Development and implementation of a Digital Twin workshop for a smoke alarm production line

Min Wu; Wenfeng Ying

doi:10.1371/journal.pone.0341224

Peer Review History

Original SubmissionApril 2, 2025
5 Aug 2025 Decision Letter - Himadri Majumder, Editor Dear Dr. Wu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Sep 19 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. 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There is no requirement to cite these works unless the editor has indicated otherwise. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? Reviewer #1: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: I Don't Know ****** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ****** Reviewer #1: The article proposes a method for constructing a digital twin workshop for the production line of smoke alarm bases, designed through a four-dimensional architecture comprising the physical layer, model layer, data layer, and service layer. It integrates multi-domain modeling techniques (geometry, physics, behavior, and information) and achieves multi-source heterogeneous data integration and virtual-real collaborative mapping based on the OPC UA protocol. A 3D visualization monitoring system was implemented to enable traceability verification of the production process. Experimental results indicate that the system can reduce labor costs by over 2.1 million yuan and increase product output by 2%. The article is well-structured, but its innovation is limited, and several issues remain: 1. The literature review focuses on industrial applications of digital twins but does not mention case studies comparing related technologies in other low-automation industries (e.g., traditional manufacturing). It is recommended to include discussions of studies in similar contexts. 2. The description of digital twin model construction in Section 3 relies heavily on mathematical expressions for definitions. While these equations provide a theoretical framework, they are highly abstract, lacking derivations of variables and their applications, as well as specific details on how they are quantified or implemented in Siemens NX software. The authors should provide concrete examples and relevant descriptions. 3. In Section 4, the case study validation mentions cost savings (2.1 million yuan/year) and a 2% output increase but lacks control group data (e.g., comparisons with traditional production lines) and corresponding details. It is suggested to include details of the experimental design and statistical analysis methods. 4. Figures 7 and 8 are critical for demonstrating the system's actual implementation, but the text lacks descriptions of specific metrics in the interface (e.g., production progress, equipment status). For example, providing sample outputs or screenshots to illustrate the interface's functionality would be helpful. The authors should provide detailed information explaining the key elements depicted in each figure. 5. The figures provided in the article are mostly conceptual, focusing on architecture and models, with a lack of actual data and real-world scenarios. 6. Transitions between sections are weak. For instance, the transition from model construction (Section 3.1) to information system design (Section 3.2) lacks connective narrative. The authors should add brief transitional paragraphs to clarify how each section builds on the previous one, thereby improving the overall coherence of the manuscript. ****** 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? 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Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0341224.r001
Revision 1
17 Aug 2025 Author Response Dear Editor and Reviewers, We thank you very much for giving us another chance to revise and improve our manuscript. We try our best to revise the paper according to the reviewers’ suggestions, and for those comments we have different opinions, we have explained in detail. A list of changes and responses are listed below. We hope the revised paper has addressed all the reviewers’ concerns and reached the requirements of the journal for publishing. Thank you very much! The authors Responses to Reviewers’ Comments Reply to reviewer #1 1.Concern of the reviewer: The literature review focuses on industrial applications of digital twins but does not mention case studies comparing related technologies in other low-automation industries (e.g., traditional manufacturing). It is recommended to include discussions of studies in similar contexts. Our response: We are grateful to the Reviewer for his elaborate work to find out our weakness.We have added the comparison between digital twin technology and traditional manufacturing . Revised text: At the same time, many scholars have made a comparative study of digital twin technology and traditional manufacturing technology. Semeraro, et al. [9] proposed a data-driven digital twin modeling method to improve the robustness of the model through the invariance mode, and compared with the flexibility and scalability of traditional rule-based modeling methods in complex production systems. Pronost, et al. [10] systematically reviewed the application of digital twins in the whole product life cycle (design, production, operation and maintenance), pointed out the limitations of traditional simulation technology in real-time and cross scale integration, and emphasized the advantages of digital twins in dynamic optimization. The case showed that the product development cycle was shortened by 28%. Mayer, et al. [11] analyzed the technical trend of digital twins in manufacturing, compared the limitations of traditional simulation tools in multi physical field coupling modeling, proposed the advantages of digital twins in interdisciplinary collaboration, and quantified its improvement in the modeling accuracy of complex systems by 42%. Fu Xiangfu, et al.