2020 ◽  
Vol 10 (7) ◽  
pp. 2574 ◽  
Author(s):  
Donatas Mažeika ◽  
Rimantas Butleris

This paper presents how Model-Based System Engineering (MBSE) could be leveraged in order to mitigate security risks at an early stage of system development. Primarily, MBSE was used to manage complex engineering projects in terms of system requirements, design, analysis, verification, and validation activities, leaving security aspects aside. However, previous research showed that security requirements and risks could be tackled in the MBSE model, and powerful MBSE tools such as simulation, change impact analysis, automated document generation, validation, and verification could be successfully reused in the multidisciplinary field. This article analyzes various security-related techniques and then clarifies how these techniques can be represented in the Systems Modeling Language (SysML) model and then further exploited with MBSE tools. The paper introduces the MBSEsec method, which gives guidelines for the security analysis process, the SysML/UML-based security profile, and recommendations on what security technique is needed at each security process phase. The MBSEsec method was verified by creating an application case study that reflects real-world problems and running an experiment where systems and security engineers evaluated the feasibility of our approach.


Author(s):  
Phanikrishna Thota ◽  
Simon Hancock ◽  
Mario Noriega-Fogliani ◽  
Rodrigo Jimenez

2021 ◽  
pp. 15-33
Author(s):  
V. B. Nguyen ◽  
T. Ba ◽  
A. Teo ◽  
K. Ahluwalia ◽  
A. Aramcharoen ◽  
...  

Current control systems for the shot peening operation merely rely on old technologies, which often require repetitive processes to obtain pre-validated Almen systems to guide industrial productions. These designs for the manufacturing paradigm are not efficient for complicated workflows in modern manufacturing operation. Thus, in this study, we propose a practical model-based control system to address the issues; especially for a smarter and automated shot peening machine. In particular, the closed-loop control system development utilizes a model-based proportional-integral-derivative (PID) control technology and extreme gradient boosting (XGBOOST) machine learning algorithm. The control system includes an internal process model, a proxy model, a model-based PID controller, and pressure sensors with a low-pass filter for feedback control. The developed control system is integrated into a physical shot peening machine for on-site control validation and demonstration. In both in-silico and on-site control demonstrations, the obtained control performance is stable, robust, and reliable for different operational conditions. The measurement intensities are very close to targeted setting intensities. All the differences are smaller than the industrial threshold of (±0.01 mmA). It implies that the control system can use in industrial peening operations without the need for Almen system development for operational guidance. In other words, the control system can significantly reduce the total cost of the actual production by eliminating the cost, time, and labor of the iterative trials to build the Almen system.


Author(s):  
Sebastian Feuerstack ◽  
Marco Blumendorf ◽  
Maximilian Kern ◽  
Michael Kruppa ◽  
Michael Quade ◽  
...  

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