Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation

Budući da proizvodna poduzeća traže kvalitetnije proizvode, mnogo svojih napora troše na praćenje i reguliranje dimenzionalne točnosti. U ovom je radu za predviđanje dimenzionalne devijacije obratka pri tokarenju 11SMn30 čelika, primijenjen konvencionalni deterministički pristup, na primjer metoda višestruke linearne regresije i dvije metode umjetne inteligencije, "back-propagation feed-forward" umjetna neuronska mreža (ANN) i optimizacija roja čestica (PSO). Kao ulazni parametri uzeti su brzina osovine, brzina napajanja, dubina rezanja, tlak rashladnog fluida za podmazivanje i broj proizvedenih dijelova , a dimenzijska devijacija obratka kao izlazni parameter. Značaj pojedinih parametara i njihovi međusobni utjecaji na dimenzionalnu devijaciju su statistički analizirani, a vrijednosti predviđene regresijskim, ANN i PSO modelima uspoređene su s eksperimentalnim rezultatima kako bi se ocijenila točnost predviđanja. Model predviđanja zasnovan na PSO pokazao se boljim od druga dva modela. Međutim, sva se tri modela mogu koristiti za predviđanje dimenzionalnih devijacija kod tokarenja.As manufacturing companies pursue higher-quality products, they spend much of their efforts monitoring and controlling dimensional accuracy. In the present work for dimensional deviation prediction of workpiece in turning 11SMn30 steel, the conventional deterministic approach, such as multiple linear regression and two artificial intelligence techniques, back-propagation feed-forward artificial neural network (ANN) and particle swarm optimization (PSO) have been used. Spindle speed, feed rate, depth of cut, pressure of cooling lubrication fluid and number of produced parts were taken as input parameters and dimensional deviation of workpiece as an output parameter. Significance of a single parameter and their interactive influences on dimensional deviation were statistically analysed and values predicted from regression, ANN and PSO models were compared with experimental results to estimate prediction accuracy. A predictive PSO based model showed better predictions than two remaining models. However, all three models can be used for the prediction of dimensional deviation in turning

Prediction of Cutting Forces with Neural Network by Milling Functionally Graded Material

AbstractPaper shows the general characteristics of graded materials, their previous industrial use and potential use of graded materials in the future. In any case, today the use of graded materials is increasing and moving from the laboratory environment into everyday use. However, the subsequent processing of the graded material remains the big unknown, and represents a major challenge for researchers and industry around the world. It could be said that the study of machinability of these materials is in its infancy and in this area are many unanswered questions. Machinability problem of graded materials was undertaken at the Faculty of Mechanical Engineering in Maribor. After a radical study of the literature and potential machining processes of graded materials, we started with the implementation of cutting processes on the workpiece. This professional paper presents the first results of the analysis, which will be used for further research and machinability study of graded materials. Also prediction of cutting forces with neural network by milling functionally graded material was made. In paper first predicted cutting forces by milling graded material are presented

Intelligent system for prediction of mechanical properties of material based on metallographic images

U radu se predstavlja razvijeni inteligentni sustav za predviđanje mehaničkih svojstava materijala na temelju metalografskih slika. Sustav se sastoji od dva modula. Prvi je modul algoritam za dobivanje karakteristika iz metalografskih slika. Prvi algoritam očitava metalografsku sliku dobivenu mikroskopom, zatim se dobivaju karakterisike razvijenim algoritmom, i na kraju algoritam izračunava omjere mikrostrukture materijala. U ovom istraživanju potrebno je što točnije odrediti omjere grafita, ferita i ausferita iz metalografskih slika. Drugi modul razvijenog sustava je sustav za predviđanje mehaničkih svojstava materijala. Predviđanje mehaničkih svojstava materijala izvršeno je pomoću feed-forward umjetne neuronske mreže. Kao ulazi u umjetnu neuronsku mrežu rabljeni su izračunati omjeri grafita, ferita i ausferita, dok su mehanička svojstva materijala upotrebljena kao ciljevi za uvježbavanje. Uvježbavanje umjetnih neuronskih mreža obavljeno je na prilično maloj bazi podataka, no mijenjajući parametre nama je to uspjelo. Umjetna neuronska mreža je naučila do te mjere da je greška bila prihvatljiva. S orijentiranom neuronskom mrežom uspješno smo predvidjeli mehanička svojstva izuzetog uzorka.This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample

Reverse Engineering of Parts with Optical Scanning and Additive Manufacturing

AbstractThis paper presents reverse engineering of car volume button. The purpose of article is to introduce reverse engineering procedure, what we need to do this kind of procedure and how we can remanufacture car's volume button. The purpose of reverse engineering is to manufacture another object based on a physic and existing object for which 3D CAD is not available. The first we need digital version of object. Because our car's volume button has free formed surfaces we decided to use 3D scanning technology to obtain the point cloud of existing object. With the help of point cloud we can developed 3D CAD model which will be used for manufacturing of button pair. We used for manufacturing of pair of buttons machine for selective laser sintering Formiga P 100. In the paper are also described costs of making of one pair of buttons and whole workspace