Pokročilé modelování povrchové drsnosti pomocí neuronových sítí, Taguchiho metody a genetického algoritmu

Modern manufacturing requires reliable and accurate models for the prediction of machining performance. Predicting surface roughness before actual machining plays a very important role in machining practice. This paper presents the modeling methodology for predicting the surface roughness in turning of unreinforced polyamide based on artificial neural networks (ANNs), Taguchi method and genetic algorithm (GA). The machining experiment was conducted based on Taguchi’s experimental design using L27 orthogonal array. Input variables consisted of cutting speed, feed rate, depth of cut and tool nose radius, while surface roughness (Ra) was considered as output variable. To systematically identify optimum settings of ANN design and training parameters, Taguchi method was applied. Furthermore, a simple procedure based on GA for enhancing the ANN model prediction accuracy was applied. Statistically assessed as an accurate model, ANN model equation was graphically presented in the form of contour plots to study the effect of the cutting parameters on the surface roughness.Moderní výroba vyžaduje spolehlivé a přesné modely pro predikci výkonu zpracování. Předvídání drsnost povrchu před vlastním zpracováním hraje velmi důležitou roli v obráběcí praxi. Tato práce představuje modelovou metodiku pro odhad drsnosti povrchu při soustružení z prostého polyamidu na bázi umělé inteligence neuronových sítí (ANNs), Taguchiho metodě a genetických algoritmů (GA). Obráběcí experiment byl proveden na základě experimentálního Taguchiho návrhu pomocí L27 ortogonální pole. Vstupními proměnnými jsou řezná rychlost, posuv, hloubka řezu a poloměru břitu, zatímco drsnost povrchu (Ra) je považována za výstupní proměnnou. Pro systematickou identifikaci optimálního nastavení ANN návrhu a odborné přípravy parametrů byla použita metoda Taguchi. Dále byl použit jednoduchý postup, založený na GA pro zvýšení přesnosti modelu ANN predikce. Statisticky vyhodnocený přesný model ANN rovnice byl graficky prezentován ve formě obrysů pro studium vlivu řezných parametrů na drsnost povrchu

Optimization of machining processes using pattern search algorithm

Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS) algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed

Mathematical modeling and optimization of laser cutting process using artificial intelligence methods

Laser cutting is one of more attractive non-conventional machining technologies which is being increasingly used in industry due to its efficiency. Laser cutting is based on the use of highly concentrated light energy, obtained by laser irradiation, for materials processing by melting or evaporation. Since it is desirable to remove the molten and evaporated material from the cutting zone as soon as possible, laser cutting is performed with a coaxial jet of an assist gas. From the technological point of view, laser cutting is a very complex process of interactions between the laser beam, assist gas and workpiece material, whose performances are influenced by a number of factors. Insufficient knowledge of the process as well as a lack of reliable and practical data about influential factors leads to underutilization of laser cutting technology with respect to the possibilities that it provides, but also to the fact that the cut quality does not satisfy users. In order to ensure achievement of the required cut quality, cost reduction and increase of productivity, it is necessary to quantify the relationships between process factors and process performances through the mathematical modeling. On the basis of developed relationships it is possible to perform a detailed analysis of the influence of process factors on process performances, identify near optimal factor values and control the process of laser cutting so as to improve the efficiency and cut quality. This paper presents the results of experimental studies carried out with the objective of modeling and optimization of laser cutting process i.e. assessment of process factors effects and application of obtained results for control of laser cutting process in order to increase the cut quality obtained in CO2 laser nitrogen cutting of stainless steel. Based on data from experimental studies carried out by using Taguchi’s orthogonal array, mathematical models relating cut quality characteristics and process factors such as laser power, cutting speed, assist gas pressure and focus position were developed. Mathematical modeling has been carried out using artificial neural networks, whereby the training of artificial neural networks was conducted by using Levenberg-Marquardt algorithm. On the basis of developed mathematical relationship between the cut quality characteristics and process factors, single and multi objective optimization of laser cutting was enabled. For the purpose of optimization two approaches were applied. The first approach is based on the integration of mathematical models created by artificial neural networks and meta-heuristic optimization methods. The second approach, which is aimed at determining the near optimal values of process factors in a way that the laser cutting process is robust to different causes of variation, was based on the application of Taguchi’s optimization method. In order to validate optimization solutions that were previously determined using different optimization methods, solve multi-criteria optimization problems and “off-line” control of laser cutting process, in the dissertation developed software prototype was presented. The modeling and optimization methodology presented in the dissertation and its implementation through the development of application software for industrial use, can raise planning of laser cutting process to a higher level and make process more economical and productive

Volba optimálních parametrů laseru při CO2 laserovém řezání pomocí Taguchi metody

Identification of laser cutting conditions that are insensitive to parameter variations and noise is of great importance. This paper demonstrates the application of Taguchi method for optimization of surface roughness in CO2 laser cutting of stainless steel. The laser cutting experiment was planned and conducted according to the Taguchi’s experimental design using the L27 orthogonal array. Four laser cutting parameters such as laser power, cutting speed, assist gas pressure, and focus position were considered in the experiment. Using the analysis of means and analysis of variance, the significant laser cutting parameters were identified, and subsequently the optimal combination of laser cutting parameter levels was determined. The results showed that the cutting speed is the most significant parameter affecting the surface roughness whereas the influence of the assist gas pressure can be neglected. It was observed, however, that interaction effects have predominant influence over the main effects on the surface roughness.Identifikace podmínek řezání laserem, které jsou citlivé na změny parametrů a hluk jsou velmi důležité. Tento článek demonstruje použití Taguchi metody pro optimalizaci drsnosti povrchu v CO2 laserové řezání nerezové oceli. Experiment řezání laserem byl naplánován a proveden v souladu s Taguchiho experimentálním návrhem pomocí ortogonálního pole L27. V experimentu byly uvažovány čtyři parametry řezání laserem jako síla laseru, rychlost řezání, tlak plynu a zaměření pozice

AN INVESTIGATION ON MEAN ROUGHNESS DEPTH AND MATERIAL EROSION SPEED DURING MANUFACTURING OF STAINLESS-STEEL MINIATURE RATCHET GEARS BY WIRE-EDM

This paper presents the results of an investigation conducted on wire electric discharge machining (wire-EDM) of miniature ratchet gears. Effects of three important process parameters spark duration, ‘Ton’, spark-off-duration ‘Toff’, and wire tension ‘WT’ on surface quality, i.e., mean roughness depth ‘Rz’ and productivity, i.e., material erosion speed ‘MES’, have been investigated by conducting seventeen experimental trials. Both Ton and Toff have been identified as the significant parameters. Further, an optimization of wire-EDM parameters resulted in simultaneously best compromise values of Rz 5.30 µm and MES 6.75 mm/min and is achieved the following cutting regime: Ton 1.5 µs, Toff 42.5 µs, and WT 1500 g. At the end, surface quality study has been conducted to evaluate the tribological fitness of the miniature ratchet gear machined at optimum combination of wire-EDM parameter values. It was investigated that the generation of uniform and shallow craters on the flank surfaces of ratchet gear machined at optimum values of parameters, imparted smoother bearing area curve and lower coefficient of friction. The profile and flank surface of the ratchet gear also found free from cracks, burrs, and dirt

POSSIBILITIES OF USING MONTE CARLO METHOD FOR SOLVING MACHINING OPTIMIZATION PROBLEMS

Companies operating in today's machining environment are focused on improving their product quality and decreasing manufacturing cost and time. In their attempts to meet these objectives, the machining processes optimization is of prime importance. Among the traditional optimization methods, in recent years, modern meta-heuristic algorithms are being increasingly applied to solving machining optimization problems. Regardless of numerous capabilities of the Monte Carlo method, its application for solving machining optimization problems has been given less attention by researchers and practitioners. The aim of this paper is to investigate the Monte Carlo method applicability for solving single-objective machining optimization problems and to analyze its efficiency by comparing the optimization solutions to those obtained by the past researchers using meta-heuristic algorithms. For this purpose, five machining optimization case studies taken from the literature are considered and discussed

Softverski prototip za optimizaciju i upravljanje proizvodnim procesima

Modeling of manufacturing processes aimed at better understanding, optimization and process control is very important in manufacturing practice. This is usually achieved by integrating empirical models with classical mathematical and meta-heuristic algorithms. In this paper, software prototype “Function Analyzer” for optimization and control of manufacturing processes is presented. It is based on the mathematical iterative search of entire space of possible input values. This way, the developed software is able to determine global extreme points of the process model and corresponding input values (process optimization). Furthermore, it is able to determine the optimal input values that satisfy the specified requirements for output value and accuracy (process control). The developed software is characterized by extendible architecture, flexible user interface and efficient operation. The abilities of software prototype “Function Analyzer” were demonstrated on two case studies. The first one considers the regression based modeling of dry turning of cold rolled alloy steel. The second case study considers the artificial neural network based modeling of dry turning of unreinforced polyamide.Modeliranje proizvodnih procesa s ciljem boljeg razumijevanja, optimizacije i upravljanja procesa je vrlo važno u proizvodnoj praksi. U tu svrhu obično se vrši integracija empirijskih modela procesa s klasičnim matematičkim i meta-heurističkim algoritmima. U ovom radu je predstavljen softverski prototip “Function Analyzer” za optimizaciju i upravljanje proizvodnih procesa koji se temelji na matematičkom iterativnom pretraživanju cijelog prostora mogućih ulaznih vrijednosti. Na taj način razvijeni softver je u mogućnosti odrediti globalne ekstremne točke modela procesa i odgovarajuće ulazne vrijednosti (optimizacija procesa). Nadalje, u stanju je odrediti optimalne ulazne vrijednosti koje zadovoljavaju određene uvjete za izlazne vrijednosti i točnosti (upravljanje procesa). Razvijeni softver karakterizira nadogradiva arhitektura, fleksibilno korisničko sučelje i učinkovit rad. Sposobnosti softverskog prototipa “Function Analyzer” su demonstrirane na dvije studije slučaja. Prva razmatra regresijsko modeliranje procesa tokarenja hladno valjanog legiranog čelika. Druga studija slučaja razmatra modeliranje procesa tokarenja neojačanog poliamida pomoću umjetne neuronske mreže

