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

Design and mechanical characterization of voronoi structures manufactured by indirect additive manufacturing

Additive manufacturing (AM) is a production process for the fabrication of three-dimensional items characterized by complex geometries. Several technologies employ a localized melting of metal dust through the application of focused energy sources, such as lasers or electron beams, on a powder bed. Despite the high potential of AM, numerous burdens afflict this production technology; for example, the few materials available, thermal stress due to the focused thermal source, low surface finishing, anisotropic properties, and the high cost of raw materials and the manufacturing process. In this paper, the combination by AM of meltable resins with metal casting for an indirect additive manufacturing (I-AM) is proposed. The process is applied to the production of open cells metal foams, similar in shape to the products available in commerce. However, their cellular structure features were designed and optimized by graphical editor Grasshopper®. The metal foams produced by AM were cast with a lost wax process and compared with commercial metal foams by means of compression tests

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

Evaluation of the effects of the metal foams geometrical features on thermal and fluid-dynamical behavior in forced convection

Metal foams are a material, featuring interesting characteristics for the aeronautical and automotive fields because of their low specific weight, high thermal properties, and mechanical performances. In particular, this paper deals with thermal and fluid dynamic study of 24 open-cell aluminum EN43500 (AlSi10MnMg) metal foams produced by indirect additive manufacturing (I-AM), combining 3D printing and metal casting to obtain a controllable morphology. A study of foam behavior function of the morphological features (pores per inch (PPI), branch thickness (r), and edges morphology (smooth-regular)) was performed. The samples produced were heated by radiation and tested in an open wind circuit gallery to measure the fluid dynamic properties such as pressure drop (Delta p), inertial coefficient (f), and permeability (k), in an air forced convection flow. The thermal characterization was performed evaluating both the theoretical (k(th)) and effective (k(eff)) thermal conductivity of the foams. Also, the global heat transfer coefficient (HTCglobal) was evaluated with different airflow rates. Analysis of variance (ANoVA) was performed to figure out which geometrical parameters are significant during both thermal and fluid dynamic processes. The results obtained show how the controllable foam morphology can affect the involved parameters, leading to an ad hoc design for industrial applications that require high thermo-fluid-dynamical performances

Improvement of the mechanical and thermal characteristics of open cell aluminum foams by the electrodeposition of Cu

Abstract Recently aluminum foaming has been of much interest due to its characteristics properties of light weight structure. Metallic foams are highly porous materials which present complex structure of three-dimensional open cells. This aspect causes strong limitations in mass transport due to electro-deposition technology. In this work, the electro-deposition of copper on aluminum open-cell foams substrates was developed, in order to enhance the thermal and mechanical properties of these cellular materials. The mechanical and thermal characterization of the produced samples was lead through compression and conductivity tests. On the basis of the experimental results, analytical models are proposed to predict the quantity and the quality characteristics of the coating

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 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

electro deposition of cu on open cell aluminum foams

This manuscript deals with the electro-deposition of Cu on aluminum foams. Metallic foams are highly porous materials which present complex structure of three-dimensional open cells. This aspect causes strong limitations in mass transport due to electro-deposition technology. Experimental tests were performed to study the influence of the operational parameters on the overall performance of the coated aluminum foams. The experimental findings revealed that the manufactured metal foams were characterized by a high thermal conductivity and low process costs, making these materials very promising in many technological fields. On the basis of the experimental results, analytical models are proposed to predict the quantity and the quality characteristics of the coating

AISI 316L carbocementato a bassa temperatura (su scala industriale vs. via plasma su scala di laboratorio): studio del comportamento tribologico in condizioni di strisciamento a secco

La cementazione a bassa temperatura (LTC, Low Temperature Carburising) consente di incrementare ladurezza superficiale degli acciai inossidabili austenitici senza comprometterne significativamente la resistenzaa corrosione. Questa tipologia di trattamento è applicata con successo su scala industriale a tale importantecategoria di materiali; tuttavia, i lunghi tempi di processo, conseguenti alla bassa temperatura di trattamento,comportano alti costi e quindi bassa competitività rispetto ai trattamenti superficiali più tradizionali. Nel tentativodi superare queste limitazioni, è stato messo a punto, su scala di laboratorio, un trattamento di cementazione abassa temperatura assistito da plasma, in cui l’attivazione della superficie è effettuata tramite processi basatisull’utilizzo di una miscela di H2/CH4 ad alta densità di energia. Il trattamento su scala di laboratorio, eseguitocon miscela al 2% di CH4, è risultato idoneo a formare uno strato di austenite espansa, con spessori compresitra 18 e 35 ?m e durezze variabili da 450 a 850 HV. Durezze più elevate (fino a circa 1100 HV) e sostanzialmenteriproducibili sono state invece rilevate sui campioni sottoposti a trattamento industriale. Sui campioni trattatimediante LTC sono state eseguite prove di strisciamento a secco, con un tribometro “pattino-su-cilindro” (pattinistazionari: AISI 316L cementato; cilindro rotante: AISI 316L non trattato). I coefficienti d’attrito sono risultaticonfrontabili per i campioni industriali e su scala di laboratorio; maggiori criticità si sono rilevate rispetto alcomportamento ad usura per i campioni trattatati su scala di laboratorio, a causa della disuniformità nei valoridi durezza. In ogni caso, il trattamento su scala di laboratorio ha dato luogo a un apprezzabile incrementodella resistenza usura dell’AISI316L rispetto all’acciaio non trattato. Al carico massimo preso in esame (10 N),i volumi di usura dei campioni a più elevata durezza sono risultati confrontabili con quelli dei provini trattatiindustrialmente. In conclusione, il trattamento al plasma su scala di laboratorio ha dimostrato la sua efficacia,anche se l’apparato prototipale impiegato per la sperimentazione non permette di ottenere un effetto omogeneosu tutta la superficie dei provini, pur di dimensioni ridotte. Si ritiene tuttavia che il problema possa esseresuperabile operando con una camera di maggiori dimensioni, dove gli effetti locali legati a variazioni di curvaturadei campioni, tipici del trattamento al plasma, possono essere meno critici