Abstract

     Artificial Intelligence (AI) and Machine learning (ML) represents an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems. Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts. In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.

Introduction

     CNC machining operation is one of the most important part-production methodologies, and it is often referred to as the engine of modern manufacturing processes. The automotive and medical sectors, aerospace, gas and oil, and warehousing services, are using the CNC machining operations to create parts in different applications [1]. CNC machining is generally used in manufacture of every machine, molded part, or finished product as one of the most important manufacturing processes. CNC machinery has paved the way in manufacturing and machining, allowing businesses to achieve their goals and targets in a variety of ways. However, because manufacturing methodologies is always evolving and new technologies are being introduced, it is critical to consider future of CNC machining operations [2, 3]. Machine learning (ML) is the study of computer algorithms that gives computers the capacity to automatically learn from data and prior experiences in order to find patterns and make predictions without human involvement. The ML and applications in different areas of study are considered to be a component of artificial intelligence [4], [5], [6].

     Machine learning and artificial intelligence in particular raise plenty of concerns about the future of CNC machining operations and how these concepts will evolve future works of manufacturing companies [7]. The way a machine learns, adapts, and optimizes output can also be influenced by real-time data, analytics, and deep learning. Data sets are essential for operators to understand how a machine works and, eventually, how a whole floor of machines works together [8, 9]. Due to the development of affordable, reliable, and resilient sensors and acquisition and communication systems, novel implementations of machine learning approaches for tool condition monitoring can be presented [10]. Machine learning systems are capable of completely examine data and identify various types of areas which should be modified. Machine tools are increasingly being equipped with edge computing options to record internal drive signals at high frequency in order to supply the necessary vast quantity of data for the use of machine learning techniques in manufacturing [11]. Productivity and efficiency are two areas where artificial intelligence can modify CNC machine tools operations in order to enhance accuracy of CNC machining operations[12]. Machines can generate and analyze production data and provide real-time findings to human operators are effective devices for increasing productivity in part production processes. As a result, shop owners can quickly adjust the way a machine operates using the modified data generated by advanced machine learning algorithms in order to enhance productivity of part manufacturing [13]. Having more knowledge and making better decisions in process planning strategies means less downtime on the work floor during process of part production. Production and maintenance process of part manufacturing using CNC machine tools can be developed using the machine learning and the artificial intelligence in order to enhance efficiency in part manufacturing operations [14].

Conclusion

     Machine learning and artificial intelligence are applied to various industrial applications in order to improve performances of industrial process. To increase accuracy as well as efficiency during CNC machining operations, different applications of the machine learning and artificial intelligence systems are studied in different research works. Reducing machine downtime, optimization of CNC machine tools, cutting tool wear prediction, cutting force model, CNC machine tool maintenance, monitoring of machining operations, surface quality prediction and energy prediction systems are some examples of machine leniting applications in the development of CNC machining operations. ML techniques are recently applied to energy consumption prediction models in order to decrease the energy consumption during CNC machining operations. Accuracy and radiality of energy consumption models are significantly enhanced using the ML methodologies in comparison to the tradition’s methods of energy usage predictions during CNC machining operations.

     The applications of machine learning and artificial intelligence systems in CNC machine tools are examined in this study by analyzing recent achievements from published papers. The main aim of this study is to provide an overview of current studies on machine learning and artificial intelligence techniques in CNC machining operations in order to provide a useful study for the researchers in the interesting field. Network of sensors and cloud data sources can connect CNC machines together can be employed to provide smart CNC machine tools. The machining industry’s efficiency can be increased as it transitions to smart machining techniques, allowing it to achieve self-optimization and adaption to uncontrolled circumstances. However, developing the applications of advanced machine learning systems in CNC machining operations as the combination of physical, computers, and networking process created challenges and difficulties regarding the safety and security of the web of data. In order to provide secure and advanced connections between the different CNC machine tool, the security of networks should be enhanced.

This paper presents a review of the contributions on entropy generation of nanofluids and hybrid nanofluids in the different types of thermal systems for different boundary conditions and physical situations . The relevant paper s are classified into three categories: entropy generation in minichannel, entropy generation macrochannel and entropy generation in cavities. The viscous dissipative, streamwise , electromagnetic effects, as well as nanoparticles concentration, the temperature and the flow regime on entropy generation , were analyzed. The reviewed literature indicates that the implementation of nanofluids/hybrid nanofluids in microchannels, minichannels , and cavities may be an important alternative to the traditional thermal systems and an interesting topic of study.

