The position of the rotor (shaft) around its own axis was selected as physical variable in order to get a desired control, for which there were fixed four position sensors in opposite side to every electromagnet actuator, owing to capture its position as the reference from the shaft axis Y. However, through the position sensors that were based in nanostructure, it can be possible to correlate another important physical variables: Sound and vibration. In Figure 6 is depicted as the flowchart, the summary of the procedure that was applied in this research. It starts from configuration parameters, measure sensors data: positions and temperatures. From this can be possible to find weights for adaptive algorithm in order to get better position control. In spite of the error signal (between desired and measured positions) can also get some predictions to optimal positions, but the energy needed to send over every electromagnet get losses, such as by the heat, but not because of friction. It is proposed by Joule effect. For this reason, it is necessary to measure temperatures and to estimate the heat evacuation from the AMB system, because of friction.
Figure 6
Flowchart to summarize steps sequence because to achieve the position control
Source: Own elaboration.
As it was described above, in order to test the shaft movement transmission, it was activated only the hybrid electromagnet that was separated from the motor connector. Hence, in Figure 7a is depicted all control position, when it is measured by “sharp” position sensors, from which curves in color light blue, green, brown and red are the position that was captured by every position sensor.
Figure 7
The position control under the nanostructure effect
Source: Own elaboration.
It is shown that system is under control owing to the desired signal is 0 mm and the rotor must to be avoided to get imbalance rotation at 2 mm from its own axis. However, as it can be analyzed, there were found some overshoots that were achieved even the adaptive algorithm that was proposed. By other side, while it is changed every position sensor by sensors that were based in nanostructures, the control improves as it can be seen in Figure 7b. According to help visual explanation, Figure 7c shows a shaft setup for position sensors around it [6].
Finally, as it was detailed in paragraphs above: what is the consequence of heat that was produced by wires around hybrid magnets, which receive energy to produce the electromagnetic field (the main component to get equilibrium in AMB systems is depicted in Figure 8c)?
Figure 8
The energy evacuation as the nanostructure effect
Source: Own elaboration.
For this reason, Figure 8a shows power that was consumed, when there is not effect of sensors that were based in nanostructures, it is expected that no short response time makes the main control system could not get right response action. In otherwise, it can activate the hybrid actuators more time than it could be. So, does it mean that AMB cannot be in total efficient? To answer this question, it is necessary to get compromise between required range of work of the AMB, while it can be used to enhance movement transmission between rotating machines, the costing of the AMB in order to reduce the heat from lubrication of traditional mechanical bearings, but whether operating range of work can force the AMB according to increase energy that it needs to get operation, then of course, also this system can get losses by the heat.
This work shows a suggestion to solve that problematic by sensors that were based in nanostructures, due to their robustness, short response time can help in extreme conditions that traditional sensors cannot get. This is depicted in Figure 8b, due to reduced consumed power from AMB, because of nanostructure effect over mechatronic systems. According to help visual explanation, Figure 8c shows a shaft setup for position of magnets (hybrid between passive magnet with electro-magnet) around it, from which electrical current can increase its temperature without a controlled model that was based in nanostructures, as it is proposed in this research.
It was verified the good effect to use position sensors that were based in nanostructures, because of the wide range of work, robustness and fast response time as a consequence of nanosystems integrated in macro systems: the mechatronic systems. By other side, it was evaluated that AMB systems can enhance by better way their performance, when it is analyzed strategies to evacuate the heat that was not produced, because of friction among the rotor and bearings. In other words, the heat that was produced, because of the source of energy, which provides to the electromagnet actuators. Additionally, it is suggested to evaluate models that were proposed in this work with particular cases, such as M3 (from figures 2 and 3 that were described above) could be a turbine, so it is suggested to analyze its dynamic, energetic and thermodynamic behavior as a consequence of nanostructures integration, as it is proposed in this research.
The author of this work expresses deep warm grateful to Mrs. Aleksandra Ulianova de Calderón for all her motivation and support in the development of this article and for the provided respective photos. Also, it is expressed thanks to Mr. Gonzalo Solano M. and Mr. Bruno Miranda Q., because of their support in simulation and experimental tests. Finally, it is expressed thanks to FONCAI team from PUCP, researchers from Energy Laboratory PUCP, and Director of Mechatronic Master program PUCP, owing to the financial support.
