A world according to artificial neural networks, Journal of Telecommunications and Information Technology, 2003, nr 3

This paper presents results from a preliminary study in the field of artificial neural networks (ANN). The overall aim of our work relates to the field of cognitive science. In this wider framework we try to investigate, reason about, and model cognitive processes in order to obtain a better understanding of the major processing device involved – the human brain. In terms of content this paper presents a novel ANN learning approach. Note that through-out the paper we assume supervised learning. In contrast to the classical ANN learning approach where an ANN algorithm alters an initial random weight assignment until a reasonable solution to a problem is obtained this approach does not alter the initial random weight assignment at all, but provides a solution to the problem by transforming the actual input data. The approach is applied to perceptrons and adalines and its quality is demonstrated on simple classification problems

Swarm intelligence for network routing optimization, Journal of Telecommunications and Information Technology, 2005, nr 3

This paper presents the results of a comparative study of network routing approaches. Recent advances in the field suggest that swarm intelligence may offer a robust, high quality solution. The overall aim of the study was to develop a framework to facilitate the empirical evaluation of a swarm intelligence routing approach compared to a conventional static and dynamic routing approach. This paper presents a framework for the simulation of computer networks, collection of performance statistics, generation and reuse of network topologies and traffic patterns

Learning defects from aircraft NDT data

Non-destructive evaluation of aircraft production is optimised and digitalised with Industry 4.0. The aircraft structures produced using fibre metal laminate are traditionally inspected using water-coupled ultrasound scans and manually evaluated. This article proposes Machine Learning models to examine the defects in ultrasonic scans of A380 aircraft components. The proposed approach includes embedded image feature extraction methods and classifiers to learn defects in the scan images. The proposed algorithm is evaluated by benchmarking embedded classifiers and further promoted to research with an industry-based certification process. The HoG-Linear SVM classifier has outperformed SURF-Decision Fine Tree in detecting potential defects. The certification process uses the Probability of Detection function, substantiating that the HoG-Linear SVM classifier detects minor defects. The experimental trials prove that the proposed method will be helpful to examiners in the quality control and assurance of aircraft production, thus leading to significant contributions to non-destructive evaluation 4.0

Quality Prediction of Continuous Ultrasonic Welded Seams of High-Performance Thermoplastic Composites by means of Artificial Intelligence

Thermoplastic composites (TCs) are a famous choice when it comes to high performance designs for industrial applications. Since the growing demand on the use of this material, it is important to be able to evaluate suitable processing technologies. One of those technologies is continuous ultrasonic welding (CUSW) which creates continuous joints, also called seams, between two or more TCs parts. In CUSW mechanical oscillations are applied to the material and result in melting and connecting of the welding parts. The approach to predict joint strength (qualities) of continuous ultrasonic welded TCs by training different neural networks is investigated in this study. Quality class prediction around 72 % accuracy is achieved with a fully connected neural network. Concluding, quality prediction of welded TCs with the help of artificial intelligence seems to be a suitable approach for quality observation but more research could lead to more reliable neural networks for industrial applications

Near net shape thermoplastic preforming with continuously automated cutting and robotic pick and place processes

Today’s automated production lines for carbon fiber reinforced thermoplastic (CFRTP) parts of the aerospace industry are mostly designed for one special part or application. Changing part geometry or material would lead to a complete re-design of the whole process chain. Here comes the flexible and highly automated the DLR process into game: A continuously automated production line “from fabric delivery to the near net shape pre-form – ready for consolidation”. With the paper and presentation, the authors would like to contribute to the industry’s increasing needs of continuously automated but highly flexible process chains for variable high vol-ume CFRTP parts

A study on fractal dimensions and convergence in fuzzy control systems, Journal of Telecommunications and Information Technology, 2002, nr 3

This paper addresses a problem in the area of intelligent, knowledge-based systems, namely the generation of knowledge, by presenting a proposal for the automation of this task. The proposed approach is limited however by focusing on fuzzy control systems (FCSs). Results obtained from different experimental investigations indicate the potential of the approach

Inline Quality Control for Thermoplastic Automated Fibre Placement

Aerospace production is relying heavily on quality assurance, especially for lightweight materials such as carbon fiber reinforced plastics (CFRPs). Due to the sequential organization of manufacturing and quality assurance lead times for parts and machine downtime are increased. An example is the highly automated process of thermoset tape laying, where every ply is manually inspected. Customized solutions to integrate quality assurance do exist, however mainly for specific processes and hardware. This leads to costly, isolated and non-transferable systems. At the Center of Lightweight Production Technology in Augsburg, Germany a more versatile and modular concept for inline data acquisition, storage and evaluation is investigated. On the basis of a laser line and camera system a generic inline measurement system for automated fiber placement (AFP) is developed, that is capable to inspect a variety of quality aspects such as positional tolerances and material properties. In a database driven approach the collected data is stored and analyzed. This paper presents the generic inline measurement system, the data storage system and the obtained measurement results for a thermoplastic AFP process. Additionally suggestions for future improvements and possible applications are described

Inline Quality Control for Thermoplastic Automated Fibre Placement

Aerospace production is relying heavily on quality assurance, especially for lightweight materials such as carbon fiber reinforced plastics (CFRPs). Due to the sequential organization of manufacturing and quality assurance lead times for parts and machine downtime are increased. An example is the highly automated process of thermoset tape laying, where every ply is manually inspected. Customized solutions to integrate quality assurance do exist, however mainly for specific processes and hardware. This leads to costly, isolated and non-transferable systems. At the Center of Lightweight Production Technology in Augsburg, Germany a more versatile and modular concept for inline data acquisition, storage and evaluation is investigated. On the basis of a laser line and camera system a generic inline measurement system for automated fiber placement (AFP) is developed, that is capable to inspect a variety of quality aspects such as positional tolerances and material properties. In a database driven approach the collected data is stored and analyzed. This paper presents the generic inline measurement system, the data storage system and the obtained measurement results for a thermoplastic AFP process. Additionally suggestions for future improvements and possible applications are described