COMPARISON OF THREE FUZZY MCDM METHODS FOR SOLVING THE SUPPLIER SELECTION PROBLEM

The evaluation and selection of an optimal, efficient and reliable supplier is becoming more and more important for companies in today’s logistics and supply chain management. Decision-making in the supplier selection domain, as an essential component of the supply chain management, is a complex process since a wide range of diverse criteria, stakeholders and possible solutions are embedded into this process. This paper shows a fuzzy approach in multi – criteria decision-making (MCDM) process. Criteria weights have been determined by fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) method. Chosen methods, fuzzy TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), fuzzy WASPAS (Weighted Aggregated Sum Product Assessment) and fuzzy ARAS (Additive Ratio Assessment) have been used for evaluation and selection of suppliers in the case of procurement of THK Linear motion guide components by the group of specialists in the “Lagerton” company in Serbia. Finally, results obtained using different MCDM approaches were compared in order to help managers to identify appropriate method for supplier selection problem solving

Ocjena karakteristika ANN-BP i ANN-GA modela u predviđanju mehaničkih svojstava i obradivosti ljevačkih legura bakra

In this paper artificial neural network (ANN) models were developed to predict the mechanical properties and machinability of Cu–Sn–Pb–Si–Ni–Fe–Zn–Al alloys on the basis of the chemical composition (wt%) of alloying elements. The multi-layer perceptron architecture was used for developing ANN models. Two ANN training approaches, namely, the classical gradient descent back propagation (BP) and genetic algorithm (GA), were applied and statistically compared. The statistical methods of root mean square error (RMSE), absolute fraction of variance (r2) and mean absolute percent error (MAPE) were used for evaluating the performance of the developed ANN models. The results showed that training with GA improved the prediction performance of ANN models. By taking the full potential of GA through fine tuning of the GA parameters, the effectiveness of the approach could be further improved allowing for a wide application in the area of material engineering for the prediction of mechanical properties.U ovom radu su razvijeni modeli umjetnih neuronskih mreža (UNM) za predviđanje mehaničkih svojstava i obradivost Cu-Sn-Pb-Si-Ni-Fe-Zn-Al legura na temelju kemijskog sastava (%) legirajućih elemenata. Za razvoj UNM modela korištena je arhitektura višeslojnog perceptrona. Dva pristupa u treniranju UNM, odnosno gradijentno opadajući algoritam širenja unatrag (BP) i genetski algoritam (GA), su primijenjena i statistički uspoređena. Za ocjenu karakteristika razvijenih modela UNM korištene su statističke metode korijen srednje kvadratne pogreške (RMSE), apsolutna frakcija varijance (r2) i prosječna apsolutna postotna pogreška (MAPE). Dobiveni rezultati pokazuju poboljšanje karakteristika predviđanja UNM modela primjenom GA. Koristeći u potpunosti potencijal GA finim podešavanjem GA parametara, učinkovitost pristupa se može dodatno poboljšati što omogućuje široku primjenu u području inženjerstva materijala za predviđanje mehaničkih svojstava

DIFFERENCES BETWEEN LOWER BODY MUSCLE POTENTIAL DURING UNLOADED AND LOADED SQUAT JUMP IN ELITE MALE SPRINT SWIMMERS

The primary purpose was to determine differences between lower body muscle potential during the unloaded and loaded squat jump (SJ) in elite male swimmers. The secondary purpose was to assess the load that would maximize power output in the SJ. Twenty-one elite male trained competitive swimmers, all members of the Central Serbia Swimming Team (Age = 20.7 ± 3.8 yrs., Height = 1.84 ± 0.56 m, Weight = 77.5 ± 7.3 kg, FINA points 2017 long course = 636 ± 80) performed two trials of the unloaded and loaded SJ (barbell loads equal to 25 and 35% body weight). Loaded SJ testing with free weights was done using the Smith machine. The Myotest performance measuring system was used to calculate absolute and relative values of average power (Pavg, PavgRel) and maximal power (Pmax, PmaxRel) achieved during the unloaded and loaded SJ. The one-way ANOVA method and POST HOC (Tukey HSD) test were used. The results showed significant interactions between the unloaded and loaded squat jump for relative values of maximal power (F= 12.95, p= 0.000) and average power (F= 12.20, p= 0.000) as well as absolute values (F= 7.66, p= 0.001; F= 7.40, p= 0.001). The instantaneous power output in the SJ at 0% additional load (body weight) was significantly greater than that at 25% and 35% in the elite male trained competitive swimmers. The practical application of this study suggests that for male sprint swimmers, the load that generates maximal power output in the squat jump is body weight, without any additional load