Abstract
Single point incremental forming (SPIF) is a cheap and flexible sheet metal forming process for rapid manufacturing of complex geometries. Additionally, it is important for engineers to measure the surface finish of work pieces to assess their quality and performance. In this paper, a predictive model based on machine learning and computer vision was developed to estimate arithmetic mean surface roughness (Ra) and maximum peak to valley height (Rz) of Ti6Al4V parts obtained by SPIF. An image database was prepared to train different classification algorithms in accordance with a supervised learning approach. A speeded up robust feature (SURF) detector was used to obtain visual vocabulary so that the classifiers are able to group the photographs into classes. The experimental results indicated that the proposed predictive method shows great potential to determine the surface quality, as classifiers based on a support vector machine with a polynomial kernel are suitable for this purpose.
Highlights
Single Point Incremental Forming (SPIF) is a rapid manufacturing technique based on the localized plastic deformation of sheet metal by a hemispherical tool [1]
The aim of this work was to develop a new methodology for the evaluation of surface quality using photographs of twenty-one parts formed by SPIF as inputs in machine learning classifiers
This paper focuses on supervised learning to build a predictive model to obtain arithmetic mean roughness from the images of a piece manufactured by incremental forming; as a result, labelled and categorized photographs are used as training data
Summary
Single Point Incremental Forming (SPIF) is a rapid manufacturing technique based on the localized plastic deformation of sheet metal by a hemispherical tool [1]. The process has been applied to commercial copper, some aluminum alloys, and high formability mild steel. Fan et al [8] developed a heating procedure based on Joule heating by electric power from the tool to the sheet, which has been adopted by other authors [9]. There have been attempts to improve formability using the local heating generated between the tool friction on the sheet metal when it rotates at high speed [13]. Some methods mixing friction and external sources of heat can be found in literature [14]
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