Case Study with Welding Alloys Group
Wear is one of the most challenging problems faced by heavy industry. One of the most common methods to combat wear is by welding highly alloyed consumables (hardfacing materials) onto the surfaces of machinery components. 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 identification of a new hardfacing material with considerable environmental and cost/benefit advantages.
Jean-Marie Bonnel and Mario Cordero, leaders of the project at Welding Alloys Group, agree that:
“The combination of extensive technical know-how and experimental data provided by WAG, and the unique deep learning algorithms provided by Intellegens, resulted in a new composition that deviates considerably from any materials used in the past.”
Wear is a very complex phenomenon. The common conception that high hardness secures high wear-resistant properties is misleading. Optimum wear resistance is defined by a complex interaction of chemical and mechanical properties of every material involved in the application. These properties include composition, hardness, toughness, Young’s modulus, grain size, and phase composition. External parameters such as temperature, pressure, and humidity also play important roles.
In addition, random and/or systematic variations on wires and hardfacing welding procedures result in finished products with large discrepancies in performance. Environmental costs due to excessive use of highly polluting Chromium (Cr) are also a big concern, as well as the increasingly strict environmental regulations that push for lean welding consumables.
The goal of this project was to take one high Cr cast iron-based welding consumable and to optimise cost/benefit as a function of chemical composition, based on abrasion-resistant standard methods as a performance metric, using the Alchemite machine learning toolkit.