Introduction

Micro/nano heat transfer has been an important issue for researchers in the last decades. The microdevices (devices with dimensions between 1 ?? and 1 mm) are successfully employed in a variety of fields such as electronic s, aerospace , telecommunication, biomedical and automotive industries to enhance heat transfer for heating and cooling . The heat transfer efficiency can be improved by several methods, such as the use of extended surfaces, the application of vibration to heat transfer surfaces, and the use of mini/microchannels. Also, heat transfer efficiency of the microdevices can also be improved by enhanced thermo -physical properties of the working fluid, especially the thermal conductivity and the specific heat [1]. The working fluids with the thermo – physical properties improved are called nanofluid/hybrid nanofluid . By combining nanofluid/hybrid nanofluid with the small channel’s dimensions of the thermal devices, it is to obtain devices that provide compactness, low thermal resistance and efficiency. The entropy generation analysis is a useful tool for performance optimization of the thermal systems. It is known as the addition of nanoparticles into the base fluid could be influence the total entropy generation. Thus , the use the nanofluids in the thermal systems decrease the temperature of the system and finally the heat transfer contribution to the total entropy generation rate decreases, while nanoparticles added into the base fluid increase the viscosity of the working fluid leads to increase pressure drop in the system. Many researchers have studied the entropy generation in order to find optimum conditions for different thermal systems and only a few reviews have been devoted to this topic.

Wind energy as one of the renewable energies is serving as an indispensable role in generating new electric power. The worldwide installation of wind farms has considerably increased recently. To extract more wind resources, multi-megawatt wind turbines are usually designed and constructed with large rotors and slender tower. These flexible structures are susceptible to external dynamic excitations such as wind, wave and seismic loads. The excessive vibrations can compromise the wind energy conversion, lead to the structural fatigue damage and even result in the catastrophic failure of wind turbines in harsh environmental conditions. Various control devices have been proposed and used to mitigate the unwanted vibrations of wind turbines to enhance their safety and serviceability. This paper aims to provide a state-of-the-art review of the current vibration control techniques and their applications to wind turbines. Firstly, the widely used control strategies in engineering structures are briefly introduced. Their applications to suppress the adverse vibrations of the structural components of wind turbines, mainly the tower and blades, are then reviewed and discussed in detail. It can be concluded that the vibration mitigation of wind turbines is very challenging due to the fact that the dynamic behaviours of wind turbines are very complicated, which are associated with the aerodynamics, rotation of the blades, interaction between the tower and rotating blades, and soil-structure interaction, etc. Moreover, it is a challenge to straightforwardly use many of the conventional control devices because of the limited spaces in the tower and blades.

Introduction

Wind energy as one of the renewable energies plays an attractive means to generate new electric power. Wind farms have experienced rapid growth and expansion recently especially in the last decade. As reported by the Global Wind Energy Council, the worldwide installations of wind turbines reached about 539 GW at the end of 2017, with an increase of 2155% compared to that in 2011 [1]. Moreover, numbers of wind turbines are erected far away from coastlines to more efficiently extract the huge wind resources. The offshore wind power reached a historical record in 2017 with 4334 MW new installations, and the cumulative capacity was 18,814 MW [1]. Fig. 1 shows the global annual and cumulative installations of wind turbines from 2001 to 2017, and the total installations of offshore wind farms in 2011–2017 are shown in Fig. 2. It can be seen that though the offshore wind capacity in 2017 was only about 3.5% of the total wind capacity, it is growing very quickly and with great prospects. Wind turbines can be broadly grouped into the horizontal and vertical axis categories depending on the orientation of the rotation axis of the blades. Fig. 3 shows the typical geometrical configurations of the horizontal and vertical axis wind turbines (HAWTs and VAWTs) [2]. As implied by the name, the blades of HAWTs rotate about the horizontal axis, and they are perpendicular to the direction of wind flow, while the blades of VAWTs rotate vertically and they are not required to face the wind. As the HAWTs dominate the utility-scale wind turbine market currently, only the research works on the vibration control of HAWTs are reviewed and discussed herein.

Wind conditions in the built environment are complex in nature and characterized by lower wind speeds and higher turbulence due to the presence of obstructions. A growing body of literature and research/testing activities related to performance evaluation of small wind turbines (SWTs) in urban wind conditions have inferred that urban installed SWTs are subjected to higher level of turbulence and face dynamic loading that impedes their performance, and reduces fatigue life. This paper reviews the diverse studies conducted on the application of SWT technology in the built environment to understand the characteristics of inflowing wind, their performance and identify the gaps in the knowledge. This review paper also investigates the extent to which the international design standard for SWTs, IEC 41400-2, is valid for urban installations. The findings from this review show that the wind models incorporated in IEC 61400-2 is not suitable for installation of SWTs in the built environment. The authors recommend a thorough study through measured data and characterization of urban wind to make current standard inclusive of wind classes that characterize urban wind conditions. Thus, SWT design can be made more consistent with urban wind conditions and their performance and reliability can be assured.

