Instron Sets Out Case for High Strain Rate Data in Simulation and Material Selection

An Instron application engineer argues that strain-rate-dependent material data can make crash simulations more representative and sharpen weight-versus-performance material choices.

High strain rate testing can give engineers more representative material data for simulation, according to Andrea Incardona, application engineer and product specialist at testing machine manufacturer Instron, who sets out the case in an article published by the company.

Incardona notes that many materials have strain-rate-dependent properties, so a material characterised under quasi-static conditions may respond differently in a rapid impact or crash event. He points to a 2025 study in the SAE International Journal of Materials and Manufacturing, 'Influence of Strain Rate Effect on Mechanical and Crashworthiness Properties of CFRP Composite Structures', which found that adding strain-rate-dependent properties to crash simulations reduced discrepancies between simulated and experimental results. Peak acceleration discrepancy fell by 6.5 per cent, while mean acceleration and displacement discrepancies fell by 5.9 and 6.3 per cent respectively.

Capturing millisecond events

According to Instron, materials can be characterised from quasi-static conditions up to strain rates approaching 1,000/s, depending on the material, specimen geometry and test method. Individual tests may last only 3 to 10 milliseconds, which places heavy demands on data acquisition. Instron says its high strain rate systems support acquisition at up to 200 kHz, and that digital image correlation (DIC) can add information on how strain develops across the specimen.

Selection and development

The company argues that conventional strength and stiffness values do not show how a material behaves once loading becomes rapid, and that two materials which appear comparable in conventional testing may diverge under high strain rate conditions. Dynamic data on deformation, energy absorption and failure could therefore inform weight-versus-performance trade-offs, for example in automotive crash structures or aerospace components.

In Instron's account, dynamic characterisation fits into a development loop in which material behaviour is measured, fed into simulation, used to compare design options and then validated through physical testing, placing materials testing earlier in the process rather than treating it as a final check.

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Chidi Okonkwo

Chidi Okonkwo covers factory automation and motion systems, including industrial robot cells, PLC-controlled assembly lines, servo drives, and machine vision inspection stations. His coverage works from measurable parameters such as cycle time, positioning repeatability, payload and reach margins, drive sizing calculations, and safety-related control boundaries rather than unverified throughput claims. He writes for automation engineers, manufacturing engineering directors, and capital procurement managers who must compare cell designs, specify interfaces, and accept a line against quantified commissioning results.