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A phase-locked loop (PLL) is a nonlinear negative-feedback control system that synchronizes its output in frequency as well as in phase with its input PLLs are now widely used for the synchronization of power-electronics-based converters and also for monitoring and control purposes in different engineering fields In recent years, there have been many attempts to design more advanced PLLs for three-phase applications The aim of this paper is to provide overviews of these attempts, which can be very useful for engineers and academic researchers

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Three-Phase PLLs: A Review of Recent Advances

This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

A comprehensive review for industrial applicability of artificial neural networks

http://doras.dcu.ie/17608/1/Final_Version%2C_Review_paper.pdf

Optimization of different welding processes using statistical and numerical approaches - A reference guide

Devices, methods, and software are disclosed for determining dimensions of a physical object using a mobile computer equipped with a motion sensing device. In an illustrative embodiment, the mobile computer can comprise a microprocessor, a memory, a user interface, a motion sensing device, and a dimensioning program executable by the microprocessor. The processor can be in communicative connection with executable instructions for enabling the processor for various steps. One step includes initiating a trajectory tracking mode responsive to receiving a first user interface action. Another step includes tracking the mobile computer's trajectory along a surface of a physical object by storing in the memory a plurality of motion sensing data items outputted by the motion sensing device. Another step includes exiting the trajectory tracking mode responsive to receiving a second user interface action. Another step includes calculating three dimensions of a minimum bounding box corresponding to the physical object.

Measuring object dimensions using mobile computer

Artificial neural networks have been studied to determine their applicability to modeling and control of physical processes. Some basic concepts relating to neural networks and how they can be used to model weld-bead geometry in terms of the equipment parameters selected to produce the weld are explained. Approaches to utilizing neural networks in process control are discussed. The need for modeling transient as well as static characteristics of physical systems for closed-loop control is pointed out, and an approach to achieving this is presented. The performance of neural networks for modeling is evaluated using actual welding data. It is concluded that the accuracy of neural network modeling is fully comparable with the accuracy achieved by more traditional modeling schemes. >

Artificial neural networks applied to arc welding process modeling and control

Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding (VPPAW) process. Three areas of welding application were investigated: weld process modeling, weld process control, and weld bead profile analysis for quality control. Experiments and analysis confirm that artificial neural networks are powerful tools for analysis, modeling, and control applications. They are particularly attractive in view of their capabilities to process nonlinear and noisy data, learn from actual welding data, and execute at relatively high speed. It is shown that neural networks are capable of modeling parameters of the VPPAW process to on the order of 10% accuracy or better. The same was observed when neural networks were used to select welding equipment parameters and the resulting bead geometries were estimated. These performance figures suggest that a VPPA welding control system can be implemented based on neural network models and control mechanisms. >

Weld modeling and control using artificial neural networks

The present invention relates to a method and an apparatus for determining a three dimensional image of the a moving object by means of reflecting means such as mirrors and a planar beam of light such as a laser light. As the object enters the light a two dimensional image of the object is captured, wherein the image comprises both the reflection of the beam of light from the object and also the reflection from the reflecting means. By transformation procedure a two dimensional image is transformed to a two dimensional object image and based on plurality of such images a three dimensional image is defined from which the volume can be calculated. This can be used to determine the weight distribution of the object, which is important for portioning.

Laser mirror vision

A novel approach for inducing and monitoring oscillations in a molten weld pool is presented. Research efforts have illustrated that the weld pool resonates at natural frequencies that are related to its dimensions and state of penetration. This phenomenon may be used to monitor the weld pool, and particularly its depth of penetration, in a closed-loop feedback control system. The approach used to induce pool oscillations was to excite the weld pool with current pulses synchronized to the natural oscillations of the pool. Implementation of this synchronous weld pool pulsing technique was based on the use of a phase locked loop (PLL) system. The natural weld pool oscillations are used as the reference frequency source and a pulsing circuit is controlled by the PLL oscillator so that the arc current pulses repeatedly impact the pool after a fixed number of reference oscillation periods. An optical sensor detects the pool oscillations which are amplified, filtered, and limited to eliminate amplitude variations from the optical signal. A model of the weld pool is developed which uses a fluid droplet formulation for the relation of weld pool geometry and other physical parameters to the natural frequencies of the weld pool. Comparison of the weld pool's actual resonant frequency with the expected resonant frequency as predicted by weld pool geometry models and measurements of the pool width (or area) allows and assessment of the state of penetration of the weld pool into the workpiece. >

Synchronous weld pool oscillation for monitoring and control

The visual inspection of weld beads and subsequent evaluation of the weld quality is an integral part of the commercial welding environment. However, this inspection process tends to be both time and labor intensive. An automated system for the performance of this task has been developed. The sequence of events in the operation of the vision-based system are: (1) image capture; (2) image enhancement; (3) image processing; and (4) image evaluation. To minimize cost and complexity, the system uses conventional video camera and related hardware and software for the image-capture and image enhancement portion of the evaluation process. Various weld processes were observed to have certain characteristic features which were most relevant for the inspection and evaluation of the particular process. Image processing codes were written to extract those features of the weld beads and store this information in data files for subsequent assessment. Numerical algorithms were written, tailored to each of the weld processes, to perform the image evaluation portion of the quality evaluation process. This result is expressed as a relative quality rating which was found to correlate well with a quality rating derived by direct observation by a human inspector of various quality welds. The vision-based weld quality evaluation system has potential for use as a post-weld quality evaluation system, or, due to the high update rate of the overall vision system (>10 Hz), as part of a real-time control system.

Kristinn Andersen

Papers

Artificial neural networks applied to arc welding process modeling and control

Weld modeling and control using artificial neural networks

Laser mirror vision

Synchronous weld pool oscillation for monitoring and control

Automated visual inspection and interpretation system for weld quality evaluation