How to describe Generative Design of Robotic Gripper for Additive Manufacturing Implementation?
Best insight from top research papers
Generative design of robotic grippers for additive manufacturing implementation involves using rapid-manufacturing and design optimization techniques to create grippers that can stably grasp a variety of objects. The design process takes into account the object's positioning with respect to the robotic arm and generates a 3D printable gripper that can adapt to different shapes . The gripper design is flexible and can handle parts with different sizes and shapes, making it suitable for today's industry . The manufacturing of the gripper can be done using additive manufacturing techniques, such as 3D printing, with different materials and printing parameters analyzed for optimal results . The use of inexpensive and readily available filaments, such as PLA and TPU, allows for the fabrication of the gripper components on a hobby-grade 3D printer, making it a cost-effective solution . The resulting gripper design is modular, easy to assemble, and maintain, with high mechanical adaptability and durability .
Answers from top 4 papers
More filters
| Papers (4) | Insight |
|---|---|
26 May 2023 | The provided paper does not specifically describe the generative design of a robotic gripper for additive manufacturing implementation. The paper focuses on the design and fabrication of an adaptive gripper using 3D printing technology. |
| The paper proposes a generative design algorithm that uses rapid-manufacturing and design optimization to create custom 3D printable passive grippers for robotic arms. | |
| The provided paper does not discuss the generative design of a robotic gripper for additive manufacturing implementation. | |
| The provided paper does not specifically describe the generative design of a robotic gripper for additive manufacturing implementation. |
Related Questions
Which paper uses generative model for generating mechanical structures?5 answersThe paper by Jun Wang, Wei Chen, Mark Fuge, and Rahul Rai utilizes a generative model for generating mechanical structures. This particular study focuses on creating variable-density cellular structures using a deep generative model that can accurately learn and generate diverse cellular unit cells conditioned on desired mechanical properties like Young's modulus and Poisson's ratio. By employing implicit function-based unit cells and conditional generative adversarial networks, this approach showcases the ability to generate unit cells that satisfy given material properties with high accuracy, create functionally graded cellular structures with high-quality interface connectivity, and improve structural performance over conventional topology-optimized variable-density structures.
What are the benefits of generative AI for the field of robotics?4 answersGenerative AI offers significant benefits to the field of robotics by enhancing various applications. In industrial settings, generative adversarial networks aid in maintaining object detection model performance by generating data with new distributions, reducing costs, and improving model accuracy. Furthermore, in robotic teleoperation over high-latency communication links, generative models enable the real-time rendering of photo-realistic images for human operators based on remote robot data, facilitating efficient teleoperation even in dynamic environments with new objects. Additionally, the integration of generative AI in vehicular networks enhances navigation optimization, traffic prediction, and data generation, albeit facing challenges like real-time processing and privacy concerns. These applications showcase how generative AI technologies positively impact robotics by improving performance, reducing costs, and enabling advanced decision-making processes.
What is the current state of research on generative design in mechanical engineering?5 answersGenerative design in mechanical engineering is an emerging field that utilizes artificial intelligence (AI) and deep learning (DL) techniques to optimize design solutions. It aims to generate high-performing designs that meet specific constraints. The use of topology optimization (TO) is common in generative design, but it often struggles to integrate layout geometry-related constraints and propose printable shapes. DL-TO, a DL-layout-driven solution, addresses this limitation by integrating layout and mechanical constraints at the conceptual level. It uses a triple-discriminator generative adversarial network (GAN) framework to generate mechanically valid designs that conform to layout constraints in a fraction of a second. Generative design has shown significant improvements in the machine-producing side of the business, offering ease of adjustments, stronger resilience, and fewer defects. It has also been applied to the design of soft robots, where it combines various numerical techniques to produce functional designs in a reduced design time. Additionally, generative design has been used to optimize the design of an airless tire for a space exploration rover, resulting in a significantly lighter design that meets the required loading conditions.
Does Generative AI improve manufacturing processes ?5 answersGenerative AI has the potential to improve manufacturing processes by enabling predictive maintenance, improving quality control, optimizing the supply chain and manufacturing processes, enabling mass customization, facilitating autonomous operations, and promoting sustainability. In the context of the manufacturing industry, artificial intelligence technologies, such as deep learning models and genetic algorithms, have been applied to forecast temperature and insulation values of motors from CNC machines. Additionally, the utilization of machine learning and artificial intelligence in semiconductor manufacturing processes can lead to efficient and intelligent automation, providing advanced solutions for controlling manufacturing processes and accessing effective predictive technologies. Therefore, generative AI has the potential to significantly improve manufacturing processes by leveraging advanced analytics approaches and embracing machine learning and artificial intelligence techniques.
How use generative AI tool for representation and architectural design processes?5 answersGenerative AI tools are being explored for representation and architectural design processes. These tools, such as image AI packages and AI-assisted frameworks, have the potential to enhance the designer's capacity to explore design options and inform early-stage 3D design. They can provide fast insights to designers using learned experiences, simulations, or datasets, expanding the solution space using expertise from previous knowledge. By combining AI networks with other computational tools, designers can engage in a different kind of design exploration, with the prospect of novel solutions heavily dependent on the interconnectedness of methods and the dataset curation process. The iterative interplay between human designers and AI collaborators facilitated by these tools can aid in conceptual form exploration and early concept exploration for practical design scenarios. While AI technology can generate design proposals and mood boards, it is seen as a complement to the architect's role in interpreting and executing the project, rather than a replacement.
What is Generative Design in terms of applied art?5 answersGenerative design in terms of applied art refers to the construction of systems by artists that have varying degrees of autonomy and are designed to generate results that would otherwise be unli. It is a fast-growing design approach that utilizes algorithms to automate design processes and test multiple complex alternatives. In the field of urban design, generative design methodologies can be classified into four categories: deforming or distorting a basic shape, simulating real urban growth, dealing with the urban form on the scale of building blocks, and analyzing a realistic spatial structure and proposing modifications based on the results. In the context of structural engineering, generative design is used to optimize the amount of material for structural elements. In the context of CAD models, generative design involves capturing design knowledge into a set of design elements to automate the creation of required design variants.