May 19, 2021 · In the spirit of ‘better and faster,’ deep learning is entering the engineering simulation and design optimization workflow to reduce time-to-market and increase product quality.

Jun 1, 2022 · Deep reinforcement learning was used to find the optimal sequences of design decisions to optimize elementally discretized structures. This work offers a gradient-free approach to solving the compliance minimization topology optimization problem.

Dec 14, 2022 · Using deep learning in product development has important implications for companies that go beyond their technology choices, however. It will require engineering departments to adapt their organizations and allocate resources in new ways.

Nov 18, 2021 · In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied...

Mar 1, 2019 · “We’re seeing users apply deep learning and have been astounded by the variety of applications in design engineering workflows.” Engineers are also incorporating deep learning into products they design, using speech recognition, language processing and imaging processing.

Mar 30, 2025 · We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact ...

Deep learning can assist CAD designers in several ways during design iterations. Delivering detailed 3D engineering predictions can suggest modifications to existing designs based on the designer's references (KPIs). It can even be pushed up to the point of being in charge of iterative tasks in design optimization.

Jun 14, 2021 · Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD designs and evaluates their engineering performance.

design space and quickly come up with solutions to emerging challenges. Organizations need to be able to rapidly iterate, to quickly test and validate those iterations, and then allow non-speciali.

Nov 18, 2021 · In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process.