Prediction of laser drilled hole geometries from linear cutting operation by way of artificial neural networks

AbstractThis paper deals on artificial intelligence (AI) application for the estimation of kerf geometry and hole diameters for laser micro-cutting and laser micro-drilling operations. To this aim laser cutting and laser drilling operation were performed on NIMONIC 263 superalloy sheet, 0.38 mm in nominal thickness, by way of a 100 W fibre laser in modulated wave regime. Linear cuts and holes (by trepanning) were performed fixing the average power at 80 W and changing the pulse duration, the cutting speed, the focus depth and the laser path (the latter only for the drilling operations). Kerf width and the holed diameter, at the upper and downsides, were measured by digital microscopy. Different artificial neural networks (ANNs) were developed and tested to predict the kerf widths and the diameters (at the upper and downside). Two ANNs were addressed to the linear cutting process modelling; also, two further ANNs were developed for micro-drilling on the base of the linear cutting process features. The networks were trained with a subset of data containing the process conditions and the kerf/hole geometry. The ANN test was performed with the remaining data. The results show that ANNs can model the cut and hole geometry as a function of the process parameters. Moreover, the ANN trained with kerf geometry is more efficient. Therefore, a functional correlation between the kerf geometries achievable in the linear cutting process and micro-drilling was assessed

optimization of the sandblasting process for a better electrodeposition of copper thin films on aluminum substrate by feedforward neural network

Abstract The influence of a proper surface preparation is essential for a better adhesion of copper thin films on aluminum substrate. In this work, the surface properties of the aluminum substrate have been modified through sandblasting process, in order to influence the quality of electroplating. To evaluate the correct adhesion of the thin film to the substrate non-destructive measurements of diffusivity by infrared thermography have been made. A combining of a feedforward artificial neural network (FFANN) and an external optimized algorithm (EOA) is proposed to optimize the substrate surface preparation process. A FFANN model is developed to map the complex non-linear relationship between the surface process conditions of the substrate and the thermal diffusivity of the electroplated sample. A good performance of the FFANN model is achieved. An EOA is used for the optimization of the sandblasting process conditions, in order to maximize the adhesion of the thin film to the substrate

Laser Texturing to Increase the Wear Resistance of an Electrophoretic Graphene Coating on Copper Substrates

In the present paper, different surface preparations are investigated with the aim of increasing the wear behaviour of an electrophoretic graphene coating on a copper plate. The study was divided into two steps: In the first step (pre-tests), to detect the most promising pretreatment technology, five different surface preparations were investigated (electropolishing, sandblasting, degreasing and pickling, laser cleaning and laser dots).In the second step, on the basis of the results of the first step, a 3(2) full factorial plan was developed and tested; three treatment types (pickled and degreased, laser-cleaned, and laser dots) and three different voltages (30, 45 and 60 V) were adopted. Analysis of variance (ANOVA) was used to evaluate their influence on wear resistance; in particular, the maximum depth and width of the wear tracks and the coating break distance were investigated. The results of this study show that, in optimal conditions, laser treatment (particularly laser dots) canlead to as high as a four-fold increase in wear resistance

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

Piste termiche

La presente invenzione riguarda la realizzazione di piste termiche, realizzate su base metallica, mediante rivestimento multistrato di rame e grafene, poste all’interno e/o sulla superficie di componenti metallici e/o non metallici, utili per la dissipazione direzionale di calore

Implementazione di reti neurali artificiali per il controllo della precipitazione di fasi secondarie nei giunti saldati di tubi di acciaio inossidabile duplex

L’impiego industriale degli acciai inossidabili duplex è legato alla realizzazione di giunti saldati ed è esteso ai settori in cui è richiesta una particolare resistenza ad ambienti aggressivi unita ad una buona resistenza meccanica. Durante un processo di saldatura possono avvenire importanti precipitazioni di carburi e fasi secondarie che alterano le caratteristiche meccaniche dei componenti saldati. Il presente lavoro ha avuto come obiettivo l’analisi dell’influenza delle percentuali di fasi secondarie sulle proprietà finali di un acciaio duplex 22Cr 5Ni 3Mo, impiegato per realizzare tubi di grandi dimensioni saldati ad arco. A tal fine si è proceduto ad indurre delle precipitazioni di fasi secondarie, effettuando sull’acciaio alcuni trattamenti isotermi a 750, 850 e 900°C con vari tempi di permanenza in forno, valutando la sensibilità del test FIMEC (flat-top cylinder indenter for mechanical characterization) al variare delle percentuali totali di fasi secondarie precipitate. La determinazione quantitativa delle percentuali delle varie fasi è stata effettuata con tecniche di analisi d’immagine; la composizione delle fasi è stata determinata tramite microanalisi EDS. Il test Fimec si è rivelato idoneo ad identificare anche piccole variazioni percentuali delle fasi secondarie presenti. I dati sperimentali sono stati utilizzati per implementare due reti neurali artificiali Multilayer Feedforward, poste in serie. Le reti neurali hanno consentito di effettuare una previsione della curva carico-penetrazione al variare dei parametri del trattamento termico

Thermal and mechanical improvement of aluminum open-cells foams through electrodeposition of copper and graphene

Thanks to its planar structure, graphene is characterized by unique properties, such as excellent chemical inactivity, high electrical and thermal conductivity, high optical transparency, extraordinary flexibility and high mechanical resistance, which make it suitable in a very wide range of applications. This paper details the state of the art in graphene coating applied to aluminum open-cells foams for the improvement of their mechanical and thermal behavior. Metallic foams are highly porous materials with extremely high convective heat transfer coefficients, thanks to their complex structure of three-dimensional open-cells. Graphene nanoplatelets have been used to improve thermal conductivity of aluminum foams, to make them better suitable during heat transfer in transient state. Also, an improvement of mechanical resistance has been observed. Before electrodeposition, all the samples have been subjected to sandblasting process, to eliminate the oxide layer on the surface, enabling a better adhesion of the coating. Different nanoparticles of graphene have been used. The experimental findings revealed a higher thermal conductivity for aluminum open cells foams electroplated with graphene. Considered the relatively low process costs and the improvements obtainable, these materials are very promising in many technological fields. The topics covered include surface modification, electrochemical plating, thermo-graphic analysis

Optimization of hot extrusion process using an artificial neural network

Extrusion of aluminium alloys is a complex process which depends on the characteristics of the material and on a process parameters (initial billet temperature, extrusion ratio, friction at the interfaces, die geometry etc.). The right choice of these parameters is fundamental to avoid surface damage of the extruded. One of the most important factors is the temperature profile. In the present work, a neural network has been implemented for optimizing the aluminium extrusion process, by determining the temperature profile of a Al 6060 alloy (UNI 9006/1) at the exit of the die. Parameters such as heating conicity at the entry of the die, permanence time in the induction heater and extrusion ratio have been varied. A three-layer neural network with back propagation (BP) algorithm has been trained with the experimental data from the industrial process. The experimental data refer to five different section bars, characterized by a specific extrusion ratio. For every section bar there is an optimal initial temperature profile of the billet, realized in the induction heater. The temperature has been controlled by five thermocouples allocated inside the induction heater to regular intervals. By means of optical pyrometry the instantaneous temperatures at the exit of the induction heater and of the die have been measured. It has been found that the temperature profile on the section bar, predicted by the neural network, closely agree whit experimental values. This indicates that the neural networks can be successfully used to predict the evolution of the extrusion process thus to control it