Generative Design

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How IGESTEK Produces 40% Lighter Automotive Parts

Autonomous Design Automation: How Far Are We?


Author: Frank Schirrmeister

Topics: Generative Design

Organizations: Cadence

As an industry, we will refine the different levels of Autonomous Design Automation further over the years to come. Eventually, the combination of the different steps of the flow with AI/ML will unlock even further productivity improvements. How long will it be until designers define a function in a higher-level language like SysML and, based on the designer’s requirements, autonomously implement it as a hardware/software system after AI/ML-controlled design-space exploration?

Read more at Semi Engineering

Improving PPA In Complex Designs With AI


Author: John Koon

Topics: Reinforcement Learning, Generative Design

Vertical: Semiconductor

Organizations: Google, Cadence, Synopsys

The goal of chip design always has been to optimize power, performance, and area (PPA), but results can vary greatly even with the best tools and highly experienced engineering teams. AI works best in design when the problem is clearly defined in a way that AI can understand. So an IC designer must first see if there is a problem that can be tied to a system’s ability to adapt to, learn, and generalize knowledge/rules, and then apply these knowledge/rules to an unfamiliar scenario.

Read more at Semiconductor Engineering

Calculating the best shapes for things to come


Topics: Generative Design

Organizations: University of Michigan, Northeastern University

Maximizing the performance and efficiency of structures—everything from bridges to computer components—can be achieved by design with a new algorithm developed by researchers at the University of Michigan and Northeastern University. It’s an advancement likely to benefit a host of industries where costly and time-consuming trial-and-error testing is necessary to determine the optimal design. As an example, look at the current U.S. infrastructure challenge—a looming $2.5 trillion backlog that will need to be addressed with taxpayer dollars.

Read more at University of Michigan News

Generative Design for Milling Lightweights EV Motorbike Part


Topics: Generative Design, Additive Manufacturing

Organizations: Autodesk

Generative design software uses a set of user-input parameters and constraints to develop efficient part designs. These shapes are often organic forms no human would design on their own, and in its earliest years generative design was locked to additive manufacturing and production methods facilitated by additive manufacturing. Not long after Lightning and Autodesk developed their first iteration of the generatively designed motorcycle swing arm, Autodesk updated its solver to support milling and other conventional manufacturing methods. Design candidates generated for milling generally cannot reach the same level of optimization as their AM siblings, but they are much easier to manufacture while still reducing the weight of the part.

Read more at Modern Machine Shop

What Is Generative Design, and How Can It Be Used in Manufacturing?


Topics: generative design

Organizations: Autodesk

The primary use case of generative design in manufacturing is to automatically trigger design options that are pre-validated to meet the requirements you’ve established. That can be especially important for efficient manufacturing. Sometimes a part or tool must fit into an entrenched workflow or pipeline—methodologically or physically—as part of a larger device or process.

Read more at Redshift by Autodesk

Rolls-Royce Finds New-Engine Benefits in Old Test Data


Author: Jim Camillo

Topics: generative design, failure analysis

Organizations: Rolls-Royce, Altair

The goal, according to Peter Wehle, head of innovation, research and testing at RRD, is to use this information to reduce new-engine weight and mass, while maintaining structural integrity.

Both parties are hopeful that using ML and AI will significantly reduce the number of sensors needed to obtain present and future data, thereby saving RRD millions of euros annually. According to Mahalingam, the software lets engineers choose the data they want from a data silo, select the algorithms they want to employ and decide whether or not they want to use a neural network to train an ML model.

Wehle notes that the disruptive tool is based on the interaction between a communication endpoint of the engine simulation and neighboring points. It carefully analyzes the effects of loads on physical structures.

Read more at Assembly

Accelerating the Design of Automotive Catalyst Products Using Machine Learning


Authors: Tom Whitehead, Flora Chen, Christopher Daly, Gareth Conduit

Topics: generative design, machine learning

Vertical: Automotive

Organizations: Intellegens, Johnson Matthey

The design of catalyst products to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. We used machine learning to accurately model 16 key performance targets for catalyst products, enabling detailed understanding of the factors governing catalyst performance and realistic suggestions of future experiments to rapidly develop more effective products. The proposed formulations are currently undergoing experimental validation.

Read more at Ingenta Connect

How Machine Learning Techniques Can Help Engineers Design Better Products


Topics: machine learning, generative design

Organizations: Altair

By leveraging field predictive ML models engineers can explore more options without the use of a solver when designing different components and parts, saving time and resources. This ultimately produces higher quality results that can then be used to make more informed decisions throughout the design process.

Read more at Altair Engineering

Evolutionary Algorithms: How Natural Selection Beats Human Design


Author: @OzdDerya

Topics: AI, generative design

Vertical: Aerospace

Organizations: NASA

An evolutionary algorithm, which is a subset of evolutionary computation, can be defined as a “population-based metaheuristic optimization algorithm.” These nature-inspired algorithms evolve populations of experimental solutions through numerous generations by using the basic principles of evolutionary biology such as reproduction, mutation, recombination, and selection.

Read more at Interesting Engineering