin transfer molding (RTM) parameters. While the study is well-structured and provides valuable insights into optimizing RTM for composite manufacturing, there are several areas that require improvement before the manuscript can be considered for publication.

1.The novelty of incorporating bio-based epoxy resin should be emphasized more. How does this material compare mechanically and environmentally to conventional petroleum-based epoxies?

2.The study assumes constant permeability values—but in reality, permeability changes dynamically as fibers absorb resin. Have these effects been considered?

3. Injection pressure effects are tested in a limited range (15-25 kPa)—would higher pressures affect void formation?

4. The sorption coefficient (β) is introduced in Equation 6, but its experimental measurement is not described.

5.The flow front evolution plots (Figures 3-5) could benefit from overlaying experimental and numerical curves for direct comparison.

Author Response

Reviewer -1

We sincerely appreciate your time and effort in reviewing our manuscript. Your insightful comments and constructive suggestions have been invaluable in improving the clarity, depth, and overall quality of our work. We have carefully considered each of your recommendations and have revised the manuscript accordingly to enhance its scientific rigor and presentation.

 

The manuscript "Computational Approach for Optimizing Resin Flow Behavior in Resin Transfer Molding with Variations in Injection Pressure, Fiber Permeability, and Resin Sorption" presents a study on resin infiltration in biaxial noncrimp carbon fiber reinforcements using a bio-based FormuLITE 2500A/2401B epoxy resin. The research integrates numerical simulations based on Darcy’s law and resin sorption effects, alongside experimental validation, to optimize resin transfer molding (RTM) parameters. While the study is well-structured and provides valuable insights into optimizing RTM for composite manufacturing, there are several areas that require improvement before the manuscript can be considered for publication.

 

Comment: 1.The novelty of incorporating bio-based epoxy resin should be emphasized more. How does this material compare mechanically and environmentally to conventional petroleum-based epoxies?

Response: Thank you for your valuable feedback. We have now included a detailed discussion on the importance of bio-based epoxy resins in the Introduction sections. This revision highlights their environmental benefits and processing advantages over conventional petroleum-based epoxies. Additionally, we have incorporated a comparative table (Tale 1) summarizing the key properties of the FormuLITE 2500A/2401B epoxy system used in this study, emphasizing its viscosity, thermal stability, and sorption characteristics. These additions further clarify the significance of using bio-based epoxy in radial injection RTM applications.

 

Comment: 2.The study assumes constant permeability values—but in reality, permeability changes dynamically as fibers absorb resin. Have these effects been considered?

Response: Permeability (k) plays a crucial role in resin infiltration and is often treated as a constant parameter in many RTM models. However, in reality, fiber compaction, capillary effects, and resin sorption can lead to dynamic variations in permeability over time. Studies have shown that as resin infiltrates fiber reinforcements, permeability may decrease due to fiber swelling, localized compression, and resin absorption effects, particularly in bio-based resins with higher sorption rates.

In this study, we assume constant permeability values based on experimentally measured data for biaxial non-crimp carbon fiber reinforcement. While this assumption provides a first-order approximation of resin infiltration dynamics, we recognize that a time-dependent permeability function could further refine the model’s predictive accuracy. Future work will focus on integrating empirical permeability evolution models, such as the Kozeny-Carman equation or sorption-dependent permeability corrections, to better capture permeability variations during RTM.

Comment: 3. Injection pressure effects are tested in a limited range (15-25 kPa)—would higher pressures affect void formation?

Response: Thank you for your insightful comment. We acknowledge that higher injection pressures (>25 kPa) could influence void formation and resin infiltration dynamics. While our study primarily focuses on a 15–25 kPa range, we recognize that further increasing the pressure may lead to higher resin velocities, increased fiber compaction, and potential air entrapment, all of which could impact void formation.

 

Comment: 4. The sorption coefficient (β) is introduced in Equation 6, but its experimental measurement is not described.

Response: Thank you for your observation. We acknowledge that while Equation 6 introduces the sorption coefficient (?), the manuscript does not explicitly describe how this parameter was experimentally determined. To address this, we have added a detailed explanation (After equation 6) of the experimental procedure used to measure ? in the Materials and Methods section.

 

Comment: 5.The flow front evolution plots (Figures 3-5) could benefit from overlaying experimental and numerical curves for direct comparison.

Response: Thank you for your suggestion. While overlaying experimental and numerical flow front evolution curves could aid in direct comparison, we have chosen to present them separately to maintain clarity and consistency across all figures. This approach ensures that each dataset is distinctly visible without overlap, reducing potential misinterpretations. Additionally, the relative error (<5%) between experimental and numerical results has been quantified in Table 2 (updated manuscript), providing a clear numerical validation of the model’s accuracy. Maintaining this format allows for uniformity across all figures while still enabling a comprehensive comparison.

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript presents a computational and experimental study on Resin Transfer Molding (RTM) for biaxial noncrimp carbon fiber reinforcement using FormuLITE 2500A/2401B bio-based epoxy resin. The study aims to optimize resin flow behavior under varying conditions of injection pressure, fiber permeability, resin viscosity, porosity, and sorption effects. The research integrates Darcy’s law, mass conservation equations, and sorption models to predict flow-front progression.

The study provides insights into RTM process optimization for defect minimization and uniform fiber impregnation, with potential applications in the aerospace, automotive, and wind energy sectors.