[12] aimed at the limitations of traditional processing error research methods, proposed a digital twin driven multi-scale quality representation framework, and compared with the traditional off-line detection method, verified that its accuracy in real-time error compensation was improved by 51%. D'ambrogio, et al.[13] reviewed the application of AI driven digital twins in manufacturing, compared with the traditional threshold based maintenance strategy, and proposed the dynamic decision-making advantage of digital twins in predictive maintenance. The case showed that the downtime of equipment was reduced by 34%. Pathy, et al. [14] built a supply chain optimization model driven by digital twins, and compared with the traditional static planning method to verify its robustness under dynamic disturbance. The experiment showed that the logistics cost was reduced by 29%. Park, et al. [15] developed a digital twin driven optimization system for production line design. Compared with the limitations of traditional simulation software in layout design, the case shows that the reconstruction time of production line is reduced by 40%. Through specific cases and data comparison, the above papers have revealed the significant advantages of digital twin in design cycle, cost control, real-time optimization, and pointed out the shortcomings of traditional manufacturing technology in complex system management and dynamic response. 2.Concern of the reviewer: The description of digital twin model construction in Section 3 relies heavily on mathematical expressions for definitions. While these equations provide a theoretical framework, they are highly abstract, lacking derivations of variables and their applications, as well as specific details on how they are quantified or implemented in Siemens NX software. The authors should provide concrete examples and relevant descriptions. Our response: Thanks very much to the Reviewer for his meticulous work to find out our weakness , To solve the above problems, in order to make readers more clearly understand the production content of the smoke alarm base, we added the section on the physical system composition and process flow of the base production line, adjusted the original section 3 to section 4, and added the section 4.4 on the construction of NX twin model in section 4, which described in detail how to build the twin model in NX. Revised text: 3. Physical system composition and process flow of the base production line 3.1 Physical system composition The production line for smoke alarm bases comprises five core subsystems (as depicted in Figure 2). The equipment and functions of each subsystem are detailed below: Injection Molding Subsystem: this includes one horizontal injection molding machine (with a clamping force of 500 KN), a raw material dryer, and a cooling water circulation system. It is responsible for melting, injecting, pressure-holding, and cooling the plastic raw materials to form the base. Assembly Subsystem: comprising two six-axis robots (with a repeat positioning accuracy of ±0.02 mm), a conveyor belt (operating at a speed of 0.5 m/s), and tooling fixtures, this subsystem handles the assembly of the base and metal inserts. Machine Vision Subsystem: this subsystem includes two industrial cameras (each with 5 million pixels), lenses (with a focal length of 16 mm), an annular LED light source, and an image processing unit. It is used for assembly positioning guidance and quality inspection. Detection Subsystem: equipped with size detection sensors (with an accuracy of ±0.01 mm) and appearance defect detection devices, this subsystem conducts a comprehensive inspection of the assembled bases. Packing and Sealing Subsystem: this subsystem includes an automatic packing machine, a tape sealing machine, and a labeling machine, which together facilitate the packaging and identification of qualified bases. Figure2 Physical drawing of workshop 3.2 Process flow analysis The entire production process of the base can be categorized into four crucial stages, with the following process parameters and logical relationships for each: Injection Molding Stage: raw materials undergo drying (at a temperature of 80°C for 4 hours) before being fed into the injection molding machine's barrel. Upon heating (with a nozzle temperature of 220°C), they melt and are injected into the mold cavity. The pressure is maintained at 80 MPa for 3 seconds, followed by a cooling period of 15 seconds. Once the mold is opened, the base blank is ejected. Manipulator Assembly Stage: the conveyor belt transports the base blank to the assembly station, where the machine vision system captures an image of the base's positioning holes. After calculating any deviations, the manipulator is guided to