Generative design of 3D printed hands-free door handles for reduction of contagion risk in public buildings

During the emergency caused by COVID 19 evidence has been provided about the risk of easily getting the virus by touching contaminated surfaces and then by touching eyes, mouth, or nose with infected hands. In view of the restarting of daily activities in presence, it is paramount to put in place any strategy that, in addition to social distancing, is capable to positively impact on the safety levels in public buildings by reducing such risk. The main aim of this paper is to conceive a design methodology, based on a digital, flawless, and sustainable procedure, for producing human-building interfacing solutions that allow anybody to interact in a safer and more comfortable way. Such solutions are focused on the adaptation of existing buildings features and are thought to be an alternative to sensor based touchless technology when this is not applicable due to economic or time constraints. The process is based on the integration of digital technologies such as 3D Scanning, Generative Design and Additive Manufacturing and is optimised to be intuitive and to be adaptive, hence, to be replicable on different kinds of surfaces. The design concept is finalised to generate automatically different products that meet geometry fitting requirements and therefore adapt to the specific geometries of existing handles. A specific case on Hands Free Door Handles is presented and the results of manufacturing and preliminary validation process are provided and discussed.

Duck Optimization for Additive Manufacturing Using Generative Design Fluids Path in Fusion360

Generative Fluid in Fusion 360 is the recently launched cutting edge technology which is revolutionary for those companies which produces parts and components working on fluid. They always thrive for weight reduction and minimum pressure drop of their components along with no sacrifice at their performance. This can now be done by this new technology at their specified rate. But the cost of running one simulation is equitable for design which it gives to us. Keyword: 1. Additive Manufacturing, 2. Computational fluid dynamics, 3. Computer aided design, 4. Generative Design, 5. Topology optimization 6. fluid mechanics

MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization

Design and manufacturing processes are entering into a new era as novel methods and techniques are constantly introduced. Currently, 3D printing is already established in the production processes of several industries while more are continuously being added. At the same time, topology optimization has become part of the design procedure of various industries, such as automotive and aeronautical. Parametric design has been gaining ground in the architectural design literature in the past years. Generative design is introduced as the contemporary design process that relies on the utilization of algorithms for creating several forms that respect structural and architectural constraints imposed, among others, by the design codes and/or as defined by the designer. In this study, a novel generative design framework labeled as MLGen is presented. MLGen integrates machine learning into the generative design practice. MLGen is able to generate multiple optimized solutions which vary in shape but are equivalent in terms of performance criteria. The output of the proposed framework is exported in a format that can be handled by 3D printers. The ability of MLGen to efficiently handle different problems is validated via testing on several benchmark topology optimization problems frequently employed in the literature.

GENERATIVE DESIGN OF STRUCTURAL STEEL JOINTS

Structural project is based on technical regulations, structural codes, construction conditions, and client requirements. Through the structural design process, some important decisions that can significantly affect the final result must be implemented. The most important factor for optimal design is the reduction in material and overall work costs. Selecting appropriate joint configurations that can reduce the overall weight and work on the structure is critical. To examine a significant number of possible configurations and their effect on structural behavior, the generative design method (GDM) is used. In this study, software is custom developed, and a relevant example of generative joint structural design is provided. The methodology for the optimal joint and structure design is described comprehensively. The final results show that the GDM is an effective methodology for application in the design of steel structures.