An experimental study of micro-end-milling operations

Cutting tools less than 2mm diameter can be considered as micro-tool. Microtools are used in variety of applications where precision and accuracy are indispensable. In micro-machining operations, a small amount of material is removed and very small cutting forces are created. The small cross sectional area of the micro-tools drastically reduces their strength and makes their useful life short and unpredictable; so cutting parameters should be selected carefully to avoid premature tool breakage. The main objective of this study is to develop new techniques to select the optimal cutting conditions with minimum number of experiments and to evaluate the tool wear in machining operations. Several experimental setups were prepared and used to investigate the characteristics of cutting force and AE signals during the micro-end-milling of different materials including steel, aluminum and graphite electrodes. The proposed optimal cutting condition selection method required fewer experiments than conventional approaches and avoided premature tool breakage. The developed tool wear monitoring technique estimated the used tool life with ±10% accuracy from the machining data collected during the end-milling of non-metal materials

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost

Cutting tool wear progression index via signal element variance

This paper presents a new statistical-based method of cutting tool wear progression in a milling process called Z-rotation method in association with tool wear progression. The method is a kurtosis-based that calculates the signal element variance from its mean as a measurement index. The measurement index can be implicated to determine the severity of wear. The study was conducted to strengthen the shortage in past studies notably considering signal feature extraction for the disintegration of non-deterministic signals. The Cutting force and vibration signals were measured as a tool of sensing element to study wear on the cutting tool edge at the discrete machining conditions. The monitored flank wear progression by the value of the RZ index, which then outlined in the model data pattern concerning wear and number of samples. Throughout the experimental studies, the index shows a significant degree of nonlinearity that appears in the measured impact. For that reason, the accretion of force components by Z-rotation method has successfully determined the abnormality existed in the signal data for both force and vibration. It corresponds to the number of cutting specifies a strong correlation over wear evolution with the highest correlation coefficient of R2 = 0.8702 and the average value of R2 = 0.8147. The index is more sensitive towards the end of the wear stage compared to the previous methods. Thus, it can be utilised to be the alternative experimental findings for monitoring tool wear progression by using threshold values on certain cutting condition

Artificial Intelligence and Industry 4.0

Cílem této práce je poskytnout přehled aplikací metod umělé inteligence v kontextu průmyslu 4.0. První kapitola je věnována definici konceptu průmyslu 4.0, předchozímu vývoji průmyslu a zařazení vědního oboru umělé inteligence do tohoto konceptu. Druhá kapitola je zaměřena na rešerši aplikací metod umělé inteligence v oblasti obrábění, výrobního průmyslu, automatizace a energetiky. Závěr práce je věnován zhodnocení metod, jejich výhod a úskalí z pohledu jednotlivých praktických aplikací a zmiňuje možné směry budoucího vývoje.The aim of this work is to provide an overview of the application of artificial intelligence methods in the context of Industry 4.0. The first chapter defines the concept of industry 4.0, previous development of the industry and inclusion of the scientific field of artificial intelligence in this concept. The second chapter is focused on the applications of artificial intelligence methods in the field of machining, manufacturing industry, automation and energetics. The work concludes with evaluation of methods, their advantages and disadvantages from the point of view of individual practical applications and mentions possible directions of future development.

Cutting tool condition monitoring of the turning process using artificial intelligence

This thesis relates to the application of Artificial Intelligence to tool wear monitoring. The main objective is to develop an intelligent condition monitoring system able to detect when a cutting tool is worn out. To accomplish this objective it is proposed to use a combined Expert System and Neural Network able to process data coming from external sensors and combine this with information from the knowledge base and thereafter estimate the wear state of the tool. The novelty of this work is mainly associatedw ith the configurationo f the proposeds ystem.W ith the combination of sensor-baseidn formation and inferencer ules, the result is an on-line system that can learn from experience and can update the knowledge base pertaining to information associated with different cutting conditions. Two neural networks resolve the problem of interpreting the complex sensor inputs while the Expert System, keeping track of previous successe, stimatesw hich of the two neuraln etworks is more reliable. Also, mis-classificationsa re filtered out through the use of a rough but approximate estimator, the Taylor's tool life equation. In this study an on-line tool wear monitoring system for turning processesh as been developed which can reliably estimate the tool wear under common workshop conditions. The system's modular structurem akesi t easyt o updatea s requiredb y different machinesa nd/or processesT. he use of Taylor's tool life equation, although weak as a tool life estimator, proved to be crucial in achieving higher performance levels. The application of the Self Organizing Map to tool wear monitoring is, in itself, new and proved to be slightly more reliable then the Adaptive Resonance Theory neural network

