Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica

Intelligent system for automated visual quality control of ceramic tiles based on machine vision is presented in this paper. The ceramic tiles production process is almost fully and well automated in almost all production stages with exception of quality control stage at the end. The ceramic tiles quality is checked by using visual quality control principles where main goal is to successfully replace man as part of production chain with an automated machine vision system to increase production yield and decrease the production costs. The quality of ceramic tiles depends on dimensions and surface features. Presented automated machine vision system analyzes those geometric and surface features and decides about tile quality by utilizing neural network classifier. Refined methods for geometric and surface features extraction are presented also. The efficiency of processing algorithms and the usage of neural networks classifier as a substitution for human visual quality control are confirmed.U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i površinskim značajkama. Predstavljeni sustav strojnog vida analizira geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih pločica na temelju navedenih značajki uporabom klasifikatora s neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za vizualnu kontrolu kvalitete ljudskim vidom

Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica

U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i površinskim značajkama. Predstavljeni sustav strojnog vida analizira geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih pločica na temelju navedenih značajki uporabom klasifikatora s neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za vizualnu kontrolu kvalitete ljudskim vidom

Multivariate statistical process monitoring

U industrijskoj proizvodnji prisutan je stalni rast zahtjeva, u prvom redu, u pogledu ekonomičnosti proizvodnje, kvalitete proizvoda, stupnja sigurnosti i zaštite okoliša. Put ka ispunjenju ovih zahtjeva vodi kroz uvođenje sve složenijih sustava automatskog upravljanja, što ima za posljedicu mjerenje sve većeg broja procesnih veličina i sve složenije mjerne sustave. Osnova za kvalitetno vođenje procesa je kvalitetno i pouzdano mjerenje procesnih veličina. Kvar na procesnoj opremi može značajno narušiti proizvodni proces, pa čak prouzrokovati ispad proizvodnje što rezultira visokim dodatnim troškovima. U ovom radu se analizira način automatskog otkrivanja kvara i identifikacije mjesta kvara u procesnoj mjernoj opremi, tj. senzorima. U ovom smislu mogu poslužiti različite statističke metode kojima se analiziraju podaci koji pristižu iz mjernog sustava. U radu se PCA i ICA metode koriste za modeliranje odnosa među procesnim veličinama, dok se za otkrivanje nastanka kvara koriste Hotellingova (T**2), I**2 i Q (SPE) statistike jer omogućuju otkrivanje neobičnih varijabilnosti unutar i izvan normalnog radnog područja procesa. Za identifikaciju mjesta (uzroka) kvara koriste se dijagrami doprinosa. Izvedeni algoritmi statističkog nadzora procesa temeljeni na PCA metodi i ICA metodi primijenjeni su na dva procesa različite složenosti te je uspoređena njihova sposobnost otkrivanja kvara.Demands regarding production efficiency, product quality, safety levels and environment protection are continuously increasing in the process industry. The way to accomplish these demands is to introduce ever more complex automatic control systems which require more process variables to be measured and more advanced measurement systems. Quality and reliable measurements of process variables are the basis for the quality process control. Process equipment failures can significantly deteriorate production process and even cause production outage, resulting in high additional costs. This paper analyzes automatic fault detection and identification of process measurement equipment, i.e. sensors. Different statistical methods can be used for this purpose in a way that continuously acquired measurements are analyzed by these methods. In this paper, PCA and ICA methods are used for relationship modelling which exists between process variables while Hotelling\u27s (T**2), I**2 and Q (SPE) statistics are used for fault detection because they provide an indication of unusual variability within and outside normal process workspace. Contribution plots are used for fault identification. The algorithms for the statistical process monitoring based on PCA and ICA methods are derived and applied to the two processes of different complexity. Apart from that, their fault detection ability is mutually compared

Environmental impact estimation of ceramic tile industry using modeling with neural networks

The ceramic tiles industry has a significant environmental impact due to consumption of raw materials, energy and environmental emissions. There are numerous activities on global level in accordance to the principles of sustainable development. This paper presents the development and application of mathematical models of manufacturing processes based on static neural networks for prediction and control of environmental impact of ceramic tiles production process. The neural network learning is made based on known input and output values from the production process. The control of the environmental impact is made on the basis of the output values from the process amounts of correct and faulty ceramic tiles. The model for prediction of correct amount of tiles and percentage of waste with an average error of 1.7% is presented in this paper. It could be successfully used to estimate and control the environmental influence. A simple model of production process has been applied in the manufacturing process of ceramic tile factory KIO Keramika d.o.o. Orahovica. It produced ceramic tiles using monofiring process according to EN 14 411 B III group Part L. The company has introduced and certified management systems according to ISO-9001. and ISO 1400

