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Thanks for the resubmission of the revision. I am happy to recommend the acceptance of your paper. Congratulations and all the best.
[# PeerJ Staff Note - this decision was reviewed and approved by Massimiliano Fasi, a PeerJ Section Editor covering this Section #]
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Thanks for the resubmission. Kindly incorporate my comments about adding a systematic literature review section (this should be section 2) that makes research gaps more explicit. Once we receive the revision, I will send the revised version to the reviewer. Use the current version (the one with reviewer comments addressed) for making revisions. Kindly make sure that when you resubmit, the changes made in response to my comments and the reviewer's comments should be explicitly marked in the paper. This will be the last review round, so kindly take time and make a resubmission addressing all comments.
**PeerJ Staff Note:** Please ensure that ALL review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**PeerJ Staff Note:** Please note that this revision has only been reviewed by the Academic Editor at this point. Once you have addressed their comments, it will still need to be sent out for peer review, so please retain your responses to the reviewers in the next response letter.
Thanks for resubmission. Please refer to my feedback, it is currently unaddressed by your team.
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Thanks for the revision. Please incorporate the reviewer comments and also add a literature review section (not a random collection of references but a systematic collection of recent articles) to make research gaps more explicit.
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**PeerJ Staff Note:** Please note that this revision has only been reviewed by the Academic Editor at this point. Once you have addressed their comments, it will still need to be sent out for peer review, so please retain your responses to the reviewers in the next response letter.
Thanks for the revision. Please incorporate the reviewer comments and also add a literature review section (not a random collection of references but a systematic collection of recent articles) to make research gaps more explicit.
This manuscript provides a comprehensive overview of data-driven product shape design technology, covering a wide range of methods and applications. However, some areas need improvement to meet the standards of a high-quality academic review. Here are some specific comments:
- Many abbreviations are not written in a standardized way. It is recommended to provide a list of abbreviations.
- There are many instances of non-standard content. The position of commas and quotation marks in lines 87–89 is incorrect (eg, "product design technology"), and other parts should be checked carefully.
"Design concept evaluation" is an important research area, but it is not included in your manuscript (also lines 87–89). Please add it to the relevant section and review and cite relevant literature.
Regarding the issues of data and quantitative changes I mentioned previously, it is hoped that specific scales and analyses can be added to enhance the persuasiveness of the paper.
The reviewers have identified a number of shortcomings, especially in the areas of methodology and interpretation of the findings.
**PeerJ Staff Note:** Please ensure that all review and editorial comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
This manuscript provides a comprehensive overview of data-driven product shape design technology, covering a wide range of methods and applications. However, several critical areas need improvement to meet the standards of a high-quality academic review.
Details are as follows:
-Several references appear to have incorrect font sizes, specifically at Lines 47, 50, and 52.
-At Line 709, there is an abnormal space between “of” and “the”. Line 710 is missed.
-The comparative analysis of different methods could be improved by including more structured comparisons and quantitative data.
-The Results section lacks detailed comparative data and dynamic change analysis among the various methods discussed. The inclusion of statistical data, tables, and figures would enhance the academic rigor and support the claims made in the manuscript.
STRENGTHS
Language and Clarity
- The paper is written in professional, clear, and unambiguous English.
- Technical terms are well-defined and used consistently.
Literature References and Background
- The paper provides a comprehensive review of 389 sources spanning from 2002 to 2024.
- The background section is thorough, with appropriate references to foundational and recent works.
Professional Article Structure
- The paper follows a logical structure consistent with PeerJ guidelines.
- Clear sectioning into Introduction, Methods, Results, Discussion, and Conclusions.
Broad and Cross-Disciplinary Interest
- The topic is relevant to both computer scientists and product designers, meeting PeerJ’s scope for cross-disciplinary content.
Introduction and Motivation
- The introduction effectively explains the motivation behind the paper and defines the target audience.
WEAKNESSES AND SUGGESTED IMPROVEMENTS
Inconsistency and Language
- Some sentences are awkwardly constructed or overly complex.
- Suggested Fix: Streamline phrasing for better flow and readability.
Example: Line 46–47: "The relationship between product aesthetics and functionality has become more complex and diversified." Suggested Fix: "The relationship between product aesthetics and functionality has become increasingly complex and diverse."
Unclear Section Transitions
- Transitions between sections, particularly from Introduction to Methods, are abrupt.
- Suggested Fix: Add linking sentences to improve flow.
Example: "Having established the significance of product shape generation, the following section outlines the methods employed in this study."
Lack of Justification for Recent Review
- The paper claims that it provides a unique perspective, but this is not explicitly justified.
Suggested Fix: Clarify why this review is necessary despite recent similar work.
Example: "While recent reviews have focused on specific shape generation techniques, this paper provides a broader perspective by integrating AI, machine learning, and industrial design methodologies."
Minor Citation Formatting Issues
- Citation style is inconsistent (e.g., missing spaces and incorrect punctuation).
- Suggested Fix: Ensure that all citations follow a uniform style.
