You are invited to read these papers on automation in industry that were published in Automation (ISSN: 2673-4052). The list of papers is as follows:
1. “Towards the Development of a Digital Twin for a Sustainable Mass Customization 4.0 Environment: A Literature Review of Relevant Concepts”
by César Martínez-Olvera
Automation 2022, 3(1), 197–222; https://doi.org/10.3390/automation3010010
Available online: https://www.mdpi.com/2673-4052/3/1/10
2. “Implementation of Digital Twin and Real Production System to Address Actual and Future Challenges in Assembly Technology”
by Lukas Christ, Elías Milloch, Marius Boshoff, Alfred Hypki and Bernd Kuhlenkötter
Automation 2023, 4(4), 345–358; https://doi.org/10.3390/automation4040020
Available online: https://www.mdpi.com/2673-4052/4/4/20
3. “Symbiotic Evolution of Digital Twin Systems and Dataspaces”
by Thomas Usländer, Michael Baumann, Stefan Boschert, Roland Rosen, Olaf Sauer, Ljiljana Stojanovic and Jan Christoph Wehrstedt
Automation 2022, 3(3), 378–399; https://doi.org/10.3390/automation3030020
Available online: https://www.mdpi.com/2673-4052/3/3/20
4. “Digital Twin of a Flexible Manufacturing System for Solutions Preparation”
by Tiago Coito, Paulo Faria, Miguel S. E. Martins, Bernardo Firme, Susana M. Vieira, João Figueiredo and João M. C. Sousa
Automation 2022, 3(1), 153–175; https://doi.org/10.3390/automation3010008
Available online: https://www.mdpi.com/2673-4052/3/1/8
5. “A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing”
by M. Azizur Rahman, Md Shihab Shakur, Md. Sharjil Ahamed, Shazid Hasan, Asif Adnan Rashid, Md Ariful Islam, Md. Sabit Shahriar Haque and Afzaal Ahmed
Automation 2022, 3(3), 400–425; https://doi.org/10.3390/automation3030021
Available online: https://www.mdpi.com/2673-4052/3/3/21
6. ”Scientometric Analysis for Cross-Laminated Timber in the Context of Construction 4.0”
by Emanuel Martinez Villanueva, Jennifer Alejandra Cardenas Castañeda and Rafiq Ahmad
Automation 2022, 3(3), 439–470; https://doi.org/10.3390/automation3030023
Available online: https://www.mdpi.com/2673-4052/3/3/23
7. “Automation of a PCB Reflow Oven for Industry 4.0”
by Isaí Vilches, Félix Juárez Durán, Alfonso Gómez-Espinosa, Mary Carmen García Carrillo and Jesús Arturo Escobedo Cabello
Automation 2023, 4(1), 78–93; https://doi.org/10.3390/automation4010006
Available online: https://www.mdpi.com/2673-4052/4/1/6
8. “Inspection Application in an Industrial Environment with Collaborative Robots”
by Paulo Magalhaes and Nuno Ferreira
Automation 2022, 3(2), 258–268; https://doi.org/10.3390/automation3020013
Available online: https://www.mdpi.com/2673-4052/3/2/13
9. “Model-Based Firmware Generation for Acquisition Systems Using Heterogeneous Hardware”
by Rens Baeyens, Joachim Denil, Jan Steckel and Walter Daems
Automation 2022, 3(3), 471–485; https://doi.org/10.3390/automation3030024
Available online: https://www.mdpi.com/2673-4052/3/3/24
10. “A Flashback on Control Logic Injection Attacks against Programmable Logic Controllers”
by Wael Alsabbagh and Peter Langendörfer
Automation2022, 3(4), 596–621; https://doi.org/10.3390/automation3040030
Available online: https://www.mdpi.com/2673-4052/3/4/30
11. “Neural Network-Based Classifier for Collision Classification and Identification for a 3-DOF Industrial Robot”
by Khaled H. Mahmoud, G. T. Abdel-Jaber and Abdel-Nasser Sharkawy
Automation 2024, 5(1), 13–34; https://doi.org/10.3390/automation5010002
Available online: https://www.mdpi.com/2673-4052/5/1/2
12. “Virtual Commissioning of Linked Cells Using Digital Models in an Industrial Metaverse”
by Marco Ullrich ,Rashik Thalappully, Frieder Heieck and Bernd Lüdemann-Ravit
Automation 2024, 5(1), 1–12; https://doi.org/10.3390/automation5010001
Available online: https://www.mdpi.com/2673-4052/5/1/1
13. “Detection of Novel Objects without Fine-Tuning in Assembly Scenarios by Class-Agnostic Object Detection and Object Re-Identification”
by Markus Eisenbach, Henning Franke, Erik Franze, Mona Köhler, Dustin Aganian, Daniel Seichter and Horst-Michael Gross
Automation 2024, 5(3), 373–406; https://doi.org/10.3390/automation5030023
Available online: https://www.mdpi.com/2673-4052/5/3/23
14. “Reinforcement Learning for Collaborative Robots Pick-and-Place Applications: A Case Study”
by Natanael Magno Gomes, Felipe Nascimento Martins, José Lima and Heinrich Wörtche
Automation 2022, 3(1), 223–241; https://doi.org/10.3390/automation3010011
Available online: https://www.mdpi.com/2673-4052/3/1/11
15. “Building an Educational Automated Mechatronics-Based Sorting System”
by Benjamin Jackvony and Musa Jouaneh
Automation 2024, 5(3), 297–309; https://doi.org/10.3390/automation5030018
Available online: https://www.mdpi.com/2673-4052/5/3/18
