A Grid-Based Secure Product Data Exchange for Cloud-Based Collaborative Design

Wu, Yonghong; He, Fazhi; Yang, Yueting

doi:10.1142/s0218843020400067

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…In a Cloud-Based Design and Manufacturing (CBDM) environment, the use of FBDE is common to allow multiple peers sharing Computer Aided Design (CAD) data [19], [20]. There are also various researches focus on extending the capability of FBDE such as security [21], collaboration [22], undo mechanism [23]. AR/VR/MR [24], and common 3D-information such as Buidling Information Modeling (BIM) technology [25], [26].…”

Section: Collaborative 3d Modelingmentioning

confidence: 99%

Dynamic Terrain Data Exchange in a Collaborative Terrain Editor

Tarigan

Sitompul

Zarlis

et al. 2022

IJCAI

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In a computer supported cooperative work (CSCW), data consistency between collaborating users is a crucial issue. Based on the type of the application, ensuring data consistency can be a lengthy process that takes time and affects the system's performance. In most 3D application, terrain data are massive due to its size. Exchanging this data may be expensive and may cause significant delay. In a real-time collaborative terrain editor, this issue becomes more significant due to terrain data exchange is consistently occurred between collaborating users. We present a solution to perform a conflict-free dynamic terrain data exchange in a real-time collaborative terrain editor. Our objective is to develop a method that able to ensure data consistency amongst collaborating peers in real-time manner. The main idea of our method is to split the terrain into smaller patches and synchronize the changes efficiently by only exchanging the modified patches. We applied our solution to a collaborative terrain editor application to test its performance in a real-time collaborative editing session. The tests were done in multiple scenarios, using different patch model, brush size (in the terrain editor), and connection setup between server and collaborating clients. The result shows that our protocol is capable to maintain data consistency between collaborating clients in a real-time terrain edition session. The delay is varied and highly depends on the data size and client-server environment setup. The overall test shows that it is possible to perform a collaborative terrain editing with an acceptable response time delay. In this paper, we present our proposed method, the implementation, and the result data from the test.Povzetek: V prispevku je opisana metoda za sprotno izmenjavo podatkov pri opisu dinamičnega terena.

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Section: Collaborative 3d Modelingmentioning

confidence: 99%

Dynamic Terrain Data Exchange in a Collaborative Terrain Editor

Tarigan

Sitompul

Zarlis

et al. 2022

IJCAI

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“…Inspired by the prior research on distributed deep-learning networks (Kang et al, 2020; Kaur and Bala, 2019; Wu et al, 2020), this paper develops a distributed cloud-computation strategy, which schedules computations for training on the clouds. In the meantime, the system also employs micro-controllers locally near targeted CNC machines.…”

Section: Cloud-computation Of a Cnn Deep-learning Networkmentioning

confidence: 99%

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

Lee

Lin

2021

Concurrent Engineering

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This paper suggests an optimization strategy to train a CNN deep-learning network, which successfully recognizing working status on the HMI panels of CNC machines. To verify the developed strategy, the research experiments using a prototype that consists of a CNC milling machine and an industrial robot. In the optimization strategy, the research first defines a length-varying hyperparameter list for the deep-learning network, and the entities in the list adjust themselves to optimize the model scales. During the optimization process, this paper adopts a two-stage training scheme that gradually augments image datasets to improve HMI control-panel recognition performances, such as recognition accuracy and recognition speed to identify the CNC machine working status. Using an open-source PyTorch platform, this research establishes a cloud-based distributed architecture to build training codes for the deep-learning network, in which an applicable optimization model is deployed to recognize the CNC control-panel working status. The optimization strategy employs minimal codes to rebuild the architecture and the least efforts to reform the manufacturing system. The optimally trained model provides up to a 99.34% CNC panel-message recognition accuracy and a high-speed recognition of 100 images in 0.6 s. Moreover, the developed optimization strategy enables the prediction of necessitated dataset augmentation to training a practically implemented CNN network.

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“…Many companies store large amounts of data in existing platforms, making switching to new platforms difficult. Additionally, many of the latest CAD/CAE tools are web-based, which can raise concerns about data security and privacy [6]. As a result, the industrial sector would benefit significantly if existing CAD/CAE tools could be adapted to the changing landscape of remote collaboration practices.…”

Section: Introductionmentioning

confidence: 99%

How Can XR Enhance Collaboration with CAD/CAE Tools in Remote Design Reviews?

Garcia Rivera,

Rostami,

Mattsson

et al. 2024

Advances in Transdisciplinary Engineering

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This study studies the challenges of effective communication and collaboration in remote design review meetings (DRMs) and explores the potential of Extended Reality (XR) technologies to address these challenges. The research focuses on identifying recurring communication issues and the preferences of companies within the context of remote DRMs. The study involves qualitative content analysis and industry workshops to uncover the current problems with conventional approaches and the aspirations of companies regarding improved collaboration in the DRM process. Drawing upon the insights gathered from both the workshop and design review observations, this paper highlights the features that are critical for collaborative software to handle online design reviews. XR technologies offer immersive and interactive experiences that can transform communication and collaboration in the context of DRMs. By identifying the specific challenges faced in remote DRMs and understanding the desires of companies, this study sets the stage for a more efficient and effective collaborative process. It emphasizes the adaptability of XR technologies to meet industry needs and integrate seamlessly into existing workflows. The study concludes by highlighting the potential for XR technologies to enhance collaboration in DRMs, making them a valuable tool for various industries.

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A Grid-Based Secure Product Data Exchange for Cloud-Based Collaborative Design

Cited by 11 publications

References 51 publications

Dynamic Terrain Data Exchange in a Collaborative Terrain Editor

Dynamic Terrain Data Exchange in a Collaborative Terrain Editor

An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network

How Can XR Enhance Collaboration with CAD/CAE Tools in Remote Design Reviews?

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