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

Quality and inspection of machining operations: Review of condition monitoring and CMM inspection techniques 2000 to present

In order to consistently produce quality parts, many aspects of the manufacturing process must be carefully monitored, controlled, and measured. The methods and techniques by which to accomplish these tasks has been the focus of numerous studies in recent years. With the rapid advances in computing technology, the complexity and overhead that can be feasibly incorporated in any developed technique has dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. Moreover, due to the relative significance of two particular machining aspects, this review focuses specifically on research publications pertaining to using tool condition monitoring and coordinate measurement machines to improve the machining process. Tool condition has a direct effect on part quality and is discussed first. The application of tool condition monitoring as it applies to turning, drilling, milling, and grinding is presented. The subsequent section provides recommendations for future research opportunities. The ensuing section focuses on the use of coordinate measuring machines in conjunction with machining and is subdivided with respect to integration with machining tools, inspection planning and efficiency, advanced controller feedback, machine error compensation, and on-line tool calibration, in that specific order and concludes with recommendations regarding where future needs remain

A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the de-noised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments

Tool wear monitoring for face milling process with intelligent algorithms

Today, machining processes are among the most common and important industrial operations. Considering the wide applications of the machining processes, the importance of having an accurate and reliable monitoring system is clear. Therefore, several researches and studies have been performed to respond to this demand. Despite considerable efforts in this field, the achievement is not satisfactory and still the request for having an accurate, reliable, automatic, inexpensive and robust monitoring algorithm is remained without a good answer. This study tries to design monitoring algorithms with aforementioned specifications. The monitoring algorithms predict the amount of tool flank wear in the face milling process. They are designed based on the pattern recognition concept. The algorithms analyze signals in four steps: preprocessing, feature extraction, feature selection and classification. Descriptors, wavelet transform and S-Transform are applied for feature extraction. Principal component analysis (PCA) and independent component analysis (ICA) perform the feature selection step. Neural network (NN), which is an artificial intelligence method, classifies the data and makes the algorithms intelligent. By combining these methods, five intelligent algorithms are developed. The results show that the most accurate algorithm between these five algorithms is the combination of S-Transform, ICA and NN. Results also confirm the good performance of S-Transform for feature extraction comparing with the wavelet transform or descriptors. Applying the best designed algorithm, the effect of sensor fusion on the accuracy of algorithm and the ability of monitoring algorithm for working in different operating conditions are studied as well. It is also shown that the accuracy of the best designed algorithm for indicating the tool status, sharp or dull, is better than the accuracy of predicting the value of the tool wear. Applying S-Transform for machining monitoring and designing five intelligent, practical, inexpensive and accurate algorithms for tool wear prediction can be considered as the key outcomes of this thesis

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the de-noised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments

Tool wear monitoring in end milling of mould steel using acoustic emission

Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features

Tool wear monitoring in end milling of mould steel using acoustic emission

Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features