The project The market for truck and agricultural tires is very diverse. It ranges from light passenger vehicles, like minivans, up to heavy-duty construction and 18 wheel long-haul tractor trailers.
Although the market is very different in all cases the truck industry is driven by today’s legislation and economic climate more and more towards environmental aspects. Car manufacturer require three main qualities in tires: low rolling resistance (improve fuel efficiency) excellent grip (improve safety) and better durability (extending lifetimes).
The issue of wear represents a very important role in the functionality of products. As the tyre tread is in direct contact with the road it is disposed to the abrasive effect of the road. The abrasion is very dependent of the load, the driving force transfer and many other factors. Besides that truck tyres and agricultural tyres, which are used on very hard terrain conditions, are also exposed to sharp stone edges and terrain irregularities and gradually cut parts of the rubber tread surfaces. This is also known as chip and chunk.
The tire wear is usually tested under running conditions. This makes the test very time-consuming and expensive. To get reliable results tyres need to be tested for at least 6 months. The most known test in the industry is the BF Goodrich cut and chip tester. This test however does not correlate with the wear data obtained by field tests. Therefore it is useful to have a quick and reliable test equipment.
The main goal is to design of a new equipment and test method for chip and chunk prediction of TBR and agricultural tyres.
The deliverables of this PDEng project are:
Literature and overview of existing methods
Outdoor wear test conditions and analysis TBR and agricultural tires with chip and chunk.
Phenomena investigation.
Design of new equipment and test method or methods for it.
Final correlation of new predictive lab test and chip and chunk outdoor data for TBR and agricultural tyres.
Further details:
PDEng - Design of a new equipment and test method for chip and chunk prediction of TBR and agricultural tyres at University of Twente