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Automating Parametric Redesign of Structural Thinwalled Frames from Topology Optimization Results

This presentation was made at NAFEMS Americas Seminar "Engineering Analysis & Simulation in the Automotive Industry: Creating the Next Generation Vehicle Accurate Modelling for Tomorrow's Technologies".

The automotive engineering community is now confronting the largest technology transformation since its inception. This includes the electrification of powertrains for more efficient consumption and cleaner emissions, the reinvention of the battery with fast wireless charging capabilities and finally the advent of a fully autonomous vehicle. Compounding to these technology changes, the automotive companies design verification process is moving away from a major reliance on physical testing to almost a full virtual simulation product verification process. The challenges to the automotive engineers are enormous and require a significant increase in the upfront use of numerical simulation capabilities, methods and processes such they’re able to efficiently design, manufacture and deliver these very innovative technologies to the market in greater speeds than ever before.

Resource Abstract

The authors will describe why Intelligent Simulation Automation (ISA) is an essential component of a widening technology landscape in the automotive industry, required to implement a successful transition from a major reliance on physical testing to almost a full virtual simulation product verification process. ISA is also key to enabling a wide range of other strategic priorities including Digital Thread traceability within the PLM backbone, Digital Twin analysis for predictive maintenance and design improvements, design space exploration, generative design, and the quality assessment of additive manufactured products. At their core, each of these requires mainstreaming simulation automation and data management to work robustly across significant (and unpredictable) design changes and entire product families that share common functional/architectural characteristics. The current, manual, inefficient and silo’ed simulation process must be replaced by CAD-enabled, “lights-out” automation that is safely accessible across the product development team – Intelligent Simulation Automation enables this.



The desire to automate simulation processes has existed for decades. The technique of choice is often scripting/programming, with unsatisfactory results, limited repeatability and minimal ROI. The ad hoc nature of this approach has resulted in fragmented solutions that do not work well across the entire design space, are difficult to comprehend, and isolated from other product information.



Since the 1990’s, optimization (PIDO) tools, have provided “process integration” to automate simulation steps. However, design changes, essential for any design space exploration (DSE), rely on automatically editing model files without semantic knowledge of their content which significantly limits the design change scope that can be explored at higher fidelity.



The authors will describe why more effective enterprise-wide SPDM is foundational to achieving closed-loop traceability with requirements, test results, and design data and Intelligent Simulation Automation which better comprehends design changes. ISA is a fundamentally different approach that works robustly across significant design changes and across an entire product family, while supporting the appropriate level of mixed-fidelity models from 0-D through 3-D and the various physics. Different from the scripting and PIDO approaches, the introduction of a neutral CAE data model directly into the PLM platform for SPDM provides an abstract model which significantly expands the design scope of the automation templates, enabling analysts to focus on real simulation challenges instead of administration.



The authors will present use cases in the automotive, electronics and heavy equipment industries to demonstrate how various companies have achieved ROI using Intelligent Simulation Automation.

Document Details

ReferenceS_Nov_18_Americas_22
AuthorWang. L
LanguageEnglish
TypePresentation
Date 8th November 2018
OrganisationThe Ohio State University
RegionAmericas

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