Example: Line 51: (Li, Xa,2023) → Should be (Li, Xa, 2023).
Figures and Table References
- Some figures (e.g., Figure 4) are not clearly labeled or referenced within the text.
- Suggested Fix: Directly reference figures and tables to strengthen the connection between text and data.
Example: "As shown in Figure 4, the product shape generation process involves..."
Oxford Comma Inconsistency
- The Oxford comma is used inconsistently.
- Suggested Fix: Apply the Oxford comma consistently throughout.
Example: Line 61–63: "feature extraction, image classification, and object detection."
STRENGTHS
Relevance to Journal Scope
- The paper aligns with the aims and scope of PeerJ Computer Science, as it explores the intersection of artificial intelligence, machine learning, and product design — a clear computer science topic.
- The paper fits the category of a literature review and adheres to the journal's guidelines for such articles.
Rigorous and Ethical Investigation
- The authors demonstrate a thorough understanding of the field by analyzing a large body of literature (389 papers) using established methods and tools (e.g., Citespace, MATLAB).
- The methodology reflects a high technical standard, with detailed descriptions of data extraction and analysis techniques.
Comprehensive and Unbiased Coverage
- The paper covers a broad range of techniques (e.g., shape extraction, decision-making, optimization) and perspectives.
- The review discusses both strengths and limitations of existing approaches, ensuring balanced coverage.
Adequate Citation and Attribution
- Sources are properly cited, and both direct quotes and paraphrasing are appropriately handled.
Logical Organization
- The study is structured logically, with clear subsections for methodology, analysis, and results.
- The use of diagrams and figures to explain key processes enhances reader understanding.
WEAKNESSES AND SUGGESTED IMPROVEMENTS
- Lack of Detail in Methodology for Replication
- While the paper mentions that Citespace, MATLAB, and Adobe Illustrator were used, it does not provide enough detail on how these tools were applied.
- Suggested Fix: Include more detail on the methodology to allow for replication.
Example: "Citespace was used to analyze citation networks and identify key research trends over time. Specifically, clustering was performed using a modularity resolution of 0.7, and the top 100 most cited papers were analyzed for content mapping."
Survey Methodology Not Fully Justified
- The paper does not explain the criteria used to select the 389 papers for the literature review.
- Suggested Fix: Provide details on the inclusion/exclusion criteria.
Example: "Articles were included based on relevance to data-driven product shape generation, peer-reviewed status, and publication within the last 20 years. Papers focusing solely on theoretical AI without direct application to product design were excluded."
Missing Explanation of Data Analysis Methods
- While the results section mentions analysis of shape generation algorithms, the methods used for analysis (e.g., clustering, regression, or thematic analysis) are not explained in detail.
- Suggested Fix: Provide a clear explanation of the analytical methods used.
Example: "Clustering analysis was conducted using the K-means algorithm with an optimal cluster size determined by the elbow method. Regression analysis was applied to model the relationship between design complexity and user satisfaction."
No Discussion of Potential Bias or Limitations
- The paper does not address potential bias in data collection or analysis.
- Suggested Fix: Acknowledge potential sources of bias and how they were mitigated.
Example: "Potential bias may arise from the predominance of English-language papers and the focus on AI-based methods. To mitigate this, we included diverse sources and validated key findings using cross-comparison."
Inadequate Description of Tools and Software Versions
- The paper mentions using Citespace and MATLAB but does not specify the version or configuration used.
- Suggested Fix: Include details on the software versions and settings used for analysis.
Example: "Citespace version 5.7.R5 was used with default modularity resolution settings. MATLAB 2021b was used for statistical analysis with the Statistics and Machine Learning Toolbox."
Lack of Consistency in Subsection Length and Detail
- Some subsections (e.g., shape generation) are very detailed, while others (e.g., data transformation) are relatively brief.
- Suggested Fix: Ensure consistency in the level of detail across all subsections.
Example: Expand the data transformation section to explain the specific types of data mapping models and how they were evaluated.
STRENGTHS
Clear and Well-Stated Conclusions
- The paper’s conclusions are clearly stated and linked to the research objectives.
- The conclusions accurately reflect the findings presented in the results and discussion sections.
- The paper successfully synthesizes information from the literature and presents meaningful insights about the current state and future prospects of product shape generation technologies.
Logical Flow and Support for Arguments
- The paper builds a cohesive argument based on the analysis of 389 references.
- The results section presents a structured breakdown of different product shape generation techniques, including extraction, analysis, and decision-making.
Meaningful Contribution to the Literature
- The paper identifies important trends and gaps in the research, such as the increasing role of artificial intelligence and machine learning in product shape generation.
- It discusses how current approaches can be improved and adapted to future technological changes.
Identifies Practical Applications and Future Directions
- The discussion highlights practical applications in industrial design, manufacturing, and user-driven product customization.
- Future research directions are identified, including improvements in algorithm efficiency and user experience integration.
