# Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance
## 1 Introduction
To become antifragile, we must prepare ourselves to face uncertainties; that is to say, something that cannot be predicted. It is known that through preventive maintenance (PvM), periodic inspections are planned with the aim of verifying and correcting deviations from the normal state and anticipating the detection of possible failures. The moments at which these interventions are performed are established using statistics based on historical data records and service intervals recommended by the manufacturers based on their design criteria. However, as the complexity of the manufacturing processes increases and, implicitly, the degree of uncertainty regarding context changes increases, preventive maintenance solutions using predefined scenarios have been gradually replaced with predictive maintenance (PdM) solutions, based on the real-time examination of assets' status, to detect behavioral anomalies that can lead to failures, but which do not have an obvious cause. There is an example that we find convincing for associating PdM with antifragility. Formula 1 has achieved spectacular performances that are based on the setting of the motors according to the most important parameters of the environment. In each tour of the circuit, the minimum travel time is sought. However, as the evolution progresses, two variable elements emerge: weight reduction due to fuel consumption and tire wear. PvM would suggest the tires must be changed when the wear has reached magnitude X. PdM considers environmental conditions and calculates how to reconfigure the parameters as the tires wear out and calls for change only when the risk of explosion may occur. In other words, a stressor (wear) is used to maintain the optimum performance. The system is antifragile. In their work, the authors of [4] present Europe's need to leap to Industry 6.0, and they define Industry 6.0 as \"Ubiquitous, customer-driven, virtualized, antifragile manufacturing\". The authors present a vision of how industry should evolve to meet various demands (environment friendly manufacturing, profitability, reduced emissions, resilience against various shocks, sustainability, and many others). The paper presents mostly a philosophical approach, as the authors only show WHAT to expect and not HOW to do it. However, this study is a great starting point for tangible proposals. This paper aims to prove that ensuring the property of antifragility is the safest way to exploit complex manufacturing systems under uncertain conditions and that this philosophy can be applied generally in process management, using an association of emerging technologies borrowed from the techniques of artificial intelligence (machine learning, neural networks) and process control through computer networks (cloud computing, edge computing, big data) by performing PdM. We named the concept resulting from this association antifragile maintenance (AfM), which is a simplification of [PERSON]'s mentioned formula and written as AfM = PdM + Learning. (In this way, we thank [PERSON] who suggested this interpretation in a private discussion; details about \"Learning\" can be found in Sections 4.3 and 5.) The rest of this paper is organized as follows: Section 2 debates why self-improving systems can be seen as antifragile systems; Section 3 presents predictive maintenance in self-improving systems; Section 4 covers the challenges of designing self-improving systems; Section 5 is dedicated to uncertainties inside antifragile manufacturing systems; Section 6 presents a case study in which artificial errors are introduced to simulate uncertainties; and Section 7 is dedicated to our conclusions.