A general problem in automation is the correct supply of assembly parts to assembly units. At the same time it is of decisive importance that the stable orientation as well as eventual bad quality characteristics of the assembly parts can be recognized to prevent a faulty assembly, a standstill of machine, or, in the worst case, a destruction of the assembly unit. In practice, among mechanical devices, optoelectronic inspectautomates are used more
and more to solve this problem of correct sorting of assembly parts. These optoelectronic devices are checking the stable orientation and corresponding desired quality characteristics of assembly parts touch-less and automated. For solving this task it is necessary to calculate some features of the assembly part that has to be checked in the first step. The second step is to use these calculated features for a safe and stable classification of an assembly part. The calculation of the necessary features as well as the classification have to be realized in real-time. The amount of time that has to be kept, typically ranges from 5ms to 200ms depending on the assembly part that has to be checked and depending on the configuration of the optoelectronic inspectautomate. For that purpose features are calculated for a realized classification with which it is possible to check the stable orientation and corresponding desired quality characteristics for a multitude of assembly parts. But it is not possible to realize a safe and stable classification with these features, where the several stable orientations and with it the corresponding desired quality characteristics can only be classified
because of small details in the acquired images of the assembly part to be checked. In the cases of application where these described features cannot be used for a safe and stable classification it is necessary to introduce an additional feature with which it is possible to fulfill these applications with optoelectronic inspectautomates, because of its special characteristics. THerefore it is possible to increase the field of applications for such devices. This paper will introduce the feature Region of Interest which gives the possibility of using small details of an assembly part for a safe and stable classification of its stable orientation and quality characteristics. Additionally, this paper will describe the general implementation of the featuer Region of Interest for an optoelectronic inspectautomate.
Various methods for multivariate calibration like Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) are evaluated for their use in the field of pattern classification. These methods have the advantage that they can deal with high-dimensional feature spaces and multi-collinear data, since they inherently reduce the dimension of the feature space to represent it by one single dimension. Additionally, they yield very simple linear classifiers, which can be used for real-time calculation. These properties make the methods particularly useful in the field of image processing, where one often find high-dimensional spaces with linearly dependent data and usually we have tight requirements on computational complexity.
The process of automatically finding objects is very important for tracking and grasping objects using computer vision. Object finding is on the one hand a very time consuming process and on the other hand very sensitive to varying environmental conditions. Hence initialization is often performed manually. This work presents a method that operates automatically, threshold-free, and uses only model based information. Region based attributes such as color or texture are used in combination with a multi-spectral classification method to build a gray scale image, where regions with a high likelihood for the object searched are bright and the other parts are dark. To adapt to the ambient lighting situation a color measurement and a full matrix color transform are proposed and evaluated. First test runs show that this approach is fast and robust and enables the successful and automatic object finding.
KEYWORDS: Calibration, Inspection, Digital signal processing, Image processing, Data modeling, Signal processing, Optical inspection, Mathematical modeling, Reliability, Data conversion
Surface defects on metallic and non-metallic components can have serious effects on the reliability of the complete system. Typical examples are pistons with surface defects that cause scratching of the cylinder surface. For this reason it is of utmost importance to detect such defects as early as possible in the production process. So far, no real time solutions exist that fully satisfy industrial requirements in terms of speed, accuracy and reliability. Hence it is common to use visual inspection by humans which is error prone and causes additional personal costs. In this paper a new approach to obtain real time behaviour of industrial image processing systems by using multivariate techniques is presented. This methodology is originally used in chemometrics for statistical evaluation of measurement data and is now applied to image processing to take advantage of the high numerical efficiency of the underlying mathematics. Multivariate techniques can be applied to both the problem of automatic identification and classification of surface defects with digital images. The key to the envisaged real time ability is the high numerical efficiency of the proposed multivariate method. It manages defect detection with vector/matrix multiplication only - no calculation of powers or exponential functions is required. This enables efficient real time implementations on DSP platforms which are profiled for this type of calculations.
The possibility of connecting sensor and actuator systems to complete networks has led to new hardware/software structures in industrial applications. Smooth integration of high bandwidth optical sensors and devices into such systems was an almost impossible task due to problems of interfacing optical components to available bus systems with transfer rates below 1 Mbit/s. Attempts are going on to build up high bandwidth deterministic bus systems that deal with enormous data rates e.g. given by visual sensors like cameras. This would lead to even worse problems in processing all the data in central computing units. This paper introduces a new concept of how to connect high data rate optical devices to currently available low cost bus systems. The concept is based on dramatically reducing data rates by supplying sensor/actuator peripherals with their own adequate computing power. Raw data transfer is replaced by doing intelligent communication within the interconnecting network on a higher level of abstraction. Computing power moves into periphery making raw sensors intelligent ones. The paper shows that the system of interconnected intelligent sensors offers a high degree of efficiency, flexibility in connecting the parts together and scalability in a natural manner. The paper figures out that integration of optical high bandwidth devices into networking systems is no longer impossible. The reduction to an absolute minimum of communication effort allows the usage of cheap well known bus systems which leads to high acceptance in industry.
This paper discusses color camera based temperature measurement. Usually, visual imaging and infrared image sensing are treated as two separate disciplines. We will show, that a well selected color camera device might be a cheaper, more robust and more sophisticated solution for optical temperature measurement in several cases. Herein, only implementation fragments and important restrictions for the sensing element will be discussed. Our aim is to draw the readers attention to the use of visual image sensors for measuring thermal radiation and temperature and to give reasons for the need of improved technologies for infrared camera devices. With AVL-List, our partner of industry, we successfully used the proposed sensor to perform temperature measurement for flames inside the combustion chamber of diesel engines which finally led to the presented insights.
KEYWORDS: Cameras, Sensors, Signal processing, Image processing, Imaging systems, Line scan image sensors, Digital signal processing, Inspection, Interfaces, Control systems
In production and assembly processes, part-recognition is an important task in the field of quality assurance, determination of position errors and sorting of the inspected parts. The benefits and the conditions of part-recognition vary in a wide range, which led to the availability of numerous systems based on different technologies. Depending on the problem which has to be solved you have to compare the several technologies and to choose the systems which solves the given problem best. For example mechanical separation has their great power in robustness and throughput rate. But depending on geometry of the parts such system can be very complex. Furthermore an additional disadvantage is the fact that its not easy to detect lacks of quality. In the worst case such a defective part can cause a destruction of the assembly machine. If high flexibility or better accuracy is requested, other technologies like an optical system have to be used. The selection of an appropriate, cost-effective system is a very difficult and time-consuming task. If it is decided to use an optical system then in many cases a special system has to be constructed. Our approach is not to develop a further system for a special problem, but to provide a system for various applications. Therefore our demands for such a system are that it is open, scaleable and intelligent.
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