Multi-categories tool wear classification in micro-milling

Ph.DDOCTOR OF PHILOSOPH

Cognitive Sensor Monitoring of Machining Processes for Zero Defect Manufacturing

The topic of this thesis, focused on cognitive sensor monitoring of machining processes for zero defect manufacturing, has been addressed within the framework of the international research project EC FP7 CP-IP “IFaCOM – Intelligent Fault Correction and self Optimizing Manufacturing systems” (2011-2015; FoF NMP – 285489) and the national MIUR PON Project on “Development of eco-compatible materials and technologies for robotised drilling and assembly processes – STEP FAR” (2014-2016). The vision of the IFaCOM project is to achieve near zero defect level of manufacturing with particular emphasis on the production of high value, large variety and high performance products. This goal is achieved through the development of improved methodologies for monitoring and control of the performance of manufacturing processes with the aim to detect abnormal process conditions leading to defects on the produced parts. The overall aim of the STEP FAR project is the study of issues related to drilling and cutting techniques of advanced lightweight components, such as composite material parts, and their relative assembly, using cooperating anthropomorphic robots. The use of innovative materials and processes developed in this research will lead to a reduction in weight and environmental impact in the construction and maintenance of primary aircraft structures. At least a 5% reduction in weight of the structures is foreseen without increase of costs (a possible rise in the cost of raw materials is compensated with the reduction of process costs). In aeronautical industry the reduction of the weight of the aircraft is becoming an increasingly important aim both for environmental requirements (lower emissions) and contraction of the management costs (lower fuel consumption). Therefore new structural architectures through the use of innovative materials and technologies have been developed. One of the innovative processes analysed in this project is the drilling via machining of carbon fibre reinforced plastic (CFRP) stack-ups. In the framework of these projects, this thesis work is focused on the development of cognitive condition monitoring procedures for zero defect machining processes with reference to two different industrial manufacturing applications. The thesis is organized as follows: Chapter 2 reviews the general concept of sensor monitoring of manufacturing processes and provides a comprehensive survey of sensor technologies, advanced signal processing techniques, sensor fusion approach, and cognitive decision making strategies for process monitoring. In Chapter 3, the Strecon industrial case, as a partner of the IFaCOM project, is discussed and analysed. The STRECON end-user case is focused on improving repeatability and predictability of the surface finish produced by a Robot Automated Polishing (RAP) process. In order to establish a robust method for the detection of the polishing process end-point, i.e. the determination of the right moment for tool and abrasive paste change, STRECON sensor system selection focuses on monitoring the progress of the surface quality during the polishing process by means of variation in VQCs (Vital Quality Characteristics), i.e. roughness and gloss of the polished surface. The output data have been used to train a neural network. The employed NN learning procedure was the leave-k-out method where k cases from the training set are put aside in turn, while the other cases are used for NN training. In Chapter 4, the Alenia Aermacchi industrial case, as coordinator and partner of the STEP FAR project, is discussed and analysed. The Alenia Aermacchi user case is based on the analysis of drilling of stacks made of two overlaid carbon fibre reinforced plastic composite laminates. In this case, a neural network based cognitive paradigm based on a bootstrap procedure has been used for the identification of correlations with tool wear development and product hole quality. Finally, Chapter 5 reports the concluding remarks and future developments of this work

Classificação multiclasse baseada em séries temporais multivariadas do estágio de operação de ferramentas de corte de torno

The majority of mechanical components went through a machining process during their manufacturing. Therefore, manufacturing processes with inadequate condition tools are likely to induce unexpected operational interruptions, accidents, product quality, and economic losses. Accordingly, the ability to classify fault imminences can result in cost reduction, along with productivity and safety increase. This work presents a low-cost data acquisition system and aims to discuss a model based on digital signal processing, the SelfOrganised Direction Aware Data Partitioning Algorithm (SODA) and machine learning techniques, including time series Feature Extraction based on Scalable Hypothesis tests (TSFRESH) and Principal Components Analysis (PCA), to solve this problem. Taking into consideration the real-time monitoring, features selection and features relevance analysis are essential as it can lead to a dimensionality and data flow rate reduction. The model proposed in this work can identify the patterns that distinguish the cutting tool’s flank wear in a multi-class scenario as adequate, intermediate, and inadequate conditions, achieving satisfactory performances in all cases and allowing to prevent fault occurrences.Grande parte dos componentes mecânicos fabricados para uso industrial sofrem algum processo de usinagem durante a sua fabricação. Portanto, processos de manufatura executados com ferramentas em condições inadequadas de operação apresentam uma alta probabilidade de sofrer interrupções inesperadas, acidentes, baixa qualidade no produto fabricado e perdas econômicas. Consequentemente, a possibilidade de classificar iminências de falha pode resultar em redução de custos, aumento de produtividade e uma maior segurança para os operadores das máquinas. Esse trabalho apresenta um protótipo de baixo custo para aquisição de dados e um modelo de inteligência computacional baseado em processamento digital de sinais, no algoritmo de particionamento de dados AutoOrganizável Ciente de Direção (SODA - Self-Organised Direction Aware) e técnicas de aprendizagem de máquina, incluindo Extração de Atributos de Series Temporais baseado em testes de Hipóteses Escalonáveis (TSFRESH - Time Series Feature Extraction based on Scalable Hypothesis tests) e Análise de Componentes Principais (PCA - Principal Components Analysis), para resolver esse problema. Considerando uma aplicação do modelo em tempo real, a análise da relevância e seleção dos atributos se torna imprescindível pela capacidade de reduzir a dimensionalidade das séries temporais observadas. O modelo proposto nesse trabalho é capaz de identificar os padrões que diferenciam o desgaste de flanco de uma ferramenta de corte em um cenário multiclasse como ferramenta em estágio adequado, intermediário e inadequado, alcançando resultados satisfatórios em todos os casos e permitindo a prevenção de falhas