Next Article in Journal
Chemical Modification of Glycoproteins’ Carbohydrate Moiety as a General Strategy for the Synthesis of Efficient Biocatalysts by Biomimetic Mineralization: The Case of Glucose Oxidase
Next Article in Special Issue
Viscoelastic Property of an LDPE Melt in Triangular- and Trapezoidal-Loop Shear Experiment
Previous Article in Journal
Precise Synthesis and Thermoresponsive Property of Poly(ethyl glycidyl ether) and Its Block and Statistic Copolymers with Poly(glycidol)
Previous Article in Special Issue
Optimization of the Warpage of Fused Deposition Modeling Parts Using Finite Element Method
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Polymers
Volume 13
Issue 22
10.3390/polym13223874
Review Report
polymers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

Printed Edition

A printed edition of this Special Issue is available at MDPI Books....
share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Article
Peer-Review Record

Transfer Learning Applied to Characteristic Prediction of Injection Molded Products

Polymers 2021, 13(22), 3874; https://doi.org/10.3390/polym13223874
by Yan-Mao Huang, Wen-Ren Jong * and Shia-Chung Chen
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Polymers 2021, 13(22), 3874; https://doi.org/10.3390/polym13223874
Submission received: 28 September 2021 / Revised: 4 November 2021 / Accepted: 4 November 2021 / Published: 9 November 2021
(This article belongs to the Special Issue Advanced Polymer Simulation and Processing)

Round 1

Reviewer 1 Report

The current work has merit, quite technical but sufficiently novel to highlight the machine learning potential for injection moulding.

The introduction is very dedicated. Perhaps good to start the discussion with examples of injection moulding processes and applications. There are several Polymers contributions to cite. I suggest to add at least three, also including a composite one (e.g. Polymers 2019, 11, 87)

Figure 5 can be improved for a general reader

2.6.1 please make a difference between n and N

Please explain to a general reader the in silico nature of the “experiments”

General comment: the number of reference is quite low. Please make this stronger, specifically in the results and discussion making comparison to previous work.

Author Response

To the reviewers:

Thank you for the suggestions. We have added several references to the paper, and also added the results of transfer learning in injection molding simulation in the conclusion. In addition, we have cited the references suggested by the reviewers. The formula (3) in 2.6.1 is the formula of Taguchi method Quality Characteristics. Last not least, we have redrawn figure 5 to make it easier to read.

Reviewer 2 Report

Attached

Comments for author File: Comments.pdf

Author Response

To the reviewers:

Thank you for the suggestions. We have added several references to verify the effectiveness of transfer learning, and also added the results of transfer learning in injection molding simulation in the conclusion. In addition, we have cited the document suggested by the reviewers. The formula(3) in 2.6.1 is the formula of Taguchi method Quality Characteristics. Last not least, the reasons for using Back Propagation Neural Network (BPNN), please refer to the supplementary explanation in 2.1.1.

Round 2

Reviewer 2 Report

Attached

Comments for author File: Comments.pdf

Author Response

To the reviewer:

Thank you for the suggestions. We have a more detailed description of the Introduction. At the same time, we have redrawn Figure 4 to more clearly express the operating principle of Random Shuffle. In addition, we have cited relevant references in the conclusion to support our research results.

Back to TopTop