grasp the metal insert (with a diameter of 8 mm) and press it into the preset position on the base (with a pressing force of 500 N). Visual Inspection Stage: an industrial camera at the inspection station captures 360° images of the assembled base. Through image algorithms (combining template matching and edge detection), it assesses the perpendicularity of the insert assembly (with a tolerance of ±0.1 mm) and detects any appearance defects, such as cracks or material shortages. Packing and Sealing Stage: qualified bases are conveyed to the packing machine via a conveyor belt and stacked in boxes of 20 each. After the sealing machine applies tape to the top and bottom of the box, a labeling machine prints and affixes a QR code label containing the production date and batch information. Each stage is logically controlled by a PLC (Siemens S7-1200), with state feedback provided by sensors (proximity switches and pressure sensors), thereby forming a continuous production process. 4.4 Construction based on NX twin model The construction of the twin model for the smoke alarm base production line adheres to a hierarchical approach of "equipment level → unit level → production line level," with the specific framework outlined below: Equipment Level Model: establish geometric and behavioral models for individual equipment such as injection molding machines, manipulators, and cameras. Unit Level Model: integrate equipment-level models into subsystems (e.g., injection molding, assembly) to facilitate collaborative simulation among equipment within these subsystems. Production Line Level Model: combine unit-level models to create a virtual representation of the entire production line, enabling comprehensive process simulation and optimization. he modeling process must adhere to the three principles of "geometric consistency, behavioral equivalence, and parameter correlation" to guarantee that the virtual model accurately mirrors the shape, motion, and performance of the physical entity. 4.4.1 Equipment level model construction (1) Modeling of injection molding machine Geometric modeling: Using NX's "synchronous modeling" technology, the 3D skeleton model is generated by importing the 2D drawing (CAD format) of the injection molding machine. Decompose the injection molding machine into sub components such as clamping mechanism, injection mechanism and ejection mechanism, and use "Boolean operation" and "feature modeling" (such as stretching and rotation) to build the detail structure. The accuracy of key components (such as screw and mold cavity) is controlled at 0.005mm, and non key components (such as shell) can be simplified (removing chamfer, small hole and other features) to reduce the complexity of the model. Behavior modeling: Define the motion pair in NX MCD: add "rotation pair" (opening and closing mold rotation axis) and "translation pair" (template movement) to the clamping mechanism, and add "screw pair" (screw rotation and axial movement) to the injection mechanism. Associated driving parameters: drive the motion pair through the "servo motor", set the mold opening and closing speed (0.5m/s), injection pressure (0-100Mpa) and other parameters, corresponding to the PLC control parameters of the physical equipment. Add sensor model: add "position sensor" (to detect the opening and closing state of the mold) and "pressure sensor" (to simulate the cavity pressure) at the mold cavity position, and its signal output is bound to the state variable of the virtual model. (2) Manipulator and machine vision model construction Geometric modeling of manipulator: The "top-down" design method is adopted. Firstly, the base and joint skeleton of the manipulator are constructed, and then the arm, wrist and end effector are added through "assembly constraints" (such as coincidence and parallel). The end effector (gripper) adopts parametric design, and the opening and closing size of the gripper is associated through "expression" (to adapt to inserts with a diameter of 5-10mm). Robot behavior modeling: Add a "rotation pair" at the joint to set the rotation range (-180 °~180 °) and angular velocity (50 °/s). Through the "path planning" function of NX, predefine the motion tracks of grabbing, moving and placing (adopt arc transition to avoid emergency stop). Associate machine vision signals: install a virtual camera model at the wrist, set the field of view angle (60 °) and resolution (5million pixels), and the output "positioning deviation value" is used as the compensation parameter of the manipulator movement. Machine vision system modeling: Build the geometric model of camera, lens and light source in NX, and define the installation position through the "coordinate system" (consistent with the installation coordinates of the physical camera). Develop virtual image processing module: call opencv algorithm library through NX open API, realize "template matching" (locating the hole position of the base) and "defect detection" (simulating gray value analysis) in the virtual environment, and output the detection results (ok/ng signal). (3) Construction of packing and sealing equipment model Geometric modeling: The sealing machine is decomposed into conveying roller table, pressing roller and belt cutting mechanism, and the "sheet metal design" module is used to build a thin shell structure (1mm thick). The carton model adopts "folding design" to simulate the deformation characteristics of the carton through the "flexible body" attribute. Behavior modeling: Add "rotating pair" to the conveying roller table and set the speed (100r/min). The "translation pair" is added to the pressing roller mechanism, and the pressing stroke (0-50mm) is set, and the pressure parameter (100N) is associated with the virtual pressure sensor. The belt cutting mechanism simulates the cutting action through "Boolean operation" and sets the cutting delay time (consistent with the action time of the solenoid valve of the physical equipment). 