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


Performance characteristics of the products made of metallic materials such as wear resistance, fatigue strength, stability of gaps and strain between the connections, corrosion resistance, etc., depend to a large extent by the quality of their surfaces roughness. An interactive control of the manufacturing parameters which influence the surface roughness is particularly crucial in the construction of many mechanical components. The present paper devises a new method for statistical pattern recognition on samples produced by the process of robot laser hardening using network theory and describes its application to the determination of surface roughness. The method is based on the analysis of SEM images. Indeed the data characterizing the state of surface irregularities detected as extremely small segments contain indicators of surface roughness. Different methods of machine learning techniques designed to predict the surface roughness of robot laser hardened material are discussed.

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Correspondence to Matej Babič.

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Babič, M., Calì, M., Nazarenko, I. et al. Surface roughness evaluation in hardened materials by pattern recognition using network theory. Int J Interact Des Manuf 13, 211–219 (2019).

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  • Surface roughness
  • Machine interactive learning
  • Statistical pattern recognition
  • Robot laser hardening
  • SEM images