Introduction

With rapid growth in population, the global energy consumption is projected to increase by 56% between 2010 and 2040 [1]. In 2015, fossil fuel (coal, petroleum and natural gas) accounted for 78.4% of global final energy consumption, with the share of renewables (modern and traditional) and nuclear power at 19.3% and 2.3% respectively[2]. Worldwide, the share of renewable energy will increase to address global climate change by 2030. Modern renewable energy is being used increasingly in four distinct markets: power generation, heating and cooling, transport fuels, and rural/off-grid energy services [3]. In recent years, progress has been made in increasing the renewable energy share in the power 103 sector particularly in the wind, solar photovoltaic (PV) and hydropower sectors [4]. In 2014, 104 the total renewable power capacity was 712 GW out of which 370 GW was from wind energy through utility scale wind turbines [5]. The total global capacity of wind energy reached 432 106 GW at the end of 2015, representing a cumulative growth of 17%. Although large-scale generation using wind energy has taken shape, there are some problems for their sustainable development. For example, the main barriers of large scale on-shore wind farm are available sites, impact of grid power quality, public acceptability and losses in transmission and distribution of electricity to the consumers [6]. One alternative that reduces some of these barriers is the application of small wind turbine (SWT) technology.

1- Introduction

2- Sustainable control objectives in freeway traffic control

3- The modelling framework for sustainable freeway traffic control

4- Sustainable freeway control strategies

5- Remarks on sustainable freeway traffic control

6- Future steps towards a greener mobility

References

Abstract

Sustainability is a key point in the design and management of mobility and traffic systems, which affects also the development of traffic control strategies for freeway networks. According to sustainability-related concepts, freeway traffic controllers should be devised not only for maximally exploiting the road capacity and decreasing vehicles travel delays, but also for reducing pollutant emissions, fuel consumptions, accidents, noise, and so on. This paper analyses the state of the art of freeway traffic control strategies characterised by sustainability-related objectives, in particular referred to traffic emissions and traffic safety, by providing a classification of the research papers in this area. The final part of the paper highlights the main research challenges for modelling and control techniques brought by the introduction of emerging information and communication technologies, that are becoming more and more widespread and are transforming the concept of vehicles in intelligent and connected agents, able to measure the traffic state and to implement specific traffic control policies.

Introduction

Nowadays, the greater awareness about the dangerous effects produced by myopic policies aimed only at pursuing short-term benefits has brought the issue of sustainable development to the centre of the political agenda of many countries worldwide. In recent years, in fact, the theme of sustainability has been addressed considering various fields of the human activity and proposing several definitions and targets (as those proposed in Kates, Parris, & Leiserowitz, 2005 and Giovannoni & Fabietti, 2014). Although concerning different aspects, such definitions agree that only the actions aimed at achieving the optimal relation between the humans and the environment can ensure a fair development of contemporary and future society. Yet, the concept of sustainability is extremely complex, since it requires the fulfilment of a mix of often conflicting objectives. The creation of sustainable cities, the promotion of a more equitable economic growth and the environmental safeguard represent just some of the objectives identified in United Nations (2017) to accomplish the 2030 Agenda for Sustainable Development (United Nations General Assembly, 2015). The implementation of the 2030 Program requires a rethinking of transportation systems in a more open-minded and environmentfriendly perspective. In this sustainable vision, the transport system should enhance the social equity and ensure a safer mobility by increasing the access to services and to the most disadvantaged areas, without damaging the environment and people’s health. This ambitious journey towards sustainable mobility starts from the awareness that the transport system, in particular the road sector, is today one of the leading sources of greenhouse gases and pollution, involving serious consequences both for human health and for the environment (Hoek, Brunekreef, Goldbohm, Fischer, & Brandt, 2002). A survey developed by the World Health Organization, on the basis of 2012 data, revealed that 3.7 millions of premature deaths may be attributed to environmental pollution. Most of these deaths and diseases are caused by the prolonged exposure to substances resulting from the use of fossil fuels.

1- Introduction

2- Desiccant cooling systems

3- Key factors for choosing desiccant materials in cooling systems

4- Advanced desiccant materials

5- Discussion and future works

6- Conclusion

References

Abstract

A desiccant cooling system is among the best options to overcome future energy demand issues while considering human comfort by its ability to control both temperature and humidity. Although desiccant materials remarkably affect the performance of desiccant-based cooling systems, most studies focus on the design. This paper presents a review of the recent investigations on advanced desiccant materials regarding the improvement of the material properties for desiccant cooling application, emphasizing other important parameters, such as environmental conditions, cost, and design to achieve optimal performance. The preparation and performance of silica- and carbon-based desiccant materials are discussed. The current most suitable desiccant materials under different conditions for desiccant air conditioning systems and suggestions for future works are presented.