WEAKNESSES AND SUGGESTED IMPROVEMENTS
- Lack of Direct Evidence to Support Some Claims
- While the paper states that AI-based shape generation leads to improved performance and efficiency, it does not provide direct evidence or examples to back this claim.
- Suggested Fix: Provide concrete examples or references to studies that demonstrate these performance improvements.
Example: "Recent studies (e.g., Li et al., 2023) have shown that AI-based product shape generation can improve design accuracy by up to 15% compared to traditional methods."
Conclusions Not Fully Tied to Results
- Some conclusions are broader than the data presented in the results section.
- Suggested Fix: Ensure that each conclusion is explicitly supported by findings from the results section.
Example: Line 660–662: "The core of this progress lies in the deep analysis, efficient conversion, precise extraction, and intelligent generation of shape design data."
- Suggested Fix: Provide specific evidence from the analysis that supports each element of this statement.
Insufficient Discussion of Limitations and Alternative Explanations
- The paper does not critically assess the limitations of the data-driven approach or consider alternative interpretations of the findings.
- Suggested Fix: Acknowledge potential limitations and suggest how they could be addressed in future research.
Example: "While data-driven methods enhance design efficiency, they may overlook contextual and aesthetic factors that are not easily quantifiable. Future research could explore hybrid approaches that combine data-driven models with user feedback."
Overgeneralized Impact of AI and Machine Learning
- The paper implies that AI and machine learning are universally beneficial for product shape generation without considering cases where they may not be suitable.
- Suggested Fix: Provide a more balanced assessment of the role of AI, highlighting scenarios where traditional methods might still be more effective.
Example: "AI-based shape generation is highly effective for complex design tasks, but simpler designs or highly creative projects may still benefit from traditional designer-led approaches."
Insufficient Justification for Novelty of the Findings
- The paper states that the review offers new insights but does not clearly explain how it differs from previous reviews.
- Suggested Fix: Explicitly state what is novel about the findings compared to prior reviews.
Example: "Unlike previous reviews, this paper provides a comprehensive classification of shape generation methods and integrates the latest AI-based approaches."
Future Work Section is Vague
- While the paper mentions future research directions, they are stated in general terms without specific suggestions for next steps.
- Suggested Fix: Provide more actionable and specific recommendations for future work.
Example: "Future research should focus on developing hybrid models that integrate AI-based algorithms with user-generated feedback to improve both efficiency and customization."
POSITIVE
Comprehensive Scope
- The paper covers a wide range of techniques and methodologies related to product shape generation, including machine learning, AI, and optimization.
- The review reflects a deep understanding of the historical and current trends in the field.
Strong Technical Content
- The paper discusses advanced techniques, such as generative adversarial networks (GANs) and decision-making algorithms, with a high level of technical detail.
- The paper’s explanation of complex algorithms (e.g., shape extraction, clustering) demonstrates a strong grasp of the subject matter.
Clarity and Structure
- The structure of the paper follows the expected format of a literature review.
- The use of headings and subheadings is effective in guiding the reader through the content.
Balanced Discussion
- The paper discusses both strengths and challenges of current product shape generation methods, ensuring a balanced perspective.
GENERAL WEAKNESSES AND SUGGESTED FIXES
Title Could Be More Precise
- The current title ("A Comprehensive Review of the Latest Developments in Data-Driven Product Shape Design Technology") is informative but too broad.
- Suggested Fix: Make the title more specific to reflect the paper's focus on AI and machine learning.
Example: "A Review of AI-Based Product Shape Generation Technologies: Trends, Challenges, and Future Directions"
Abstract Is Overly Technical and Lengthy
- The abstract includes excessive technical jargon and is longer than necessary for a summary.
- Suggested Fix: Simplify the abstract while retaining key points about scope and findings.
Example: "This paper reviews recent advancements in data-driven product shape generation, focusing on AI-based methods. Key techniques such as shape extraction, clustering, and decision-making algorithms are analyzed. Future work should focus on improving algorithm efficiency and integrating user feedback."
Excessive Length and Redundancy
- Combine the "Data Analysis Phase" and "Shape Generation Phase" since they overlap in content.
Figures and Tables Need Better Integration
- Some figures are not clearly referenced in the text, and the captions lack detail.
- Suggested Fix: Ensure that all figures and tables are explicitly referenced and explained in the text.
Example: "Figure 4 illustrates the shape extraction process, highlighting key steps such as edge detection and contour analysis."
Lack of Practical Implications
- While the paper discusses technical methods, it does not sufficiently address how these findings could be applied in practice.
- Suggested Fix: Include a section on practical implications for product designers and industry professionals.
Example: "AI-based product shape generation can reduce design time by automating the extraction and optimization process. This has potential applications in automotive design, consumer electronics, and furniture manufacturing."
Potential Ethical or Privacy Concerns
- The paper does not mention potential ethical issues related to AI-based design methods (e.g., bias, intellectual property).
- Suggested Fix: Add a brief discussion on ethical considerations in AI-based design.
Example: "AI-based design methods must address issues of bias and intellectual property, ensuring that generated designs are fair and legally protected."
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