**6. Conclusions**

This paper aimed to target one of the most challenging subjects in smart manufacturing, which is the quality control at the shop floor level considering emerging technologies. There are many conceptual models and general recommendations when discussing a new paradigm of quality associated with I4.0, but there are relatively few works in action. The hybrid model proposed in this work supports the troubleshooting of the plaster production process, which is a complex manufacturing system. To have both descriptive and prescriptive approaches, NN and ES were integrated where NN deals with the determination of fault areas, and ES provides the recommendation of corrective actions.

The main achievements and contributions of this work are as follows:


This paper shows the capability of I4.0 to change the quality paradigm in factories of the future. The key element is the level of intelligence of the system, which leads to smart manufacturing. There is no doubt that emerging technologies will shift quality processes to a different level, while monitoring, fault detection, cause, root analysis, and even corrective actions and strategies would be autonomous.

For further research, there are many potential areas of working, as outlined below.


The models proposed in this paper were independent of software; thus, free software and modern scripting languages such as Python, Ruby, etc. could be utilized for the same purpose.

This paper was a real case of Quality4.0 in action to show the capabilities and applicability of emerging technologies and intelligent algorithms to shift control quality to the new stage, and it represents the initial step of a long journey.

**Author Contributions:** Conceptualization, J.R.; methodology, J.R. and J.J.; writing—drafting Section 2, J.R. and J.J.; writing—drafting Section 3, J.R. and J.J.; writing—drafting Section 4, J.R.; writing—drafting Section 5, J.R.; writing—drafting Section 6, J.J.; writing—reviewing and editing, J.R. and J.J. All authors read and agreed to the published version of the manuscript.

**Funding:** This research was funded in part by the Portuguese "Fundação para a Ciência e a Tecnologia" (FCT) in the context of the Center of Technology and Systems CTS/UNINOVA/FCT/NOVA, reference UIDB/00066/2020.

**Acknowledgments:** This work was supported by the Portuguese Foundation for Science and Technology (FCT) and the Center of Technology and Systems (CTS).

**Conflicts of Interest:** The authors declare no conflict of interest.