Adaptivna estimacija teško-mjerljivih procesnih veličina

There exist many problems regarding process control in the process industry since some of the important variables cannot be measured online. This problem can be significantly solved by estimating these difficult-tomeasure process variables. In doing so, the estimator is in fact an appropriate mathematical model of the process which, based on information about easy-to-measure process variables, estimates the current value of the difficultto-measure variable. Since processes are usually time-varying, the precision of the estimation based on the process model which is built on old data is decreasing over time. To avoid estimator accuracy degradation, model parameters should be continuously updated in order to track process behavior. There are a couple of methods available for updating model parameters depending on the type of process model. In this paper, PLSR process model is chosen as the basis of the difficult-to-measure process variable estimator while its parameters are updated in several ways – by the moving window method, recursive NIPALS algorithm, recursive kernel algorithm and Just-in-Time learning algorithm. Properties of these adaptive methods are explored on a simulated example. Additionally, the methods are analyzed in terms of computational load and memory requirements.Problemi s upravljanjem mnogih procesa u industriji vezani su s nemogućnošću on-line mjerenja nekih važnih procesnih veličina. Ovaj se problem može u značajnoj mjeri riješiti estimacijom ovih teško-mjerljivih procesnih veličina. Estimator je pri tome odgovarajući matematički model procesa koji na temelju informacije o ostalim (lako-mjerljivim) procesnim veličinama procjenjuje trenutni iznos teško-mjerljive veličine. Budući da su procesi po prirodi promjenjivi, točnost estimacije zasnovane na modelu procesa izgra.enog na starim podacima u pravilu opada s vremenom. Kako bi se ovo izbjeglo, parametre modela procesa je potrebno kontinuirano prepodešavati kako bi model što bolje opisivao (trenutno) vladanje procesa. Ovisno o tipu matematičkog modela, za prepodešavanje njegovih parametara na raspolaganju je više metoda. Kao osnova estimatora teško-mjerljive veličine u radu se koristi PLSR model procesa, dok se njegovi parametri prepodešavaju na više načina – metodom pomičnog prozora, rekurzivnim NIPALS algoritmom, rekurzivnim kernel algoritmom te Just-in-Time Learning metodom. Svojstva navedenih metoda adaptacije PLSR modela procesa ispitana su na odabranom primjeru. Nadalje, metode adaptacije su analizirane i s obzirom na računalnu i memorijsku zahtjevnost

Povećanje točnosti mjerenja temperature primjenom adaptivnog algoritma mikroupravljača u pretvorniku

The article compares the most common methods for converting measured resistance of platinum sensor to a temperature value in relation to the new adaptive algorithm applied in a compact temperature transmitter. The hardware of the temperature transducer is based on a microcontroller with input/output circuits that generate an output signal proportional to the measured temperature. The compact temperature transmitter has size constraints because it is required to be installed in the head of an industrial thermometer. The microcontroller is limited by the available power supply and memory resources. The platinum temperature sensors should conform to industry standards and fit a mathematical curve known as the Callendar-Van Dusen equation with nominal values of constants A, B and C. The increase in the total accuracy of temperature measurement is possible by matching the sensor characteristics with the connected temperature transducer. After calibration of the platinum sensor, corresponding new constants are calculated and written into the temperature transmitter. The presented new software method enables the use of the Callendar-Van Dusen constants retaining or even increasing the accuracy of the resistance to temperature conversion.U članku su uspoređene najčešće metode pretvorbe izmjerenog otpora platinskog senzora u vrijednost temperature u odnosu na novi adaptivni algoritam u kompaktnom pretvorniku temperature. Hardverska osnova pretvornika temperature je mikroupravljač s ulazno-izlaznim sklopovima koji generiraju izlazni signal proporcionalan izmjerenoj temperaturi. Kompaktni pretvornik temperature ima dimenzijska ograničenja kako bi se mogao montirati u glavu industrijskog termometra, a mikroupravljač je ograničen glede raspoložive struje napajanja te memorijskih resursa. Platinski temperaturni senzori normirani su Callendar-Van Dusenovom jednadžbom s definiranim koeficijentima A, B i C. Povećanje ukupne točnosti mjerenja temperature moguće je usklađivanjem karakteristika senzora s pretvornikom temperature na koji se priključuje. Kalibracijom platinskog senzora odrede se pripadni koeficijenti CvD jednadžbe koji se mogu upisati u pretvornik temperature. Prikazana nova programska metoda omogućuje korištenje navedenih koeficijenata uz zadržanu ili čak i povećanu točnost pretvorbe otpora u temperaturu

