Neural network implementation for the prediction of load curves of a flat head indenter on hot aluminum alloy

The indentation test performed by means of a flat-ended indenter is a valuable non-destructive method for assessment of metals at a local scale. Particularly, from the indentation curves it is possible to achieve several mechanical properties. The aim of this paper is the implementation of an artificial neural network for the prediction of the indentation load as a function of the penetration depth for an aluminium substrate. In particular, the neural network is addressed to the mechanical characterization of the bulk in function of temperature and indentation rate. The results obtained showed a high accuracy in curves prediction

Image-based system and artificial neural network to automate a quality control system for cherries pitting process

Abstract This work proposes a non-destructive quality control for a pitting process of cherries. A system composed of a video camera and a light source records pictures of backlit cherries. The images processing in MATLAB environment provides the dynamic histograms of the pictures, which are analysed to state the presence of the pit. A feedforward artificial neural network was implemented and trained with the histograms obtained. The network developed allows a fast detection of stone fractions not visible by human inspection and the reduction of the accidental reject of properly manufactured products

Neural networks approach for IR-heating and deformation of ABS in thermoforming

This study focuses on the interaction between an IR-heating source and material to be thermoformed, with the aim of providing an accurate description of the polymer behaviour under the conjugated effect of stress and temperature. The possibility to model material behaviour and develop a reliable and simple system to define thermoforming strategy is of great interest to improve industrial production, reducing manufacturing costs. In this investigation, both tensile tests and temperature measurements were performed on ABS subjected to IR radiation. Different values of distance polymer-lamp, sample thickness, and test rate were considered. The experimental trends were modelled by artificial neural network. A good generalisation capability and high flexibility were found for the proposed neural network solution, in accordance with the experimental results