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Luis Alberto Cruz Salazar1,2(), Ángela Viviana Peña2, David Alexander Urrego2, Juan Cardillo Albarrán3, Edgar Chacón Ramírez3
1 Institute of Automation and Information Systems, Technical University of Munich, Munich, Germany
2 Facultad de Ingeniería Mecánica, Electrónica y Biomédica, Universidad Antonio Nariño, Bogotá, Colombia
3 Escuela de Sistemas, Facultad de Ingeniería, Universidad de Los Andes, Mérida, Venezuela
Corresponding Author: luis.cruz@tum.de
Existing research of academia and industry reflects much higher expectations regarding the future of the factory. Several studies have demonstrated that modern automation focuses on the progress of the New Information and Communications Technology (NICT) and the emerging concepts of the Internet of Things (IoT). For example, Cyber-Physical Systems (CPS) for the industry are a crucial cause of converging technologies that can reply to the emerging globalized market demands. Indeed, applying CPS to manufacturing shows the advantages that gave birth to new terms as Cyber-Physical Production Systems (CPPS). Development platforms from the trend of automation, called the German Industry 4.0 (I4.0), are based on CPPS. Both I4.0 and CPPS are often associated with other local concepts in Latin America (LA), e.g., Agriculture 4.0 and the Industrial Internet of Things (IIoT), providing strong economic potential.
In LA, not all of the theory–referenced as the fourth industrial revolution–has been explored with rigor yet. Undoubtedly, promoting experiences and familiarizing researchers with these concepts should be the best way to address (substantially) new challenges in the LA industry. The main objective of this paper is to show a general analysis of the features of CPPS architecture that could be replicated in any production process. An adaptable CPPS would support the extension of industrialization, providing higher quality and more innovative products for the final consumer. The contextualization of the CPPS (the I4.0 foundation) offers a suitable opportunity to face the current challenges of the future factory. This work aims to address the futuristic landscape of industrial automation within the next years in LA by offering comparable international case studies.
Keywords: Cyber-Physical Systems, Cyber-Physical Production Systems, Industrial Internet of Things, Industry 4.0, Intelligent Manufacturing Systems.
Today in the research areas of academia and industry, there is a significant increase in results compared to expectations of future factory. Based on the progress of the New Information and Communications Technology (NICT) and emerging concepts of the Internet of Things (IoT), studies have demonstrated modern automation; a specific case of converging technologies is called Cyber-Physical Systems (CPS) and their applicability to respond to the emerging globalized market demands. Indeed, the use of CPS-manufacturing systems ongoing research seeking to demonstrate the advantages that these exposed [1]; namely, CPS conceived recent terms such as Cyber-Physical Production Systems (CPPS). Thereby, development platforms initiative called the –German– Industry 4.0 (I4.0) is focused on CPPS, often related to other concepts for Latin America (LA) as Agriculture 4.0 (extended by Brazil) and the Industrial IoT or IIoT (extended by the USA).
Fourth Industrial Revolution is a concept that is being developed, and which is generating a considerable impact on society and the economy [2], [3]. “Revolution” refers to a rapid and fundamental change, with significant transformations in a relatively short time. First Industrial Revolution was preceding the I4.0 (Figure 1a), occurred at the end of the eighteenth century, through the “acceleration” of mechanical production, which is based on water and steam; subsequently, the Second Industrial Revolution was generated in the early twentieth century, with the introduction of the conveyor belt and mass production. Stand out the names of the icons of this era, such as Henry Ford and Frederick Taylor; thus, the Third Industrial Revolution conceived by automation of production with electronics and computers. I4.0 has the relevant characteristics of Intelligent Manufacturing Systems (IMS) and CPS, as seen in Figure 1b, since become similar and high compatibility is projecting [1]. Emerging and prevailing in the future companies can widely benefit from the hallmarks of IMS approached from knowledge management. An example is the Plattform-i40 [4] where I4.0 and IIoT result from Germany, France, Italy, and other countries’ cooperation, with open-access consultation. For this platform, I4.0 is not the computer that is the core technology, but rather the Internet. I4.0 means “the intelligent networking of machines and processes for industry with the help of information and communication technology” [4, pp.]. Other LA countries support smart manufacturing development by various initiatives (see Section 4.1).
Figure 1
The fourth industrial revolution based on CPS. Part a) I4.0 chronology. Part b) IMS vs. CPS
Source: Adapted from [9]
Several authors have defined the CPS. Particularly in [5], it refers to a new generation of systems with integrated computational and physical capabilities that can interact with humans through many new modalities. The ability to interact with and expand the capabilities of the physical world over computation, communication, and control, it is a crucial enabler for future technology developments. From processes and manufacturing, the CPS is the natural evolution of what corresponds to the plant floor giving rise to the CPPS, but still suffers from the integration/interaction with the business processes as required by I4.0 and that are present in the holonic approach. The state of the art of this paper is an extension lead of the work in [6].