4.4.2 production line level model integration Layout integration: In the "assembly" environment of NX, each equipment model is placed according to the actual layout of the physical workshop (the coordinate origin is aligned with the workshop ground reference point). Ensure the relative position accuracy (± 5mm) between the equipment through "distance constraint" and "angle constraint", for example, the docking gap between the injection molding machine and the conveyor belt is set to 10mm. Logical Integration: The "signal flow" module of NX MCD is used to define the control signal between the equipment: the signal from the top of the injection molding machine triggers the start of the conveyor belt, and the camera at the signal trigger detection station is photographed after the assembly is completed. Build virtual PLC logic: write control program in NX through "ladder diagram" to simulate the i/o point of physical PLC (for example, i0.0 is the ready signal of injection molding machine and q0.1 is the start signal of manipulator). Performance Optimization: Carry out "lightweight processing" on the model: delete invisible parts (such as internal cables of equipment), simplify surfaces (replace non critical surfaces with planes), and control the total number of triangular patches of the model within 5million; Adopt "hierarchical display" technology: automatically load/unload model details according to the simulation perspective to improve the real-time rendering efficiency (the frame rate is kept above 30fps). The final result is shown in Figure 5. Figure 5 Workshop digital twin model 3.Concern of the reviewer: In Section 4, the case study validation mentions cost savings (2.1 million yuan/year) and a 2% output increase but lacks control group data (e.g., comparisons with traditional production lines) and corresponding details. It is suggested to include details of the experimental design and statistical analysis methods. Our response: We are grateful to the Reviewer for his elaborate work to find out our weakness .We added chapter 7.2 operation effect to introduce the verification and comparison between digital twin model and traditional production in detail and adjusted the data. Revised text: 7.2 Operation effect (1) Twin model accuracy verification Geometric mapping accuracy: randomly select 10 base parts and measure the key dimensions (such as diameter and height) of the physical parts and the virtual model, with an average error of 0.08mm, meeting the design requirements (≤ 0.1mm). Behavior synchronization accuracy: record the physical execution time and virtual simulation time of the manipulator grasp action, and the synchronization error is 35ms, which is lower than the threshold (50ms). (2) Productivity improvement Virtual debugging cycle: traditional physical debugging takes 72 hours, while virtual debugging based on digital twin takes only 24 hours, with an efficiency increase of 66.7%; Equipment utilization rate: through real-time monitoring and scheduling optimization of virtual and real mapping, the equipment utilization rate increased from 85% to 92%, and the daily output increased to 5300 pieces. (3) Quality and cost optimization Quality in Attachments Attachment Submitted filename: Response to the Reviewers comments.docx https://doi.org/10.1371/journal.pone.0341224.r002
1 Sep 2025 Decision Letter - Himadri Majumder, Editor Dear Dr. Wu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== ACADEMIC EDITOR: ============================== Please submit your revised manuscript by Oct 16 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). 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Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Himadri Majumder, Ph.D Academic Editor PLOS ONE Journal Requirements: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: (No Response) ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ****** Reviewer #1: The paper focuses on the construction and implementation of a digital workshop for a smoke alarm base production line based on digital twin technology. Integrating this with actual production needs, it proposes key technologies such as system architecture, model building, and data acquisition and mapping, and verifies their effectiveness through a company case study. Overall, the paper has considerable application value, but it does have some shortcomings in structure, methodology, and presentation. 