Monitoring Single-point Dressers Using Fuzzy Models

AbstractGrinding causes progressive dulling and glazing of the grinding wheel grains and clogging of the voids on the wheel's surface with ground metal dust particles, which gradually increases the grinding forces. The condition of the grains at the periphery of a grinding wheel strongly influences the damage induced in a ground workpiece. Therefore, truing and dressing must be carried out frequently. Dressing is the process of conditioning the grinding wheel surface to reshape the wheel when it has lost its original shape through wear, giving the tool its original condition of efficiency. Despite the very broad range of dressing tools available today, the single-point diamond dresser is still the most widely used dressing tool due to its great versatility. The aim of this work is to predict the wear level of the single-point dresser based on acoustic emission and vibration signals used as input variables for fuzzy models. Experimental tests were performed with synthetic diamond dressers on a surface-grinding machine equipped with an aluminum oxide grinding wheel. Acoustic emission and vibration sensors were attached to the tool holder and the signals were captured at 2MHz. During the tests, the wear of the diamond tip was measured every 20 passes using a microscope with 10 to 100 X magnification. A study was conducted of the frequency content of the signals, choosing the frequency bands that best correlate with the diamond's wear. Digital band-pass filters were applied to the raw signals, after which two statistics were calculated to serve as the inputs for the fuzzy models. The results indicate that the fuzzy models using the aforementioned signal statistics are highly effective for predicting the wear level of the dresser

Smart process monitoring of machining operations

The following thesis explores the possibilities to applying artificial intelligence techniques in the field of sensory monitoring in the manufacturing sector. There are several case studies considered in the research activity. The first case studies see the implementation of supervised and unsupervised neural networks to monitoring the condition of a grinding wheel. The monitoring systems have acoustic emission sensors and a piezoelectric sensor capable to measuring electromechanical impedance. The other case study is the use of the bees' algorithm to determine the wear of a tool during the cutting operations of a steel cylinder. A script permits this operation. The script converts the images into a numerical matrix and allows the bees to correctly detect tool wear

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

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Desenvolvimento e validação de um método dinâmico, baseado em emissão acústica, para a caracterização em processo de rebolos convencionais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2015A tecnologia de emissão acústica (EA) é utilizada no desenvolvimento de um método dinâmico para caracterização em processo (DICM) da topografia de rebolos convencionais. Experimentos planejados são conduzidos em uma bancada de ensaios desenvolvida, contendo um software de aquisição de sinais de EA. A bancada de ensaios e o software de aquisição possibilitam o reconhecimento de interferências entre rebolo (vs= 30 m/s) e uma ponta de diamante, na faixa de deformação elástica das ferramentas. Os sinais de EARAW adquiridos de forma on-line e originados destas interferências são utilizados como dados de entrada para técnicas de processamento de sinal e para uma Rede Neural (RN). Ambas as análises são efetuadas fora do processo de retificação, representando um método dinâmico de caracterização pós-processo (DPCM) da topografia de rebolos. Os resultados do DPCM são validados através de medições específicas nas peças retificadas (p. ex., rugosidade, microscopia, camada termicamente afetada, desvio de forma) e em réplicas extraídas da topografia do rebolo. Com base nas técnicas de processamento de sinais validadas e propostas no DPCM,implementa-se o DICM. Para este método, desenvolve-se uma bancada experimental baseada na aquisição de sinais de múltiplos transdutores,na qual sinais de EA e de força são medidos. A bancada experimental eseu software de aquisição permitem a caracterização em processo da topografia de rebolos convencionais através da extração de informações quantitativas dos sinais on-line de EARAW adquiridos durante asinterferências entre rebolo (vs= 30 m/s) e ponta de diamante na faixa de 1 µm. A informação quantificada associada com a topografia do rebolo é baseada na análise em processo dos sinais de EARAW nos domínios dotempo e frequência. Os resultados de ambas as análises são obtidos de forma instantânea em processo sem reduzir a velocidade de corte do rebolo, e sem alterar o setup do processo de retificação. Visando-se otimizar o DICM, os principais fatores que apresentam influência sobre a resposta no domínio do tempo são analisados através de uma Análise Fatorial Fracionada. O DICM é validado correlacionando-se a informação quantitativa obtida da topografia, com as análises pós-processo de sinais de força de retificação e com medições da rugosidade efetiva do rebolo (parâmetro Rts). Abstract : A Dynamic In-process Characterization Method (DICM) based on acoustic emission (AE) is developed and validated, aiming at the in-process appraisal of the topography of conventional grinding wheels. For implementing the method, planned experiments are carried out by firstly developing an AE-based experimental rig with its particular software application. This enables to recognize shallow interferences amid the grinding wheel (vs= 30 m/s) and a diamond tip, in the elasticdeformation range of the tools. The on-line acquired AERAW signalsderived from such interferences are used as input data for signal processing techniques and a Neural Network (NN). Both analyses areimplemented out of the grinding process and therefore consist in aDynamic Post-process Characterization Method (DPCM). The DPCM´sresults are validated by measuring both the ground workpieces (i.e. roughness, microscopy, thermally affected layer and form deviation) and the replicas extracted from the grinding wheel´s topography. Based on the validated signal processing techniques proposed by the DPCM, the DICM is implemented. This is achieved by employing a transducer-fused experimental rig in which both AE and force signals are measured. The experimental rig and its developed software application enable in-process characterization of the topography of the conventional grinding wheel by extracting quantitative information from the AERAW signalswhich are on-line acquired during the interferences between the grinding wheel (vs= 30 m/s) and a diamond tip in a range of 1 µm. The quantified information associated with the grinding wheel´s topography is based on both a time domain and a frequency domain in-process analysis. Theresulting outputs from these analyses are obtained instantaneously in-process by neither interrupting the grinding process nor decelerating the grinding wheel´s cutting speed. In order to define an optimizedexperimental condition to assess the grinding wheel´s topography, the main factors which present direct influence on the time domain output were analyzed by using a Fractional Factorial Analysis. The DICM is validated by correlating the obtained quantified information from thegrinding wheel´s topography with both the post-process evaluation of the grinding cutting force and the post-process measurements of the effective roughness of the grinding wheel (parameter Rts)

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