Naïve matrix multiplication versus Strassen algorithm in multi-thread environment

U zadnjih nekoliko desetljeća, računalna se snaga znatno povećala. Najveće brzine i snaga su i dalje rezervirani za super-računala, ali snažna računala su dostupna kućnim i amaterskim korisnicima već neko vrijeme. Obični korisnici uglavnom koriste samo mali dio računalnih resursa koji su im dostupni; čak i kod najvećih zahtjeva, dobar dio tih resursa ostaje neiskorišten. Djelomično je to uzrokovano lošim programiranjem . Većina programera i dalje koristi jedno-nitno programiranje iako su platforme za paralelno programiranje široko dostupne već duže vrijeme. Ovaj članak opisuje korištenje jedne takve platformu (.NET Framework) da se skrati vrijeme potrebno za računanje rezultata množenja matrica, vrlo čestog postupka. Članak pokušava prikazati rezultate koji se mogu postići korištenjem uobičajene opreme i lako dobavljive programske podrške.In last few decades computational power of computers has greatly increased. Highest speeds and power are still reserved for super-computers, but high-speed computers have been available for home and amateur users for some time. Normal user most of the time uses only a small amount of computational resources available; even in cases of high-strain, a good part of these resources stays unused. This is partly a result of poor programming. Most of programmers still use single-threaded programming although platforms for parallel programming have been widely available for long time. This article describes using one such platform (.NET Framework) to decrease time needed for multiplication of matrices. This article tries to present what results can be achieved using common equipment and easily acquirable software

Naïve matrix multiplication versus Strassen algorithm in multi-thread environment

U zadnjih nekoliko desetljeća, računalna se snaga znatno povećala. Najveće brzine i snaga su i dalje rezervirani za super-računala, ali snažna računala su dostupna kućnim i amaterskim korisnicima već neko vrijeme. Obični korisnici uglavnom koriste samo mali dio računalnih resursa koji su im dostupni; čak i kod najvećih zahtjeva, dobar dio tih resursa ostaje neiskorišten. Djelomično je to uzrokovano lošim programiranjem . Većina programera i dalje koristi jedno-nitno programiranje iako su platforme za paralelno programiranje široko dostupne već duže vrijeme. Ovaj članak opisuje korištenje jedne takve platformu (.NET Framework) da se skrati vrijeme potrebno za računanje rezultata množenja matrica, vrlo čestog postupka. Članak pokušava prikazati rezultate koji se mogu postići korištenjem uobičajene opreme i lako dobavljive programske podrške.In last few decades computational power of computers has greatly increased. Highest speeds and power are still reserved for super-computers, but high-speed computers have been available for home and amateur users for some time. Normal user most of the time uses only a small amount of computational resources available; even in cases of high-strain, a good part of these resources stays unused. This is partly a result of poor programming. Most of programmers still use single-threaded programming although platforms for parallel programming have been widely available for long time. This article describes using one such platform (.NET Framework) to decrease time needed for multiplication of matrices. This article tries to present what results can be achieved using common equipment and easily acquirable software

Visual Diagnostics Based on Image Wavelet-Transform

Abstract The image processing described in this paper is used for visual quality control in ceramic tile production. The tiles surface quality is described by the surface defects. The described image processing is based on the discrete wavelet transform method. The diagnostic algorithm is described. It is based on comparing of the wavelet coefficients of the original image without surface defects and the real images of ceramic tiles. The method is verified by using the artificial defects on the image and sensitivity testing on failure contrast and size is done. The algorithm is evaluated experimentaly using the real tile images. The analysis of the detection capabilities and sensitivity expressed in nondetected failures and false proclaimed defect is done also. Optimal connection between the segment size and DSL for each type of surface failure could be used to make efficient system for quality control and failure classification in automated production process