Mechatronics applications of measurements for smart manufacturing in an industry 4.0 scenario

The usefulness of the availability of some guidelines to manage and control the informative flow deriving from measurements to be used in an Industry 4.0 scenario has been already discussed in [1]. In this work, it has been highlighted that having reliable and efficacious guidelines for the management of the informative flow deriving from measurements implies both formal and substantial actions, with reference to the canonical techniques for data management and the issues connected to a mechatronic system [1]. Formal actions focus on the fulfillment of particular aspects which are considered as prerequisites for the application of the data-processing procedure (e.g., avoiding formal errors in data translation and integration). Substantial actions are linked to the physical interpretation of the monitored phenomena and stress the physical meaning of data and of processing operations.

[1]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[2]  Emanuela Natale,et al.  Dynamic calibration uncertainty of three-axis low frequency accelerometers , 2015 .

[3]  Nuno Pereira,et al.  Cyber-physical systems clouds: A survey , 2016, Comput. Networks.

[4]  Athanasios V. Vasilakos,et al.  The role of big data analytics in Internet of Things , 2017, Comput. Networks.

[5]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[6]  Soundar R. T. Kumara,et al.  Cyber-physical systems in manufacturing , 2016 .

[7]  Olivia Penas,et al.  Multi-scale approach from mechatronic to Cyber-Physical Systems for the design of manufacturing systems , 2017, Comput. Ind..

[8]  Diego Galar Pascual,et al.  Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and DIagnosis , 2015 .

[9]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[10]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[11]  Pankaj Sharma,et al.  A Big Data Analytical Architecture for the Asset Management , 2017 .

[12]  Christophe Nicolle,et al.  Understandable Big Data: A survey , 2015, Comput. Sci. Rev..

[13]  Hala A. Mansour,et al.  Utilizing the Internet of Things (IoT) Technologies in the Implementation of Industry 4.0 , 2017, AISI.

[14]  Emanuela Natale,et al.  Measurements for Smart Manufacturing in an Industry 4.0 Scenario A Case-Study on A Mechatronic System , 2018, 2018 Workshop on Metrology for Industry 4.0 and IoT.

[15]  Giulio D'Emilia,et al.  Uncertainty of slip measurements in a cutting system of converting machinery for diapers production , 2015 .

[16]  Antonella Gaspari.  VALIDATION OF SIGNAL PROCESSING TECHNIQUES FOR VIBRATION MEASUREMENTS , 2017 .

[17]  Richard Boateng,et al.  Cloud computing research: A review of research themes, frameworks, methods and future research directions , 2018, Int. J. Inf. Manag..

[18]  Dan Cornford,et al.  Managing uncertainty in integrated environmental modelling: The UncertWeb framework , 2013, Environ. Model. Softw..

[19]  Jian Yu Chen,et al.  Application of Programmable Logic Controller to Build-up an Intelligent Industry 4.0 Platform , 2017 .

[20]  Emanuela Natale,et al.  Calibration test bench for three-axis accelerometers An accurate and low-cost proposal , 2018, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).