Get clarity with strain gauge measurement

What are strain gauges?

Strain gauges are used for practical measurements of the load condition of load affected components. Strain gauges are glued to an exposed surface area and will then deform together with the loaded component whereby the strain can be measured. Examples of materials that we can measure on:
  • Steel
  • Stainless steel
  • Aluminium
  • Composites

Why are strain gauges relevant to you?

Get clarity on loads in mechanical or structural components in order for the strength of the material to be used correctly or the deformation level to be kept within a desired limit state. Strain gauge measurements are used, among other things, where a deeper understanding of the construction is desired for the purpose of optimization. In other cases, live monitoring of tethering can be used as input for the control program of a regulation system, thus enabling an optimized process execution.

How do we use strain gauges together?

At Tech Invent, we can, for example, help with measurements and analyses in the following cases:
  • Verification of structures and structures
  • Clarity of the load on a structural element
  • Wireless measurement in the case of rotating components or shafts
  • Measurements of pressure equipment
  • Optimization of a simplified FE model or as a supplement to an FE modeling
  • Use of strain gauges in combination with situational data processing
  • Incorporation of strain gauges into a specially developed product
  • Mechanical performance analysis and frequency analysis
  • Live monitoring of stresses from simultaneous measurements
Strain gauges can be built into machines and equipment as a permanent solution whereby the controller can take component loads into account in a live scenario. Remote monitoring can provide an overview in cases where many units are monitored simultaneously and provide benefits where the equipment or machines are difficult to access. Strain gauge measurements can make it possible to make a simple FE model work correctly by revising the model’s setup parameters and assumptions based on the observations.