Pattern recognition of micro and macro grinding phenomenon with a generic strategy to machine process monitoring

Abstract In modern manufacturing environments waste is an issue of great importance. Specifically the research in this thesis looks at issues in establishing the initial steps to gain a generic process monitoring system that ensures that grinding is both optimised but not the determent where costly malfunctions mean the scrapping and re-melting of expensive quality intensive materials. The research conducted in this thesis investigates the process of cutting, ploughing and rubbing during single grit scratch tests. These investigations meant the correlation between physical material removal phenomenon and the emitted material dislocations gained from acoustic emission extraction. The initial work looked at different aerospace materials and the distinction of cutting, ploughing and rubbing during single grit radial scratch tests. This initial work provided novel results not seen in this area before and paved the way for more robust results in investigating the same phenomena during horizontal single grit scratch tests. This work provided more robust classification of cutting, ploughing and rubbing and transferred directly to grinding pass cuts from 1um and 0.1mm depth cuts respectively. In using robust classifiers such as the Neural Network and novel classifiers such as non-linear data paradigms, Fuzzy-c clustering with Genetic Algorithm optimisation, cutting, ploughing and rubbing phenomenon was investigated. These investigations showed that more cutting occurs when there is moreinteraction between grit and workpiece based on the increase depth of cut. Other thesis results investigated a generic classifier using Genetic Programming to classify multiple anomaly phenomena. This work can be bridged together with the unit event grit classification work

Pattern recognition of micro and macro grinding phenomenon with a generic strategy to machine process monitoring

Abstract In modern manufacturing environments waste is an issue of great importance. Specifically the research in this thesis looks at issues in establishing the initial steps to gain a generic process monitoring system that ensures that grinding is both optimised but not the determent where costly malfunctions mean the scrapping and re-melting of expensive quality intensive materials. The research conducted in this thesis investigates the process of cutting, ploughing and rubbing during single grit scratch tests. These investigations meant the correlation between physical material removal phenomenon and the emitted material dislocations gained from acoustic emission extraction. The initial work looked at different aerospace materials and the distinction of cutting, ploughing and rubbing during single grit radial scratch tests. This initial work provided novel results not seen in this area before and paved the way for more robust results in investigating the same phenomena during horizontal single grit scratch tests. This work provided more robust classification of cutting, ploughing and rubbing and transferred directly to grinding pass cuts from 1um and 0.1mm depth cuts respectively. In using robust classifiers such as the Neural Network and novel classifiers such as non-linear data paradigms, Fuzzy-c clustering with Genetic Algorithm optimisation, cutting, ploughing and rubbing phenomenon was investigated. These investigations showed that more cutting occurs when there is moreinteraction between grit and workpiece based on the increase depth of cut. Other thesis results investigated a generic classifier using Genetic Programming to classify multiple anomaly phenomena. This work can be bridged together with the unit event grit classification work

Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals

By using acoustic emission (AE) it is possible to control deviations and surface quality during micro milling operations. The method of micro milling is used to manufacture a submillimetre waveguide where micro machining is employed to achieve the required superior finish and geometrical tolerances. Submillimetre waveguide technology is used in deep space signal retrieval where highest detection efficiencies are needed and therefore every possible signal loss in the receiver has to be avoided and stringent tolerances achieved. With a sub-standard surface finish the signals travelling along the waveguides dissipate away faster than with perfect surfaces where the residual roughness becomes comparable with the electromagnetic skin depth. Therefore, the higher the radio frequency the more critical this becomes. The method of time-frequency analysis (STFT) is used to transfer raw AE into more meaningful salient signal features (SF). This information was then correlated against the measured geometrical deviations and, the onset of catastrophic tool wear. Such deviations can be offset from different AE signals (different deviations from subsequent tests) and feedback for a final spring cut ensuring the geometrical accuracies are met. Geometrical differences can impact on the required transfer of AE signals (change in cut off frequencies and diminished SNR at the interface) and therefore errors have to be minimised to within 1 µm. Rules based on both Classification and Regression Trees (CART) and Neural Networks (NN) were used to implement a simulation displaying how such a control regime could be used as a real time controller, be it corrective measures (via spring cuts) over several initial machining passes or, with a micron cut introducing a level plain measure for allowing setup corrective measures (similar to a spirit level)

