Research on Lithium-Ion Battery Diaphragm Defect Detection Based on Transfer Learning-Integrated Modeling

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript entitled ‘Research on Lithium-ion Battery Diaphragm Defect Detection Based onTransfer Learning Integrated Modeling’ resents a practical detect flaws in lithium-ion battery components, combining advanced AI models and smart learning techniques to boost detection accuracy. I recommend this manuscript could be considered for acceptance in Electronics after minor revisions.

1. Please reformat the abstract into continuous paragraphs without section numbering.

2. What is the content of the () after the first structure Backbone in Figure 3 on page 6, please explain if there is any, if there is no content please modify it.

3. The border forms in Table 1 are not drawn in accordance with the standard three-line table and should be redrawn.

4. How specific defects differentially degrade lithium-ion battery performance?

5. What is the difference between bubbles and composite bubbles? How to quantitatively distinguish them?

6. More details about data acquisition are suggested to be added because the full dataset isn't public.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Please find attached.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Language should be improved. Many instances  where the sentences are too long and vague without proper punctuations are seen in the manuscript.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf