Next Article in Journal
Estimating the Characteristic Curve of a Directional Control Valve in a Combined Multibody and Hydraulic System Using an Augmented Discrete Extended Kalman Filter
Next Article in Special Issue
Human–Machine Integration in Processes within Industry 4.0 Management
Previous Article in Journal
Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
Previous Article in Special Issue
Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One

Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany
Complex Systems Group, Universidad Politécnica de Madrid, Av. Puerta de Hierro 2, 28040 Madrid, Spain
Department of Organizational Engineering, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain
Independent Researcher, Niesmannshof 54, 46535 Dinslaken, Germany
Author to whom correspondence should be addressed.
Academic Editor: Leopoldo Angrisani
Sensors 2021, 21(15), 5031;
Received: 15 June 2021 / Revised: 20 July 2021 / Accepted: 21 July 2021 / Published: 24 July 2021
With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes. View Full-Text
Keywords: quantum simulation; JIDOKA; industry 4.0; shopfloor management quantum simulation; JIDOKA; industry 4.0; shopfloor management
Show Figures

Figure 1

MDPI and ACS Style

Villalba-Diez, J.; Gutierrez, M.; Grijalvo Martín, M.; Sterkenburgh, T.; Losada, J.C.; Benito, R.M. Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization. Sensors 2021, 21, 5031.

AMA Style

Villalba-Diez J, Gutierrez M, Grijalvo Martín M, Sterkenburgh T, Losada JC, Benito RM. Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization. Sensors. 2021; 21(15):5031.

Chicago/Turabian Style

Villalba-Diez, Javier, Miguel Gutierrez, Mercedes Grijalvo Martín, Tomas Sterkenburgh, Juan C. Losada, and Rosa M. Benito 2021. "Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization" Sensors 21, no. 15: 5031.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop