Leonardo Nicolosi

New CNN based algorithms for the full penetration hole extraction in laser welding processes: Experimental results.

In this paper the results obtained by the use of new CNN based visual algorithms for the control of welding processes are described. The growing number of laser welding applications from automobile production to micro mechanics requires fast systems to create closed loop control for error prevention and correction. Nowadays the image processing frame rates of conventional architectures [1] are not sufficient to control high speed laser welding processes due to the fast fluctuation of the full penetration hole [3]. This paper focuses the attention on new strategies obtained by the use of the Eye-RIS system v1.2 which includes a pixel parallel Cellular Neural Network (CNN) based architecture called Q-Eye [2]. In particular, new algorithms for the full penetration hole detection with frame rates up to 24 kHz will be presented. Finally, the results obtained performing real time control of welding processes by the use of these algorithms will be discussed.

A novel spatter detection algorithm based on typical cellular neural network operations for laser beam welding processes

Real-time monitoring of laser beam welding (LBW) has increasingly gained importance in several manufacturing processes ranging from automobile production to precision mechanics. In the latter, a novel algorithm for the real-time detection of spatters was implemented in a camera based on cellular neural networks. The latter can be connected to the optics of commercially available laser machines leading to real-time monitoring of LBW processes at rates up to 15 kHz. Such high monitoring rates allow the integration of other image evaluation tasks such as the detection of the full penetration hole for real-time control of process parameters.

Cellular Neural Network (CNN) based control algorithms for omnidirectional laser welding processes: Experimental results

The high dynamics of laser beam welding (LBW) in several manufacturing processes ranging from automobile production to precision mechanics requires the introduction of new fast real time controls. In the last few years, algorithms for the control of constant-orientation LBW processes have been introduced. Nevertheless, some real life processes are also performed changing the welding orientation during the process. In this paper experimental results obtained by the use of a new CNN based strategy for the control of curved welding seams are discussed. It is based on the real time adjustment of the laser power by the detection of the full penetration hole in process images. The control algorithm has been implemented on the Eye-RIS system v1.2 leading to a visual closed loop control solution with controlling rates up to 6 kHz.

New CNN based algorithms for the full penetration hole extraction in laser welding processes

In this paper new CNN based visual algorithms for the control of welding processes are proposed. The high dynamics of laser welding in several manufacturing processes ranging from automobile production to precision mechanics requires the introduction of new fast real time controls. In the last few years, analogic circuits like Cellular Neural Networks (CNN) have obtained a primary place in the development of efficient electronic devices because of their real-time signal processing properties. Furthermore, several pixel parallel CNN based architectures are now included within devices like the family of EyeRis systems [1]. In particular, the algorithms proposed in the following have been implemented on the EyeRis system v1.2 with the aim to be run at frame rates up to 20 kHz.

Real-Time Control of Laser Beam Welding Processes: Reality

Cellular neural networks (CNN) are more and more attractive for closed-loop control systems based on image processing because they allow for the combination of high computational power and short feedback times. This combination enables new applications, which are not feasible for conventional image processing systems. Laser beam welding (LBW), which has been largely adopted in the industrial scenario, is an example for such processes. Concerning the latter, monitoring systems using conventional cameras are quite common, but they do a statistical postprocess evaluation of certain image features for quality control purposes. Earlier attempts to build closed-loop control systems failed due to the lack of computational power. In order to increase controlling rates and decrease false detections by a more robust evaluation of the image feature, strategies based on CNN operations have been implemented in a cellular architecture called Q-Eye. They allow enabling the first robust closed-loop control system adapting the laser power by observing the full penetration hole (FPH) in the melt. In this paper, the algorithms adopted for the FPH detection in process images are described and compared. Furthermore, experimental results obtained in real-time applications are also discussed.

Omnidirectional algorithm for the full penetration hole extraction in laser welding processes

In this paper a new Cellular Neural Network (CNN) based visual algorithm for welding processes is proposed. The idea described in [1] can be used in processes, whose welding direction has a constant orientation well known a priori. The algorithm proposed in the following is omnidirectional in the sense that it does not depend on the welding direction. This fact enables closed loop control systems for welding processes with curved seeds. On Eye-RIS systems [2] processing times of about 110 μs are achievable for both acquisition and evaluation of full frame images.

A monitoring system for laser beam welding based on an algorithm for spatter detection

This paper deals with the realization of a visual monitoring system for the real time detection of spatters in laser beam welding (LBW). Spatters deteriorate the corrosion resistance and the aesthetics of the welding result. Therefore, the real time detection of spatters allows providing on-line quality information about the process, thus reducing material waste in production chains. The proposed Cellular Neural Network (CNN) based algorithm has been implemented in the Eye-RIS vision system (VS). Monitoring rates up to 15 kHz have been reached, allowing the integration of the spatter detection with the evaluation of additional image features, e.g. the full penetration hole (FPH).

A camera based closed loop control system for keyhole welding processes: Algorithm comparison

Real time monitoring of laser welding has a more and more importance in several manufacturing processes ranging from automobile production to precision mechanics. Despite the huge improvement in welding technology, sophisticated image based closed loop control systems have not been integrated in commercially available equipments yet. Due to the high dynamics of laser beam welding (LBW) processes, robust closed loop control systems require fast real time image processing with frame rates in the multi kilo Hertz range. In the last few years, some new high speed Cellular Neural Network (CNN) based algorithms for the full penetration hole detection in keyhole welding processes have been introduced. In particular, they can be distinguished in two categories: Orientation dependent and orientation independent algorithms. The former can be used only for the welding of straight lines, while the latter has been implemented for the control of curved weld seams. Both algorithms have been used to build up a real time closed loop control system for LBW processes. An algorithm comparison by the description of some experimental results is addressed in this paper.

Multi-feature detection for quality assessment in laser beam welding: Experimental results

Laser beam welding (LBW) has been largely used in manufacturing processes ranging from automobile production to precision mechanics. The complexity of LBW requires the development of strategies for the real-time control of the process. Most of the available feedback systems lack of temporal and/or spatial resolution and, therefore, they hardly allow observing more than one characteristic of the process. In the last years, we proposed some high-speed visual algorithms for image feature extraction from process images. The detection of the full penetration hole (FPH) allowed controlling the laser power at rates of up to 14 kHz. Another strategy enables observing the occurrence of spatters at monitoring rates of 15 kHz. The achievement of these results was made possible by the adoption of a visual system including a focal plane processor programmable by typical Cellular Neural Network (CNN) operations. This paper is focused on a new visual algorithm for the simultaneous detection of FPH and spatters, which led to real-time control rates of about 8 kHz. Besides the algorithm description, some interesting experimental results will be presented.