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

In-process tool condition forecasting based on a deep learning method

It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing

Machine Learning Assisted Framework for Advanced Subsurface Fracture Mapping and Well Interference Quantification

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., maximizing the fluid flow and capacity of the wells) is to be able to quantify and map the subsurface natural fracture systems. This needs to be done both locally, i.e., near the wellbore and generally in the scale of the wellpad, or region. In this study, the conventional near wellbore natural fracture mapping techniques is first discussed and integrated with more advanced technologies such as application of fiber optics, specifically Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS), to upscale the fracture mapping in the region. Next, a physics-based automated machine learning (AutoML) workflow is developed that incorporates the advanced data acquisition system that collects high-resolution drilling acceleration data to infer the near well bore natural fracture intensities. The new AutoML workflow aims to minimize human bias and accelerate the near wellbore natural fracture mapping in real time. The new AutoML workflow shows great promise by reducing the fracture mapping time and cost by 10-fold and producing more accurate, robust, reproducible, and measurable results. Finally, to completely remove human intervention and consequently accelerate the process of fracture mapping while drilling, the application of computer vision and deep learning techniques in new workflows to automate the process of identifying natural fractures and other lithological features using borehole image logs were integrated. Different structures and workflows have been tested and two specific workflows are designed for this purpose. In the first workflow, the fracture footprints on actual acoustic image logs (i.e., full, or partial sigmoidal signatures with a range of amplitude and vertical and horizontal displacement) is detected and classified in different categories with varying success. The second workflow implements the actual amplitude values recorded by the borehole image log and the binary representation of the produced images to detect and quantify the major fractures and beddings. The first workflow is more detailed and capable of identifying different classes of fractures albeit computationally more expensive. The second workflow is faster in detecting the major fractures and beddings. In conclusion, regional subsurface natural fracture mapping technique using an integration of conventional logging, microseismic, and fiber optic data is presented. A new AutoML workflow designed and tested in a Marcellus Shale gas reservoir was used to predict near wellbore fracture intensities using high frequency drilling acceleration data. Two integrated workflows were designed and validated using 3 wells in Marcellus Shale to extract natural fractures from acoustic image logs and amplitude recordings obtained during logging while drilling. The new workflows have: i) minimized human bias in different aspects of fracture mapping from image log analysis to machine learning model selection and hyper parameter optimization; ii) generated and quantified more accurate fracture predictions using different score matrices; iii) decreased the time and cost of the fracture interpretation by tenfold, and iv) presented more robust and reproducible results

Running Dynamics of Rail Vehicles

The investigation of rail vehicle running dynamics plays an important role in the more than 200 year development of railway vehicles and infrastructure. Currently, there are a number of new requirements for rail transport associated with the reduced environmental impact, energy consumption and wear, whilst increasing train speed and passenger comfort. Therefore, the running dynamics of rail vehicles is still a research topic that requires improved simulation tools and experimental procedures. The book focuses on the current research topics in railway vehicles running dynamics. Special attention is given to high-speed railway transport, acoustic and vibrational impact of railway transport to the surroundings, optimization of energy supply systems for railway transport, traction drives optimization and wear of wheels and rails