Automated measurement of sintering degree in optical microscopy through image analysis of particle joins
2015 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 11, 3451-3465 p.Article in journal (Refereed) Published
In general terms, sintering describes the bonding of particles into a more coherent structure, where joins form between packed particles, usually as a result of heating. Characterization of sintering is an important topic in the fields of metallurgy, steel, iron ore pellets, ceramics, and snow for understanding material properties and material strength. Characterization using image analysis has been applied in a number of these fields but is either semi-automatic, requiring human interaction in the analysis, or based on statistical sampling and stereology to characterize the sample. This paper presents a novel fully automatic image analysis algorithm to analyze and determine the degree of sintering based on analysis of the particle joins and structure. Quantitative image analysis of the sintering degree is demonstrated for samples of iron ore pellets but could be readily applied to other packed particle materials. Microscope images of polished cross-sections of iron ore pellets have been imaged in their entirety and automated analysis of hundreds of images has been performed. Joins between particles have been identified based on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and determined to hold discriminative power by displaying properties consistent with sintering theory and results from traditional pellet diameter measurements on the heated samples, and a statistical evaluation using the Welch t-test.
Place, publisher, year, edition, pages
2015. Vol. 48, no 11, 3451-3465 p.
Research subject Signal Processing; Dependable Communication and Computation Systems
IdentifiersURN: urn:nbn:se:ltu:diva-12912DOI: 10.1016/j.patcog.2015.05.012Local ID: c0f7d54d-414d-4f70-9a87-df78138249b5OAI: oai:DiVA.org:ltu-12912DiVA: diva2:985863
ProjectsHLRC PIA - Automated Image Analysis for Quantitative Characterisation of Iron Ore Pellet Structures
Validerad; 2015; Nivå 2; 20130224 (frinel)2016-09-292016-09-29Bibliographically approved