1. Some sections (particularly the introduction and background) contain redundant information and could be more concise. The authors should streamline the content to avoid repetition and ensure a clear flow of ideas. The discussion on petroleum-based resins in the introduction partially overlaps with the background section. Consider merging or restructuring these sections to enhance readability.
2. The selection of specific process parameters (e.g., permeability range, and viscosity values) is not explicitly justified. The authors should provide a stronger rationale for why these particular values were chosen. Were they based on prior studies? How do they relate to industrial RTM conditions?
3. While the study discusses future work, it does not explicitly acknowledge its limitations. The authors should clearly outline the main limitations (e.g., potential sources of experimental error, and model assumptions). Example: The effect of fiber compaction on permeability could be further investigated.
4. The study does not perform a statistical sensitivity analysis to determine which parameters most significantly affect resin infiltration. The authors should consider conducting a sensitivity analysis or uncertainty quantification to: Identify the most influential variables. Strengthen the predictive capability of the numerical model.
5. The study mentions a machine-learning-based surrogate model but does not detail its methodology. The authors should provide more information on the machine learning approach (e.g., the type of algorithm used) and on accuracy metrics to validate its predictive performance.

Author Response

Reviewer -2

We sincerely appreciate your time and effort in reviewing our manuscript. Your insightful comments and constructive suggestions have been invaluable in improving the clarity, depth, and overall quality of our work. We have carefully considered each of your recommendations and have revised the manuscript accordingly to enhance its scientific rigor and presentation.

 

The manuscript presents a computational and experimental study on Resin Transfer Molding (RTM) for biaxial noncrimp carbon fiber reinforcement using FormuLITE 2500A/2401B bio-based epoxy resin. The study aims to optimize resin flow behavior under varying conditions of injection pressure, fiber permeability, resin viscosity, porosity, and sorption effects. The research integrates Darcy’s law, mass conservation equations, and sorption models to predict flow-front progression.

 

The study provides insights into RTM process optimization for defect minimization and uniform fiber impregnation, with potential applications in the aerospace, automotive, and wind energy sectors.

 

Comment: Some sections (particularly the introduction and background) contain redundant information and could be more concise. The authors should streamline the content to avoid repetition and ensure a clear flow of ideas. The discussion on petroleum-based resins in the introduction partially overlaps with the background section. Consider merging or restructuring these sections to enhance readability.

Response: Thank you for your thoughtful suggestion. We have carefully reviewed the Introduction and Background Study sections and have made efforts to streamline the content while maintaining clarity. The Introduction has been structured to provide a broad context and motivation for the study, while the Background Study explicitly focuses on past research and advancements in the field. This distinction ensures that the literature review remains comprehensive without unnecessary overlap. However, we appreciate your feedback and will continue refining these sections to enhance readability and avoid redundancy.

 

Comment: The selection of specific process parameters (e.g., permeability range, and viscosity values) is not explicitly justified. The authors should provide a stronger rationale for why these particular values were chosen. Were they based on prior studies? How do they relate to industrial RTM conditions?

Response: Thank you for your insightful comment. We acknowledge the need to provide a clearer justification for the selection of permeability range, viscosity values, and other process parameters. To address this, we have revised the Materials and Methods section to explicitly state that these values were chosen based on experimental measurements and well-established findings from prior literature. The permeability range was selected in accordance with reported values for biaxial non-crimp carbon fiber reinforcements used in RTM, ensuring consistency with previous studies on fiber permeability and resin infiltration. Similarly, the viscosity values correspond to bio-based epoxy formulations, as documented in recent composite manufacturing research, which highlights their processing advantages in low-pressure RTM applications. Additionally, the chosen parameter range aligns with industrial RTM conditions, ensuring a comprehensive assessment of resin infiltration behavior that reflects both experimental observations and validated numerical models.

 

Comment: While the study discusses future work, it does not explicitly acknowledge its limitations. The authors should clearly outline the main limitations (e.g., potential sources of experimental error, and model assumptions). Example: The effect of fiber compaction on permeability could be further investigated.

Response: Thank you for your valuable suggestion. We acknowledge the importance of explicitly stating the limitations of our study to provide a balanced perspective on the findings. To address this, we have added a Limitations subsection in the Conclusion to discuss key constraints, including model assumptions, experimental uncertainties, and areas for further investigation. Specifically, we highlight that the effect of fiber compaction on permeability was not explicitly modeled and that real-time resin infiltration variability could introduce minor deviations between experimental and numerical results. These limitations will guide future research toward refining the model and improving its applicability in industrial RTM processes.

 

Comment: The study does not perform a statistical sensitivity analysis to determine which parameters most significantly affect resin infiltration. The authors should consider conducting a sensitivity analysis or uncertainty quantification to: Identify the most influential variables. Strengthen the predictive capability of the numerical model.

Response: Thank you for your thoughtful suggestion. We agree that a statistical sensitivity analysis or uncertainty quantification would provide deeper insights into the relative influence of different parameters on resin infiltration behavior. However, incorporating such an analysis is a substantial undertaking that goes beyond the current scope of this study. As our primary focus is on validating the numerical model through experimental comparisons, we have not performed a formal sensitivity analysis at this stage. That said, we recognize the value of this approach and will consider it in future studies to further refine the predictive capability of the model and identify the most influential process parameters.

 

Comment: The study mentions a machine-learning-based surrogate model but does not detail its methodology. The authors should provide more information on the machine learning approach (e.g., the type of algorithm used) and on accuracy metrics to validate its predictive performance.

Response: Thank you for pointing this out. We sincerely apologize for the oversight. While we initially considered implementing a machine-learning-based surrogate model, we realized that it would require a substantial amount of additional work, including extensive dataset generation and model training. Due to time constraints, we decided to defer this aspect to future studies, and we had previously mentioned it in the future work section. However, we mistakenly forgot to remove the reference to it in the abstract. We have now corrected this to ensure consistency and to avoid any misrepresentation of the study’s scope. We appreciate your attention to detail and your valuable feedback.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I agree to publish