1、The innovations are rather engineering-oriented and lack theoretical depth. For example, while the construction of the digital twin model is detailed, it does not introduce novel algorithms (such as AI-driven predictive models) or cross-innovations with other fields (such as integration with edge computing or blockchain). The authors are advised to emphasize the differences with existing research, for example, through comparative analysis, highlighting the unique advantages of this approach (such as the efficient mapping of multi-source heterogeneous data). 2、There are minor grammatical errors and awkward phrasing, such as the use of "compared with transmission production" in the Abstract (a possible typo for "traditional production"). The reference to "untraceable quality issues" in the Introduction could be more precisely replaced with "untraceable quality defects." 3、Some of the results (such as an 8% decrease in energy consumption and a 3.2% increase in defective product identification rate) lack systematic comparison with existing research, making it difficult to highlight their advantages. 4、The discussion section is descriptive and lacks analysis of the rationality and limitations of the results; the mechanism of energy consumption and yield improvement is not discussed in detail, only the results are given. 5、Some paragraphs contain Chinglish, with lengthy sentences and insufficiently detailed diagram descriptions. ****** 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 ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org https://doi.org/10.1371/journal.pone.0341224.r003
Revision 2
13 Oct 2025 Author Response We thank you very much for giving us another chance to revise and improve ourmanuscript. We try our best to revise the paper according to the reviewers’suggestions, We have made changes to the proposed suggestions one by one and uploaded them as documents.We hope the revised paper has addressed all the reviewers’ concerns and reached the requirements of the journal for publishing. Attachments Attachment Submitted filename: Response to the Reviewers comments2.docx https://doi.org/10.1371/journal.pone.0341224.r004
10 Nov 2025 Decision Letter - Himadri Majumder, Editor Dear Dr. Wu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== ACADEMIC EDITOR: Based on the reviewers’ feedback, the paper has been evaluated as requiring minor revision before it can be reconsidered for publication. ============================== Please submit your revised manuscript by Dec 25 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Himadri Majumder, Ph.D Academic Editor PLOS ONE Journal Requirements: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: (No Response) ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: (No Response) ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: (No Response) ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: (No Response) ****** Reviewer #1: 1、The abstract contains repeated sentences: At the end of the abstract, the conclusion sentence was copied and pasted twice. 2、The introduction does not delve deeply enough into the research background and problems, and fails to fully explain why the production of smoke alarms is particularly well-suited for the application of digital twin technology, as well as the unique challenges that this industry faces compared to other manufacturing sectors in applying digital twins. 3、The terms "digital twin workshop" and "digital workshop" are used interchangeably. 4、It is recommended to deepen the theoretical innovation points and clearly distinguish the essential differences between the method in this paper and existing technologies. ****** 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 ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation. NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications. https://doi.org/10.1371/journal.pone.0341224.r005
Revision 3
14 Dec 2025 Author Response We sincerely appreciate you providing us with another opportunity to revise and improve our manuscript. We have diligently revised the paper in accordance with the reviewers’ suggestions and have provided detailed explanations for any comments with differing views. The modifications and our responses can be found in the document titled "Response to the Reviewers' comments." We hope that the revised manuscript has addressed all the reviewers' concerns and now meets the journal’s publication standards. Thank you very much! Attachments Attachment Submitted filename: Response to the Reviewers comments3.docx https://doi.org/10.1371/journal.pone.0341224.r006
6 Jan 2026 Decision Letter - Himadri Majumder, Editor Development and implementation of a Digital Twin workshop for a smoke alarm production line PONE-D-25-17830R3 Dear Dr. Wu, 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. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support . If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Himadri Majumder, Ph.D Academic Editor PLOS One Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes ****** Reviewer #1: I am satisfied with the revisions of the paper. I think this paper is acceptable in its current form. ****** 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 ******** https://doi.org/10.1371/journal.pone.0341224.r007
Formally Accepted
Acceptance Letter - Himadri Majumder, Editor PONE-D-25-17830R3 PLOS One Dear Dr. Wu, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Himadri Majumder Academic Editor PLOS One https://doi.org/10.1371/journal.pone.0341224.r008

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