Classification of patterns with use of intelligent methods

Magistrsko delo obravnava področje umetne inteligence, strojnega učenja, razvrščanja kompleksnih vzorcev in metode določitve značilk. Predstavljeno je delovanje nekaterih najpogosteje uporabljenih razvrščevalnih algoritmov. Izdelan je bil algoritem za zaznavo Parkinsonove bolezni na podlagi zajetega zvočnega signala. Meritve zvoka so bile narejene na štiridesetih posameznikih. Od tega je bila polovica zdravih in polovica z Parkinsonovo boleznijo. Namen naloge je razviti robusten sistem za zaznavo prisotnosti Parkinsonove bolezni. Za izboljšanje natančnosti razvrščanja, so bile uporabljene različne tehnike določitve značilk (Pearsonov korelacijski koeficient, Khendallov korelacijski koeficient in Samoorganizacijske gruče) in topologije nevronskih mrež. S pomočjo usmerjene nevronske mreže, je bila dosežena 86,47 % natančnost razvrščanja. Omenjena natančnost je bila dosežena z uporabo redukcije značilk na podlagi Pearsonovega korelacijskega koeficienta.This Master’s thesis discusses artificial intelligence, machine learning, classification of complex patterns and feature selection procedure. Some of the most used classification algorithms are introduced. Algorithm for the detection of Parkinson’s disease based on sound measures has been made. Sound measurements of forty individuals were used as a dataset. Half of the individuals are healthy and half have the Parkinson’s disease. Purpose of this thesis is to present robust system for Parkinson’s disease detection. Few different feature selection techniques (Pearson’s correlation coefficient, Khendall’s correlation coefficient and Self-organizing maps) and neural network topologies have been used for improving classification accuracy. With the use of feed-forward neural network 86,47 % accuracy was achieved based on Pearson’s correlation coefficient

SUPPORT SYSTEM FOR SPOTTING OF DEEP DRAWING TOOLS

Doktorska disertacija se ukvarja z digitalizacijo postopka tuširanja orodij za globoki vlek. Tuširanje orodja in analiza tuširne slike sta integralni del postopka izdelave orodij, ki služita za določanje prileganja orodja in preoblikovanca. Rezultat analize tuširne slike je pokazatelj tehnološke kakovosti orodja. Zaradi odsotnosti znanstvene obravnave postopka tuširanja doktorska disertacija opisuje postopek in ga umesti v proces izdelave orodij. V nadaljevanju je zaradi digitalizacije postopka tuširanja predstavljen sistem za analizo tuširne slike, ki je sestavljen iz 3D digitalno optičnega zajemanja barvnih in geometrijskih informacij tuširanih preoblikovancev, predhodne obdelave zajetih podatkov z decimacijo, segmentacijo po metodi k-voditeljev, določanja stopnje svetlosti, mreženja (2D in 3D) in prikaza področij z informacijo o kakovosti naleganja na CAD modelu. Ob tem je predstavljena tudi integracija analize tuširne slike z rezultati simulacije preoblikovanja za ocenjevanje naleganja orodij. Razviti so bili kazalniki, za prikaz stopnje pokritosti oblakov točk in stopnje homogenosti. Za delovanje sistema je treba s predobdelavo zajete 3D tuširne slike ločiti barvne točke spodnje in zgornje površine preoblikovanca. Učinkovitost segmentacije je empirično validirana na podlagi Davies-Bouldinovega indeksa. Rezultati sistema so tudi primerjani s strokovnjakovo sposobnostjo analize tuširne slike za določitev naleganja orodja.Presented PhD thesis tackles the digitalisation of the spotting process of deep-drawing tools. Tool spotting and spotting image analysis represent an integral part of the overall tool making process, and serve to assess the degree of fit between tool and sheet metal part. The result of the spotting image analysis is an indicator of the technological quality of the tool. Due to the lack of scientific description of spotting process, the PhD thesis also provides an extensive description of the spotting process and places it into the overall toolmaking process. In the following, due to the digitalization aim, a spotting image analysis system is presented, which consists of 3D digital optical capture of the spotted surface (colour and geometric information), pre-processing in terms of decimation, segmentation by k-means, brightness level determination, meshing (2D and 3D) and display of areas with information on the quality of the fit on the CAD model. The integration of the forming simulation results for assessing contact areas is also presented. Various indicators are given, such as the degree of coverage of each point cloud and the degree of homogeneity. For appropriate deployment of the presented system, the coloured point clouds need to be separated into the lower and upper surfaces by pre-processing. The segmentation performance is empirically validated based on Davies-Bouldin index. The presented system’s results are also compared with the expert’s ability to analyse the spotting image

Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718

This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts

Technology of Numerically Controlled Lathes with Driven Tools

Razvoj obdelovalnih strojev z računalniško podprtim krmiljenjem je povečal zmogljivosti obdelovalnih sistemov in zahtevnost priprave tehnologije in programiranja. Sodobne numerično krmiljene stružnice s krmiljenim glavnim vretenom in orodji z lastnim pogonom so omogočile izvajanje zahtevnih tehnoloških operacij struženja in rezkanja na istem stroju, vendar od tehnologov zahtevajo dodatno znanje in izkušnje za učinkovito izrabo. Knjiga celovito obravnava področje priprave tehnologije in programiranja numerično krmiljenih stružnic z gnanimi orodji v skladu s standardom ISO, pri čemer je poudarek predvsem na dejstvih, ki niso odvisna od proizvajalcev strojev in krmilj. Razlage in primeri so podkrepljeni s slikovnim gradivom, zato je mogoče knjigo uporabljati ne le za izobraževanje, temveč tudi kot priročnik v industriji. Ob opisu temeljev programiranja numerično krmiljenih strojev, najbolj razširjenih ukazov in predpostavk, ki se uporabljajo na stružnicah z gnanimi orodji, knjiga zajema tudi razlago struženja navojev, opise obdelovalnih ciklov, ki poenostavljajo ročno programiranje, in parametrično programiranje, ki omogoča avtomatizacijo programiranja za skupine izdelkov.The development of machine tools with computer-aided control has increased the capabilities of machining systems and the complexity of technology preparation and programming. Modern numerically controlled lathes with controlled main spindle and driven tools have made it possible to perform complex technological turning and milling operations on the same machine, but require additional knowledge and experience of technologists for efficient use. The book comprehensively covers the field of technology preparation and programming of numerically controlled lathes with driven tools according to the standard ISO and focuses on matters that do not depend on the manufacturers of the machines and controls. Explanations and examples are supported by illustrations, so that the book can be used not only for training but also as a manual in industry. In addition to the basics of programming numerically controlled machines, the most common commands and assumptions used on lathes with driven tools, the book also includes an explanation of thread turning, descriptions of machining cycles that simplify manual programming, and parametric programming to automate the programming of product groups

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved

Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718

This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts

Determination of the Grain Size in Single-Phase Materials by Edge Detection and Concatenation

This paper presents a novel approach for edge detection and concatenation. It applies the proposed method on a set of optical microscopy images of aluminium alloy Al 99.5% (ENAW1050A) samples with different grain size values. The performance of the proposed approach is evaluated based on the intercept method and compared with the manual grain size determination method. Edge detection filters have proven inefficient in grain boundaries’ detection of the presented microscopy images. To some extent only the Canny edge-detection filter was able to compute grain boundaries of lower-resolution images adequately, while the presented method proved to be superior, especially in high-resolution images. The proposed method has proven its applicability, and it implies higher automatisation and lower processing times compared to manual optical microscopy image processing

Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation