MAT-714: CONDITION ASSESSMENT AND DETERIORATION PREDICTION TOOLS FOR CONCRETE BRIDGES: A NEW LOOK

Structural problems created by corrosion, ageing, aggressive environments, material defects and unforeseen mechanical or seismic loads can compromise the serviceability and safety of bridges. The importance of an effective bridge-management system (BMS) cannot be overstated, especially in light of the recent collapse of bridges in North America and elsewhere. Several technologies are available for assessing the condition of concrete bridges and a number of deterioration models are used to predict future bridge conditions and estimate associated funding requirements. This paper critically reviews the different available condition assessment and deterioration prediction approaches for concrete bridges. The potential applications of condition assessment technologies with particular focus on their advantages and limitations are presented. The various types of deterioration models are discussed and compared. The findings indicate that: (i) non-destructive testing (NDT) methods and structural health monitoring (SHM) systems can play a major role in effectively evaluating the conditions of concrete bridges; (ii) mechanistic models for deterioration prediction embrace a reliability-based approach that can provide bridge owners and maintenance personnel with an improved tool to assess bridge conditions and to make decisions regarding their maintenance; and (iii) automated data collection and interpretation analysis is needed for improved BMS. The challenges associated with the different technologies and models are outlined. Furthermore, to empower bridge asset managers in making more informed decisions, recommendations are made on the selection of appropriate evaluation and prediction models that meet desired service goals

Platform for Monitoring and Comparing Machining Processes in Terms of Energy Efficiency

This paper presents a smart energy consumption monitoring platform for machining processes with a unique electrical energy consumption indicator (EECI) for the evaluation and comparison of machining processes in terms of energy efficiency. The purpose of the developed smart monitoring platform with the integrated EECI and the Industry 4.0 digital technologies is to raise the level of efficiency of the machining process and thus to stimulate more sustainable and environmentally friendly machining. The energy consumption monitoring of a milling process is performed by connecting a machine tool with integrated sensors for measuring electrical power and cutting force to a cloud platform by using resources for signal acquiring and data acquisition. The platform includes applications for energy consumption monitoring and/or analysis. Results of four machining experiments are presented to demonstrate the effectiveness of the proposed energy consumption monitoring in machining and the applicability of the newly introduced EECI at the engineering and the operational level

Hybrid modeling to support the smart manufacturing: concepts, theoretic contributions and real-case applications about Hybrid and Wisdom-based Systems

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A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment

Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app

Cyberspace and Organizational Structure: An Analysis of the Critical Infrastructure Environment

Now more than ever, organizations are being created to protect the cyberspace environment. The capability of cyber organizations tasked to defend critical infrastructure has been called into question by numerous cybersecurity experts. Organizational theory states that organizations should be constructed to fit their operating environment properly. Little research in this area links existing organizational theory to cyber organizational structure. Because of the cyberspace connection to critical infrastructure assets, the factors that influence the structure of cyber organizations designed to protect these assets warrant analysis to identify opportunities for improvement. This thesis analyzes the cyber‐connected critical infrastructure environment using the dominant organizational structure theories. By using multiple case study and content analysis, 2,856 sampling units are analyzed to ascertain the level of perceived uncertainty in the environment (complexity, dynamism, and munificence). The results indicate that the general external environment of cyber organizations tasked to protect critical infrastructure is highly uncertain thereby meriting implementation of organic structuring principles

Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management

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

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.publishedVersio