Surface roughness evaluation in hardened materials by pattern recognition using network theory

Babič, Matej; Calı̀, Michele; Nazarenko, Ivan; Fragassa, Cristiano; Ekinović, Sabahudin; Mihaliková, Mária; Janjić, Mileta; Belič, I.

doi:10.1007/s12008-018-0507-3

Cited by 21 publications

(15 citation statements)

References 21 publications

(14 reference statements)

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“…In the meantime, also considering the hypothesis where additional tests are not imaginable in short, a different approach was here proposed for data analysis. It is in line with another recent paper by some of the authors [54] where ML algorithms were used for the evaluation of the surface roughness evaluation in steel after thermal hardening by laser radiation. In brief, the validation is performed on the full dataset of values, not on part of it.…”

Section: Prediction Model Validationsupporting

confidence: 89%

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

Fragassa

Babič

Bergmann

et al. 2019

Metals

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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.

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Section: Prediction Model Validationsupporting

confidence: 89%

“…In this research, in line with previous experiences where the ML has been applied to the prediction of metal properties [54], the analysis has been limited to the use of the following methods:…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

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

Fragassa

Babič

Bergmann

et al. 2019

Metals

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“…Several methods of ML can be conveniently considered. In the present work, according to preceding similar experiences, as detailed in [2], the following ones were preferred.…”

Section: Evaluation Methodsmentioning

confidence: 99%

Evaluation of Hardness in Cast Iron: How Simple It Is !

Fragassa¹,

Babič²,

Pavlović³

2019

PES

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The accurate prediction of the mechanical properties of foundry alloys is a rather complex charge given the substantial variability of metallurgical conditions that can be created during casting even in the presence of minimal variations in the constituents and in the process parameters. In this study an application of intelligent methods, based on the machine learning, to the estimation of the hardness of a traditional spheroidal cast iron and a less common compact graphite cast iron is proposed. Microstructures are used as inputs to train the neural networks, while hardness is obtained as outputs. As general result, it is possible to admit that 'light' open source self-learning algorithms, combined with databases consisting of about 20-30 measures are already able to predict hardness properties with errors below 15%.

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“…A large choice of models and algorithms have been developed in the years [20]. Between many others, the following ones were preferred in this investigation in consideration of valid results emerged in previous similar researches [21].…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Approaches to Predict the Hardness of Cast Iron

Fragassa¹,

Babič²,

Santos³

2020

Tribol. Ind.

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The accurate prediction of the mechanical properties of foundry alloys is a rather complex task given the substantial variability of metallurgical conditions that can be created during casting even in the presence of minimal variations in the constituents and in the process parameters. In this study an application of different intelligent methods of classification, based on the machine learning, to the estimation of the hardness of a traditional spheroidal cast iron and of a less common compact graphite cast iron is proposed. Microstructures are used as inputs to train the neural networks, while hardness is obtained as outputs. As general result, it is possible to admit that 'light' open source self-learning algorithms, combined with databases consisting of about 20-30 measures are already able to predict hardness properties with errors below 15 %.

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Surface roughness evaluation in hardened materials by pattern recognition using network theory

Cited by 21 publications

References 21 publications

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

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

Evaluation of Hardness in Cast Iron: How Simple It Is !

Machine Learning Approaches to Predict the Hardness of Cast Iron

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