11/14/2023 0 Comments Blender 2.79![]() ![]() I-nteract is a CPS that enables the user to interact with both the virtual as well as the physical objects (deformable and non-deformable) simultaneously in a visio-haptic mixed reality (VHMR) environment. Potential improvements in the AM workflow. ![]() In this context, innovations in the design of CPS and technological advancements in its supporting tools (IoT, mixed reality, cloud computing, robotics, machine learning) are playing an important role in the widespread adoption of AM by the general public as well as the industry. ![]() Moreover, most CAD design software programs not only require professional training but also restrain the design of 3D virtual models to 2D interfaces, making the design process unintuitive and cumbersome for non-technical consumers and, hence, limiting their involvement in the design phase to facilitate customisation. Hence, the entire loop is reiterated through a trial-error procedure until the desired results are achieved, making the design process costly and time-consuming. Therefore, in such a workflow, testing of the designed 3D model for the desired functionality is postponed to the end of the printing process. Finally, the manufactured product is inspected for the desired quality and conformance during the testing phase. Then, during the manufacturing phase, the 3D printer builds the physical object layer upon layer and post-processing is done either to remove support structures or to give the finishing touch to the 3D-printed product. ![]() It starts with the three-dimensional virtual model of the desired product designed via a computer-aided design (CAD) tool or obtained from 3D scanning in the design phase. A workflow of AM, depicted in Fig 1, consists of three phases. AM, also known as 3D printing, rapid prototyping, or generative manufacturing, refers to depositing successive thin layers of materials upon each other in precise geometric shapes based on 3D model files to manufacture three-dimensional physical objects. Additive manufacturing (AM), one of the main driving forces in the realisation of this fourth industrial revolution, has emerged during the last decade as a key enabling technology poised to deeply transform manufacturing. Industry 4.0 is a digital industrial revolution in which numerous emerging technologies are converging to provide digital solutions to achieve mass customisation with increased speed, better quality, and improved productivity. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting information files.įunding: This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under grant number 16/RC/3872 and is co-funded under the European Regional Development Fund and by I-Form industry partners. Received: OctoAccepted: JPublished: July 27, 2023Ĭopyright: © 2023 Malik et al. The presented developmental advances provide a novel and immersive form of interaction to facilitate the inclusion of a consumer into the design process for personal fabrication.Ĭitation: Malik A, Lhachemi H, Shorten R (2023) A cyber-physical system to design 3D models using mixed reality technologies and deep learning for additive manufacturing. The effectiveness of the system is demonstrated by generating 3D models of furniture (e.g., chairs and tables) and fitting them into the physical space in a mixed reality environment. The system also enables the user to adjust the dimensions of the 3D models with respect to their physical workspace. Furthermore, a novel generative neural architecture, SliceGen, has been proposed and integrated with the system to overcome the limitation of single-type genus 3D model generation imposed by differentiable-rendering-based deep neural architectures. In specific, by taking advantage of the generative capabilities of deep neural networks, the system has been automated to generate 3D models inferred from a single 2D image captured by the user. This paper presents novel advances in the development of the interaction platform to generate 3D models using both constructive solid geometry and artificial intelligence. I-nteract is a cyber-physical system that enables real-time interaction with both virtual and real artifacts to design 3D models for additive manufacturing by leveraging mixed-reality technologies. ![]()
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