Stanje razvoja strojnog vida

Machine vision (system visional) it\u27s a apply computer vision in industry. While computer vision is focused mainly on image processing at the level of hardware, machine vision most often requires the use of additional hardware I/O (input/output) and computer networks to transmit information generated by the other process components, such as a robot arm. Machine vision is a subcategory of engineering machinery, dealing with issues of information technology, optics, mechanics and industrial automation. One of the most common applications of machine vision is inspection of the products such as microprocessors, cars, food and pharmaceuticals. Machine vision systems are used increasingly to solve problems of industrial inspection, allowing for complete automation of the inspection process and to increase its accuracy and efficiency. As is the case for inspection of products on the production line, made by people, so in case of application for that purpose machine vision systems are used digital cameras, smart cameras and image processing software. This paper presents the principle of image processing, the components of the system and possible applications of machine vision in the present.Strojni vid (vidni sustav) je primjena računalnog vida u industriji. Dok je računalni vid usmjeren uglavnom na obradu slike na razini hardvera, strojni vid najčešće zahtijeva korištenje dodatnog hardvera I / O (input / output) i računalnih mreža za prijenos podataka generiranih od strane drugih komponenata procesa, kao što je robotska ruka. Strojni vid je pod kategorija inženjerskog projektiranja, a bavi se pitanjima informatičke tehnologije, optike, mehanike i industrijske automatizacije. Jedna od najčešćih primjena strojnog vida je inspekcija proizvoda kao što su mikroprocesori, automobili, hrana i farmaceutski proizvodi. Sustavi strojnog vida koriste se sve više za rješavanje problema industrijske inspekcije, omogućujući potpunu automatizaciju procesa inspekcije i povećanje njezine točnosti i efikasnosti. Kao što je slučaj kod kontrole proizvoda na proizvodnoj liniji koju provode ljudi, tako se i u slučaju primjene sustava strojnog vida koriste digitalne kamere, pametne kamere i programi za obradu slike. U radu su prikazani princip obrade slike, komponente sustava i moguće primjene strojnog vida u sadašnjosti

In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing

Liquid Metal Jet Printing (LMJP) is a revolutionary three-dimensional (3D) printing technique in fast but low-cost additive manufacturing. The driving force is produced by magneto-hydrodynamic property of liquid metal in an alternating magnetic field. Due to its integrated melting and ink-jetting process, it can achieve 10x faster speed at 1/10th of the cost as compared to current metal 3D printing techniques. However, the jetting process is influenced by many uncertain factors, which impose a significant challenge to its process stability and product quality. To address this challenge, we present a closed-loop control framework by seamlessly integrating vision-based technique and neural network tool to inspect droplet behaviours and accordingly stabilize the printing process. This system automatically tunes the drive voltage applied to compensate the uncertain influence based on vision inspection result. To realize this, we first extract multiple features and properties from images to capture the droplet behaviour. Second, we use a neural network together with PID control process to determine how the drive voltage should be adjusted. We test this system on a piezoelectric-based ink-jetting emulator, which has a very similar jetting mechanism to the LMJP. Results show that significantly more stable jetting behavior can be obtained in real-time. This system can also be applied to other droplet related applications owing to its universally applicable characteristics

Simultaneous image color correction and enhancement using particle swarm optimization

Color images captured under various environments are often not ready to deliver the desired quality due to adverse effects caused by uncontrollable illumination settings. In particular, when the illuminate color is not known a priori, the colors of the objects may not be faithfully reproduced and thus impose difficulties in subsequent image processing operations. Color correction thus becomes a very important pre-processing procedure where the goal is to produce an image as if it is captured under uniform chromatic illumination. On the other hand, conventional color correction algorithms using linear gain adjustments focus only on color manipulations and may not convey the maximum information contained in the image. This challenge can be posed as a multi-objective optimization problem that simultaneously corrects the undesirable effect of illumination color cast while recovering the information conveyed from the scene. A variation of the particle swarm optimization algorithm is further developed in the multi-objective optimization perspective that results in a solution achieving a desirable color balance and an adequate delivery of information. Experiments are conducted using a collection of color images of natural objects that were captured under different lighting conditions. Results have shown that the proposed method is capable of delivering images with higher quality. © 2013 Elsevier Ltd. All rights reserved

The essence and applications of machine vision

Pojam strojne vizije (vizijskih sustava) obuhvaća industrijsku primjenu računalnih vizijskih sustava. Dok je računalna vizija usmjerena uglavnom na obradu slikovnih zapisa na hardverskoj razini, sustavi strojne vizije najčešće zahtijevaju uporabu dodatnog izlazno/ulaznog sučelja i računalnih mreža za prijenos podataka generiranih od strane drugih procesnih komponenti, primjerice robota, manipulatora itd. Jedan od najčešćih primjena strojne vizije jest kontrola kvalitete proizvoda, primjerice mikroprocesora, automobila, hrane i farmaceutskih proizvoda. Sustavi strojne vizije učestalo se upotrebljavaju za rješavanje problema industrijske kontrole, te omogućuju potpunu automatizaciju procesa i povećanje pouzdanosti i učinkovitosti. Takvi sustavi rabe digitalne fotoaparate, kamere i odgovarajući softver za obradu slikovnih zapisa kako kod ručne, tako i kod automatske kontrole na proizvodnoj liniji. U radu su opisana temeljna načela obrade slikovnih zapisa, dijelovi sustava i današnje mogućnosti primjene sustava strojne vizualizacije.Machine vision (system vision) comprises using computer vision in industry. While computer vision is focused mainly on image processing at the level of hardware, machine vision most often requires the use of additional hardware I/O (input/output) and computer networks to transmit information generated by the other process components, such as a robot arm. One of the most common applications of machine vision is inspection of the products such as microprocessors, cars, food and pharmaceuticals. Machine vision systems are used increasingly to solve problems of industrial inspection, allowing for complete automation of the inspection process and to increase its accuracy and efficiency. In the case of manual inspection on the production line as well as in the case of application of machine vision systems, digital cameras, smart cameras and image processing software have been used. This paper presents the principle of image processing, the components of the system and possible applications of machine vision in the present

Quality Assessment of Photographed 3D Printed Flat Surfaces Using Hough Transform and Histogram Equalization

Automatic visual quality assessment of objects created using additive manufacturing processes is one of the hot topics in the Industry 4.0 era. As the 3D printing becomes more and more popular, also for everyday home use, a reliable visual quality assessment of printed surfaces attracts a great interest. One of the most obvious reasons is the possibility of saving time and filament in the case of detected low printing quality, as well as correction of some smaller imperfections during the printing process. A novel method presented in the paper can be successfully applied for the assessment of at surfaces almost independently on the filament's colour. Is utilizes the assumption about the regularity of the layers visible on the printed high quality surfaces as straight lines, which can be extracted using Hough transform. However, for various colours of filaments some preprocessing operations should be conducted to allow a proper line detection for various samples. In the proposed method the additional brightness compensation has been used together with Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Results obtained for the database of 88 photos of 3D printed samples, together with their scans, are encouraging and allow a reliable quality assessment of 3D printed surfaces for various colours of filaments

Defect Detection and Localization of Nonlinear System Based on Particle Filter with an Adaptive Parametric Model

A robust particle filter (PF) and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals