Harshwork develops software that will produce reliable predictions on wear

Harsh conditions in mines wears out the strongest equipment, but with a good prediction of wear it is possible to design more durable components and to predict when reparation is needed, thus save time and money.

Our aim is to fulfil the need for customers to tell where, and how much, the wear from granular material will affect the equipment. By the end of the Harshwork project, we can validate and confirm that our novel software model is working for at least three different industrial cases.

Pär Jonsén, Professor at Luleå University of Technology

Jonsén is leading the Harshwork project and with his colleagues from Bianna Recycling, Boliden Mineral, Fundació CTM Centre Technologic in Spain, IDP lngenieria y Arquitectura Iberia in Spain, Outotec and SSAB he has a thorough understanding of how wear affects different materials. The team is developing a software model that will increase the efficiency of handling parts working in harsh environments by predicting and quantifying wear.

The software is of keen interest to mining companies and mining equipment manufacturers, as well as a wider range of steelmakers, engineering and recycling sector. Hence, part failure by wear being one of the most relevant sources for part malfunction in the industry. Apart from the cost of part reparation or substitution, wear failure in large industrial parts has an important economical impact on production efficiency because of production losses during machinery downtime.

Good predictions save time and money

Parts in mining industry machinery are usually subjected to harsh working conditions, with high forces, abrasive media, low temperature and big impacts. All these conditions give rise to severe wear damage in many parts of the machinery, with the associate high costs for maintenance and substitution.

Therefore, a software that can predict the level of wear and localize and quantify wear would help to define the need for maintenance of large industrial parts, e.g. those used in mining industry.

A good prediction of wear will help the customer to choose more suitable materials in their production and plan for more efficient maintenance of the machines and equipment. This means that they can optimize exploitation costs by reducing maintenance periods, decreasing costs and increasing performance of machinery.

Pär Jonsén, Professor at Luleå University of Technology

Harshwork is developing a software that will produce reliable predictions on wear. This software will save time for equipment designers and producers by giving them a chance to design the equipment even ‘better’, e.g. make the components more durable and use better materials.

The support we have received from EIT RawMaterials has been necessary for us to carry out measurement and develop and scale up the solution. Furthermore, industrial application has been possible thanks to the EIT RawMaterials partner network. Generally, there are few funding possibilities for applied research projects like these in our industry.

Pär Jonsén, Professor at Luleå University of Technology

Read more about the Harshwork project.