Estimation of CNC Grinding Process Parameters Using Different Neural Networks

Continuation of research on solving the problem of estimation of CNC grinding process parameters of multi-layer ceramics is presented in the paper. Heuristic analysis of the process was used to define the attributes of influence on the grinding process and the research model was set. For the problem of prediction - estimation of the grinding process parameters the following networks were used in experimental work: Modular Neural Network (MNN), Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN) and Self-Organizing Map Neural Network (SOMNN). The experimental work, based on real data from the technological process was performed for the purpose of training and testing various architectures and algorithms of neural networks. In the architectures design process different rules of learning and transfer functions and other attributes were used. RMS error was used as a criterion for value evaluation and comparison of the realised neural networks and was compared with previous results obtained by Back-Propagation Neural Network (BPNN). In the validation phase the best results were obtained by Back-Propagation Neural Network (RMSE 12,43 %), Radial Basis Function Neural Network (RMSE 13,24 %,), Self-Organizing Map Neural Network (RMSE 13,38 %) and Modular Neural Network (RMSE 14,45 %). General Regression Neural Network (RMSE 21,78 %) gave the worst results

Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel

Surface quality measures such as roughness, and especially its uncertain character, affect most magnetic non-destructive testing methods and limits their performance in terms of an achievable signal-to-noise ratio and reliability. This paper is primarily focused on an experimental study targeting nuclear reactor materials manufactured from the milling process with various machining parameters to produce varying surface quality conditions to mimic the varying material surface qualities of in-field conditions. From energising a local area electromagnetically, a receiver coil is used to obtain the emitted Barkhausen noise, from which the condition of the material surface can be inspected. Investigations were carried out with the support of machine-learning algorithms, such as Neural Networks (NN) and Classification and Regression Trees (CART), to identify the differences in surface quality. Another challenge often faced is undertaking an analysis with limited experimental data. Other non-destructive methods such as Magnetic Adaptive Testing (MAT) were used to provide data imputation for missing data using other intelligent algorithms. For data reinforcement, data augmentation was used. With more data the problem of ‘the curse of data dimensionality’ is addressed. It demonstrated how both data imputation and augmentation can improve measurement datasets

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

The Public Service Media and Public Service Internet Manifesto

This book presents the collectively authored Public Service Media and Public Service Internet Manifesto and accompanying materials.The Internet and the media landscape are broken. The dominant commercial Internet platforms endanger democracy. They have created a communications landscape overwhelmed by surveillance, advertising, fake news, hate speech, conspiracy theories, and algorithmic politics. Commercial Internet platforms have harmed citizens, users, everyday life, and society. Democracy and digital democracy require Public Service Media. A democracy-enhancing Internet requires Public Service Media becoming Public Service Internet platforms – an Internet of the public, by the public, and for the public; an Internet that advances instead of threatens democracy and the public sphere. The Public Service Internet is based on Internet platforms operated by a variety of Public Service Media, taking the public service remit into the digital age. The Public Service Internet provides opportunities for public debate, participation, and the advancement of social cohesion. Accompanying the Manifesto are materials that informed its creation: Christian Fuchs’ report of the results of the Public Service Media/Internet Survey, the written version of Graham Murdock’s online talk on public service media today, and a summary of an ecomitee.com discussion of the Manifesto’s foundations

Structural Health Monitoring Damage Detection Systems for Aerospace

This open access book presents established methods of structural health monitoring (SHM) and discusses their technological merit in the current aerospace environment. While the aerospace industry aims for weight reduction to improve fuel efficiency, reduce environmental impact, and to decrease maintenance time and operating costs, aircraft structures are often designed and built heavier than required in order to accommodate unpredictable failure. A way to overcome this approach is the use of SHM systems to detect the presence of defects. This book covers all major contemporary aerospace-relevant SHM methods, from the basics of each method to the various defect types that SHM is required to detect to discussion of signal processing developments alongside considerations of aerospace safety requirements. It will be of interest to professionals in industry and academic researchers alike, as well as engineering students. This article/publication is based upon work from COST Action CA18203 (ODIN - http://odin-cost.com/), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation