Non-destructive evaluation of an infusion process using capacitive sensing technique

In this study, a capacitive sensing based non-destructive evaluation technique is applied to a vacuum assisted resin infusion process for the fabrication of glass fibre reinforced composites, as such different steps of the fabrication process (the injection of resin, the curing and the post curing) can be better understood to increase the quality of the fabricated part and reduce the fabrication costs. An interdigital coplanar capacitive sensor was designed, fabricated, and embedded in the glass fibre reinforced composites. Experimental data clearly shows different stages of the resin infusion process: wetting of the glass fibres marked by rapid increase of capacitance; domination of ionic conduction at the early stage of the cure when the resin is still in a liquid state; the vitrification point, indicating a transition of the resin from a gelly state to a glassy state, marked by the relatively big decrease in capacitance; further polymerization during post-curing, marked by a peak in capacitance at the beginning of post-curing cycle, and finally the completion of the cure marked by the saturation of capacitance to a final value. The different phenomena observed during the experiment can be used as a tool for in situ on-line monitoring of composites cure

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

A strategy for achieving manufacturing statistical process control within a highly complex aerospace environment

This paper presents a strategy to achieve process control and overcome the previously mentioned industry constraints by changing the company focus to the process as opposed to the product. The strategy strives to achieve process control by identifying and controlling the process parameters that influence process capability followed by the implementation of a process control framework that marries statistical methods with lean business process and change management principles. The reliability of the proposed strategy is appraised using case study methodology in a state of the art manufacturing facility on Multi-axis CNC machine tools

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS