Thermography is widely used to detect delamination defects in carbon fiber reinforced plastics (CFRP). This paper proposes a model to detect defects automatically by extracting the thermal signal characteristics of CFRP materials. An optically excited thermography system is constructed for pulsed and lock-in thermography experiments to compare thermal signal data sets in different excitation modes. A multi-task joint loss function is defined to train the model for defect detection and depth prediction. The effects of different attention modules (AM) are analyzed to improve the model performance. By comparing the effects of traditional thermography processing methods and methods based on Convolutional Neural Network (CNN), it is found that the proposed model can detect defects with minimum aspect ratio (ratio of short side to depth) of 2.5, and the relative error percentage in depth prediction is below 10%.

Control loop performance assessment (CLPA) techniques are crucial for optimizing any plant or machine. They can bring huge energy and material savings and increase product quality. In this paper, the employment of running discrete Fourier transform (RDFT) in CLPA field is discussed. The first part of the paper documents the development of new RDFT function block which is suitable for CLPA. The paper focuses on implementation aspects whose aim is to minimize the number of arithmetic operations and to avoid numerical errors which are cumulated in many algorithms when running over longer time period. Then three RDFT applications are introduced. They are mostly dedicated to CLPA area: The changes in RDFT output help to detect increasing valve stiction or reveal a cause of oscillations in the loop. RDFT can be also used for continuous monitoring of process changes at particular frequencies. The most advanced problem presented is the estimation of special performance indices. More specifically, key samples of sensitivity function are gained and compared to the reference ones. Inspired by the model free design techniques, only a minimum a priori information about the process is assumed. The authors believe that the presented ideas will be suitable for both academic and industrial sphere.

Modern machines strive to run at limit performance and dependability while their operational area and size are getting restricted. To achieve those objectives, often swift integration of custom-made subsystems is required, either actuators, sensors, electronic, or SW modules. Such a diverse suite of elements needs specific approaches and tools for fast optimization and adjustment following model-based system engineering (MBSE) and digital twinning principles. The large-scale I-MECH project was an industry-driven initiative striving to give a scientific response to those demands. The intermediate results were summarized in the authors’ previous work. The purpose of this paper is to report on final project results, namely specific performance achievements and figures based on measurable KPIs. After a brief description of key technologies, special focus is given to industrial printing technology based on a generic substrate carrier. However, it is shown that similar and consistent methodology can be applicable in many other industrial domains, such as semiconductors, healthcare robotics, machining, packaging, etc. Thus, the main merit of this survey is a holistic approach to motion control design.

The paper presents novel tools for model-based control system design based on FMI/FMU standard (Functional Mock-up Interface / Unit). It is focused on application of FMI standard for easy integration of control system development cycle starting with Model-in-the-Loop (MIL) simulation and finishing with Hardware-in-the-Loop (HIL) simulation. It is shown, how the Functional Mock-up Units (FMU) containing dynamic differential-algebraic equations of various parts of the device (mechanical, electrical, hydraulic, thermal, etc.) can be easily deployed to unified simulation environment where the control system is designed, consequently. The procedure allows to combine inputs from various Modelica-based tools at the process model side, utilizing power of Matlab/Simulink for design, analysis and optimization of control system and perform final test via HIL scenario where both the model and control system are simulated in real-time on separated HW units. The pros and cons of both FMI concepts, i.e. Co-Simulation and Model Exchange are discussed in detail. The whole procedure is demonstrated on a steam turbine example combining component-based and equation based modeling. Both the turbine model and the full control loop are validated in all phases of control system development. It is shown, that monolithic simulation block with proprietary solver reduces computational burden compared to automatic FMU concept.