Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Cristiano Fragassa,Giangiacomo Minak,Carlos Perez Bergmann,Matej Babic

doi:10.3390/met9050557

Abstract

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

Highlights

An accurate knowledge of the mechanical properties of materials represents the first step towards their correct use in any field of engineering and in everyday life
- information can be directly taken from micrographs by a conventional process of image comparing measures and predictions with respect to their mean values (μ)/standard deviations (σ) analysis and macro-indicators without the need to go through deeper metallurgical (Table 7) and by the overall trend of the Pearson coefficients (Table 8)
-thisthe average experiments and predictions often seems true,values even inofthe presence of a quite limited(measured dataset to by be μ) used forcoincide training;in practice; the deviation with respect to the average values shows variability in information can be directly taken from micrographs by a conventional process ofaimage analysis in line with the intrinsic as revealed the experimental measurements; andprediction macro-indicators without the needvariability to go through deeperby metallurgical investigations;

Summary

Introduction

An accurate knowledge of the mechanical properties of materials represents the first step towards their correct use in any field of engineering and in everyday life. Thanks to this information, for instance, it is possible to design structures and components in order to optimize their functionality according to technical parameters of specific interest such as strength, weight, safety, costs and so forth [1,2,3,4]. In the case of metals and, especially, of rather common cast irons [5,6,7], the shared opinion is that their properties are quite predictable (e.g., fatigue [8], abrasion [9], fracture [10], strength [11]). Since metals are widely known and used, low unpredictability in their properties is expected but quite common metal alloys, such as cast irons, can be affected by a not negligible variability in their essential properties [12,13]

Methods

Results

Discussion

Conclusion