Introduction

Modern lifestyle and industrialization come with the consumption of a huge amount of energy for cooling systems to provide thermal comfort in buildings and working places. Recent statistics from IEA claims triple demands on air conditioner system worldwide by the next 30 years [1], which means that almost three-fold energy usage and global warming will result if no significant changes will be made in modifying or replacing available air conditioner systems. This fact opens many research challenges for scientists and industries to study and develop eco-friendly and sustainable alternative for conventional vapor compression air conditioning systems. The desiccant cooling system is among these alternatives with very interesting concepts. Desiccant cooling system is not only energy efficient but also could effectively control the indoor humidity, which is another important factor for human comfort aside from temperature. The ability of separate control of latent and sensible loads, which removes the overcooling and reheating of process air, is an advantage of desiccant cooling system over conventional air conditioning systems, because of a huge decrease in energy consumption and increase in coefficient of performance (COP) [2]. The utilization of desiccant cooling system in industries such as pharmaceutical, food, laboratories, and chemical industries, which needs very dry air to maintain severe hygienic conditions constantly, is highly preferred because of its strong control for adjusting humidity and its cost-effectiveness.

Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies.

Introduction and motivation

Nowadays, wind energy is one of the most important renewable energy sources. In 2016, wind energy systems (WES) provided more than 420 GW, and this is expected to rise to more than 1000 GW in the 2030s [1]. WES are undergoing a modernization process where the number of requirements has increased to ensure efficient energy production [2,3]. There has been an increase in WES and their complexity, as well as the demand for new techniques and methods to improve reliability [4], maintenance [5] and investments [6], leading to greater competitiveness in the energy market. The wind energy market, as one of the most exploitable and growing markets, requires both technical and economic advances. Regarding the technical aspects, efforts are oriented towards harnessing the wind to a maximum level. There are many issues addressed in the literature, such as the aerodynamic optimization of wind turbines (WT) [7], the optimization of blade shapes [8], the study of power curve under different circumstances [9], the optimization of WT position in a wind farm [10], etc. With respect to the economic issues, the main objective is to maximize profits obtained from the available resources. For this purpose, the literature covers topics such as wind speed modelling [11], strategies based on energy price forecasting [12], the study of the interactions between wind energy and the power market [13], wind turbine life cycle analysis [14], etc. This paper shows an exhaustive review of the current techniques and methods concerning these issues employing artificial neural networks (ANN) WTs are equipped with a large number of devices to evaluate the humidity, temperature, vibration, etc. [15]. Data acquisition systems measure all the variables in order to determine the system condition [16]. Data processing requires robust algorithms [17] that enable as much information as possible to be gathered from the available data [18]. Machine learning algorithms are widely employed due to their ability to process a large amount of data, ANNs being one of the most employed methods [19]. ANNs are complex structures based on biological neurons. These structures provide a good solution to problems that cannot be analytically defined. An ANN consists of neurons which are simple processing units, and weighted connections between those neurons. A typical structure corresponds to the multilayer perceptron (MLP), shown in Fig. 1 [20]. The ANN receives a dataset and starts a training process to adjust the weights of the interconnections between neurons. The training will be supervised if the output is known, otherwise it will be named unsupervised training [21].

Abstract

A literature survey of the published work on drag reduction by Drag Reducing Agent (DRA) in two-phase flow is reviewed. Characteristics of the two-phase flow with drag reducing additives are described and the research approaches and methodology concerning drag reduction with additives in multiphase flow is introduced. Suggested mechanisms for drag reduction phenomena and procedure in two-phase flow are discussed. Some of the industrial application of the use of drag reducing additives in two-phase flow is explained. Finally, Recommendations, new suggested approaches for future research needs and potential areas that need further research is highlighted.

Introduction

One of the most fascinating advances in single-phase turbulence is the finding that the introduction of small amounts of long-chain polymers into a liquid flow can cause large decreases in the frictional resistance at the wall (Toms, 1948). Several studies with laser doppler velocimetry (Harder and Tiederman, 1991; Wei and Willmarth, 1992; Warholic et al., 1999) have revealed how the turbulence properties differ from those of the solvent.

Warholic and Hanratty used a solution of a co-polymer of polyacrylamide and sodium-acrylate (Percol 727) in water. They realized significant drag-reduction with a concentration as low as 0.25 ppm. The principal effect of the polymer is to reduce Reynolds shear stresses and velocity fluctuations in a direction normal to the wall. Maximum drag-reductions, for which the Reynolds stresses are approximately zero, were observed for polymer concentrations of 13 ppm and 50 ppm.