Weld Rebuild On Con Crusher For Mining Site

Wear is one of the most challenging problems faced by heavy industry. For example, in the mining industry alone, approximately 17% of the consumed energy is used to combat wear failure, accounting for 2.7% of global CO emissions . If these figures are multiplied for every sector (construction, oil, and gas, etc.) the scale of the problem becomes evident. Thus avoiding or minimising wear is a top priority in industry.

One of the most common methods to combat wear is by welding highly alloyed consumables (hardfacing materials) onto the surfaces of components. These consumables must meet stringent requirements on safety, cost, environmental impact, and performance. Wear performance is determined by a complex interaction of properties, therefore optimising cost/benefit for hardfacing materials is a highly complex operation.

This case study describes the journey taken by Welding Alloys Group (WAG) and Intellegens in applying machine learning to this problem, which resulted in the development of an improved hardfacing material with drastic cost/benefit advantages, not only from a performance but also from an environmental point of view.