PONE-D-24-46880Predicting the Tensile Properties of Heat Treated and Non-Heat Treated LPBFed AlSi10Mg Alloy using Machine Learning Regression AlgorithmsPLOS ONE

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PLOS ONE

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study investigated the effectiveness of machine learning algorithms in predicting the tensile properties of heat-treated and non-heat-treated AlSi10Mg alloys, especially the ultimate tensile strength, yield strength and elongation. The alloy was produced by laser powder bed fusion ( LPBF ) technology. Multiple machine learning regression ( MLR ) models such as linear regression ( LR ), Gaussian process regression ( GPR ), random forest regression ( RFR ) and decision tree ( DT ) were used to analyze the data. The structure of the article is rigorous, and the results and discussions are rich. However, this manuscript still needs a lot of modifications :

1.The quality of the pictures in this article is generally poor, please upload high-quality pictures and add some picture labels.

2.The second chapter of the article has more conceptual content, please simplify the conceptual content and supplement the experimental data content involved in this article.

3.Although RMSE, MSE, MAE and R2 are used to evaluate the model, more statistical analysis methods can be added in the discussion to support the superiority statement of the model.

4.If the model has been hyper-parameterized, it can be clearly stated in the paper, which can help readers understand whether the reported performance indicators are the best results for each model.

5.The training process of the prediction model is less, such as : optimizer, activation function, neuron, number of iterations, learning rate and other data.

6.The paper mentions data augmentation, ' Data was augmented up to a limit of 100 Iterations... ', why there is no overfitting in the number of iterations within 100 times, please explain this part in more detail.

7.7.Although the effect of heat treatment on tensile properties ( such as tensile strength and yield strength ) was reported, the results showed inconsistencies, which may be confusing to readers. It is recommended to explain the reasons for these inconsistencies, such as changes in the heat treatment process, material batch differences, or factors such as the size / geometry of the sample. It is necessary to further explain why heat treatment does not generally improve the ductility and other properties, which will contribute to the practical application value of the research.

8.Although the conclusions summarize the research results well, the practical application implications of these results can be further discussed, such as : how the model can help guide the design of LPBF aluminum alloy components, and whether these findings can affect the decision-making in the manufacturing process.

Reviewer #2: The manuscript explores the application of machine learning techniques to model the mechanical properties of AlSi10Mg alloy samples fabricated via laser powder bed fusion. Various modeling approaches were employed, including linear regression, Gaussian process regression, random forest regression, and decision tree regression.

My considerations regarding the manuscript are as follows:

#1 Carefully review the text to identify and correct minor typographical errors, such as the one in the sentence following Equation 2.

#2 Enhance the quality and resolution of the figures, particularly Figures 4 and 5, to improve clarity.

#3 Improve the discussion on the tensile test results, emphasizing the differences between treated and untreated samples. Use new references to facilitate this discussion.

#4 The conclusions are too long, it is necessary to summarize them.

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Reviewer #1: Yes:  Jun GUO

Reviewer #2: No

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Predicting the Tensile Properties of Heat Treated and Non-Heat Treated LPBFed AlSi10Mg Alloy using Machine Learning Regression Algorithms

PONE-D-24-46880R1

Dear Dr. Bonyah,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Vasudev Vivekanand Nayak

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear authors, please provide higher resolution versions of the figures used in this manuscript to the production office during the proofing stage.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study evaluated the capability of four machine learning regression models in predicting the tensile properties of heat-treated and non-heat-treated AlSi10Mg alloys. The Gaussian Process Regression (GPR) model demonstrated the highest prediction accuracy for heat-treated samples. For non-heat-treated specimens, the Linear Regression (LR) model showed superior fitting in predicting ultimate tensile strength, while GPR remained optimal for other properties. The research confirms the universality and high reliability of GPR in additive manufacturing material performance prediction, providing an efficient data-driven approach for process optimization. Upon review, the authors have addressed expert feedback through manuscript revisions and improvements. However, figure quality issues persist unresolved. Subject to these final amendments, this revised manuscript is recommended for acceptance.

Reviewer #2: Dear authors,

All the suggestions have been taken into account and have improved the understanding of the manuscript. Therefore, I recommend the manuscript for publication.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

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