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Nowadays, there is a fresh push towards putting more attention on sustainability issues without affecting productivity as main target in industrial cyberphysical systems. In this direction, this article proposes a procedure and presents a data-driven insight method in order to predict the remaining useful life and to classify faults by a condition base-monitoring. Therefore, by using a framework that combines both outputs, a maintenance stop can be scheduled near to the failure, thus improving its sustainability, without affecting productivity. A fuzzy decision-making strategy supported on generated insights is developed in order to extend the useful life of electromechanical devices. A case study is presented in order to assess the proposed methodology using a dataset of bearing faults. Experimental results and its comparison with previous reported works corroborate a good trade-off solution offered by the proposed procedure considering productivity and sustainability for bearing faults detection.

Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.

Exponential technologies, if blended with advantageous conceptual setups, enable innovative developments for Internet-based production ecosystems. The focus of this study is on developing a support mechanism for homeostatic and evolutionary abilities in large-scale production environments. Starting from a reflection on the state-of-the-art, a suitable framework was developed for the conceptualization of Internet-based production ecosystems. Based on augmented context awareness and eco-systemic intelligence, homeostatic and adaptive abilities were designed, and used along with the operational controls to maintain the homeostasis of loads, workflows, and variability, and facilitate the long-term adaptation of the actors. The latter may comprise self-made adaptation of human and digital actors, as well as externally supported modification of functions operated by cyber-agents. The advantage of the proposed solutions is twofold: firstly, stakeholders can improve their performance and adaptability; and secondly, they can also benefit from non-selfish behaviors.

The enterprises to be competitive are constantly looking for continuous increase of productivity, quality and level of services. With the development of Industry 4.0 concept, manufacturers are more confident about new advantages of automation and systems integration. Lean management is well developed and empirically proven effective managerial approach. Combining Lean and Industry 4.0 practices seems to be necessary evolutionary step for further raise the level of operational excellence (exploitation of finance, workload, materials, machines/ devices). There is an increasing number of Industry 4.0 solutions used to reduce waste (as known from Lean Management). Therefore, the main objective of this article aims at presenting the results of a literature review on the concept of ‘Lean Industry 4.0ʹ. Dynamic methodology called “Systematic Literature Network Analysis (SLNA)” was used. It combines the Systematic Literature Review approach with the quantitative analysis of bibliographic networks to detect emerging topics and the dynamic evolution of the topics. The paper is a comprehensive systematization and rationalization of knowledge about the integration of LM and I4.0 concepts, identifies the most important research trends and defines directions for future research. The article contains a framework that presents the current state of knowledge in the area of Lean Industry 4.0.

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

A digital twin-based optimization procedure is presented for an ultraprecision motion system with a flexible shaft connecting the motor to the (elastic) load, which is subject to both backlash and friction. The main contributions of the study are the design of the digital twin and its implementation, assuming a two-mass drive system. The procedure includes the virtual representation of mechanical and electrical components, non-linearities (backlash and friction), and the corresponding control system. A procedure for digital twin-based optimization is also presented, in which the maximum absolute position error is minimized while maintaining accuracy with no significant increase in the control effort. The optimal settings for the controller parameters and for the backlash peak amplitude, the backlash peak time, and the hysteresis amplitude are then determined, in order to guarantee an appropriate dynamic response in the presence of backlash and friction. The surface quality of certain manufactured components, such as hip and knee implants, depends on the smoothness and the accuracy of the real trajectory produced in the cutting process that is strongly influenced by the maximum position error. The simulations and experimental studies are presented using a real platform and two references for trajectory control, and a comparison of four digital twin-based optimization methods. The simulation study and the real-time experiments demonstrate the suitability of the digital twin-based optimization procedure and lay the foundations for the implementation of the proposed method at an industrial level.