Theories about I4.0 describe the development of called smart factories. Hehenberger et al. [7], explains the I4.0 paradigm implementation must be via NICT, and its integration should apply IIoT in manufacturing processes, which initially were considered as a network to promoted smart networks [8]. Moreover, Cruz et al. [6], shows I4.0 theoretical basis from nine technologies, such as: IoT, Big Data, autonomous robots, the 3D simulation, and others (artificial intelligence, 3D printing, autonomous vehicles, nano-materials, blockchain, biotechnologies, etc.) [9], [10]. Now, I4.0 focuses on CPS (Figure 1b), among others [11], [12]. CPSs are the integration of devices (computational type), networks, and physical processes; also, they are in charge of monitor and control of any process over feedback loops, which may include connectivity to the cloud. CPS technologies are based on old and new control theory concepts [12], but principally on still-evolve embedded systems [13]. Other paradigms are deployed as physical component entities (hardware) and cyber (software) converging towards the CPS, listed in [11], [14], [15].
In [15], authors show a 5C architecture, which consists of five levels in a sequential workflow manner and illustrates how to construct a CPPS from the initial data acquisition through analytics to the final value creation. This work gives examples from the field of process, machine, or system-level monitoring. In a CPPS, approach the smart connection level (Level I) represents the physical space, Levels II-IV the “pure” cyberspace, while the configuration level (Level V) realizes the feedback from the cyberspace to the physical space. A description of the architecture levels is given in [15] and an application for intelligent machines given in [16]. This case study, on saw devices, shows an implementation of CPS in the manufacturing where all the necessary steps are covered, from acquiring data, processing the information, presenting to the users and supporting decision making. The case study shows the integration of the 5C architecture for processing and managing a fleet of CNC cutting machines, which are commonly used in manufacturing, but the current combination of the 5C CPS architecture is in its nascent stage. This architecture shows a way to implement CPS; we would have to explain about the process industry, for which we do not have until now reference.
This proposal takes a holistic research methodology; therefore, even if there are multiple approaches to perceive a system (using tools to observe, learn, and understand what is perceived quantitatively and qualitatively). The ideas, rather than opposites, are considered complementary. Thus, this proposal is understood that research is a continuous and organized process which aims to meet some event (feature, process, or situation) and get answers to a need [17], which are established from the holistic approach, and given their complexity. These are hierarchical levels order low to high relevance: Perceptual, Apprehensive, Comprehensive, and Integrative [17]. The aim is describing an IMS control for production processes from the paradigm of CPS meets smart properties such as autonomy, flexibility, etc. One example is in [18], Cardin defines four aspects to be considered at the introduction of the CPPS into the industry, among them “The Learning Factory”, which demands engineers and technicians trained to work in that environment. Additionally, the implementation of such systems needs a near Real-time IT-architecture in the cloud connected to a Real-time OT-architecture that controls the physical system.
Those aspects are not commonly available for Small and medium-sized enterprises (SMEs). Those are the critical constraints to the implementation of such systems. At the cloud, models of processes, products, and equipment are necessaries to describe, programming, supervise, and control the physical system. In [19], models for the description of the flows are shown as necessaries to perform the scheduling activities. Models of CPPS must describe the behavior of processes in several levels (plant-level execution, supervision) to have an integrated operation of the different CPPS working together to do production objectives. Those models are digital twins of physical processes and equipment. In practice, the new industry must ensure the convergence between IT / OT and the new concept of Engineering Technology that allows the application of digital twins, performing planning and scheduling activities in an efficient way and a better knowledge of the shop floor activities [20].
In factories in LA, the incorporation of plant floor technologies, associated with the third industrial wave, has been low compared to Europe, North America, or Southeast Asia. The execution of manufacturing tasks is mostly associated with operators, and the programming of operations is given mainly by expertise that by the Engineering systems using information in real time. The application of the broad concept of intelligent manufacturing is “… understood as a combination of software, hardware and/or mechanics, which should lead to optimization of manufacturing resulting in reduction of unnecessary labour and waste of resource …” given in [21]. This concept can apply to the SMEs in LA, and the introduction of CPPS can help to improve the performance of those industries. The introduction of models (digital twins) for equipment, processes, and resources will allow advancement in the operation scheduling and the monitoring and evaluation of the execution, also considering economical aspects.
The main requirement to incorporate the concept of CPPS in SMEs, it is that SMEs decide to integrate the three levels of choice associated with the production process: economic viability of a production mode, logistics viability for a production order and the viability of execution on the technological infrastructure and human resources. Common models achieve the integration to the three levels of decision (digital twins), and the decision process established under the concept of feedback systems for the CPPS. Associated with the decision of the company to address the change for the incorporation of the I4.0 concept at the enterprise, it is necessary the presence of experts that help the industry in the management of the ideas and the construction of digital twins and the implantation or adequacy of decision-making systems.