journal article·2018-08

Modelling the Dynamic Behaviour of a Friction-Type Mechanical Shock Indicator

V. Rouillard, M.J. Lamb, M.A. Sek

Experimental Techniques· Volume 42· Issue 4
Publisher page

Abstract

This paper deals with the challenges associated with modelling a friction-type shock indicator used during product distribution. Cost-effective mechanical shock indicators are widely used to reveal if a package has been subjected to a shock event exceeding a predetermined acceleration threshold. Such devices are an important part of the quality control and monitoring process during the distribution of fragile products. However, there is growing evidence that, in many cases, false detection or no detection have been experienced producing misleading information hence loss of confidence in the data. This paper describes the development and validation of a mathematical and numerical model that fully characterises the behaviour of a mechanical shock detector device. The device uses the displacement of a magnet when a pre-determined acceleration threshold is exceeded. The aim of the model is to assist in understanding the mechanical behaviour of the device and how it responds to different types of impulsive excitation. Modelling friction with a unary coefficient was found to have shortcomings and a more sophisticated approach to predict the effects of friction was developed. The numerical model was validated by producing a variety of excitation motions using a programmable actuator. The paper discusses the challenges associated with experimentally establishing the mechanical parameters of the device, especially friction coefficients, and shows that an adoption of the Stribeck friction model to characterise the motion of the magnet produces accurate results.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Gear and Bearing Dynamics Analysis · Dynamics and Control of Mechanical Systems · Adhesion, Friction, and Surface Interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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