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The simulation software market is becoming more complex and universal. Computer simulations are thus more accessible and are becoming a modern tool that has a wide application in industry. Their potential and benefits can be used in small and large projects. A simulation model can take into account inventory, assembly, production and human resources, leading to decisions that can maintain or improve efficiency at the lowest possible cost. The data obtained through the simulation allow to test different combinations and scenarios in the virtual world. The benefits of manufacturing simulation include reducing investment risk, minimizing waste, improving efficiency, reducing energy consumption and even increasing worker health. The question arises as to which of the possible simulation packages is the most suitable for a given company, so that the investments made are the best possible. In the first part of the paper the theoretical basis of simulation in Industry 4.0 is presented, including the description of the possible simulation modelling tools. The second part of the paper offers comparative and descriptive analysis of six selected discrete-event simulation software packages – AnyLogic, Arena, FlexSim, SIMUL8, Tecnomatix Plant Simulation and WITNESS. The given simulation tools are compared based on their main characteristics, simulation features, application areas and popularity among the companies which use simulation software packages.

Sustainable development of technology in manufacturing motivates entrepreneurs to increasingly introduce technical innovations into their production process. In times of technical progress, the selection and support of qualified personnel are especially important. The process of introducing new technologies or innovations in an enterprise is closely related to the personnel involved in this process. Sustainable development of the enterprise is possible, subject to the sequence of processes, namely, ensuring the adaptation of personnel and collecting feedback. A mobile application can be innovative for a particular enterprise and ensure sustainable development and adaptation to Industry 4.0 technologies. Given the pressure of technology and the environment on small- and medium-sized manufacturing enterprises, it was proposed to conduct a practical study in the conditions of a typical enterprise of this category. To explore the attractiveness of mobile applications as a tool for enterprise sustainable development, an application with basic features that should help provide a framework for integrating Industry 4.0 technologies into the manufacturing process was proposed. In the course of the study, a cycle of innovations and a set of evaluation methods for some of them were proposed. With the help of the proposed assessment methods, as well as the practical use of the mobile application, it became clear that the proposed solution can create a positive effect. The success of this kind of innovation and the further sustainable development of the enterprise is possible if the gradual adaptation of employees will be ensured; it will enable further innovation.

The paper presents a new approach to recognize small parts from high-resolution 4K images grabbed by the embedded camera with trained convolutional network models. The main principle is a combination of standard machine vision techniques (OpenCV library) to identify blobs in the high-resolution image and parse to a suitable set of regions of interest, which are later recognized by trained convolutional neural network model (Tensorflow framework). The convolutional neural network model with single-shot detection was selected to identify the position of the object. 3D virtual models of parts with autogenerated orientations are used for simpler input data generation. Two embedded devices were selected for use in the experimental system. The first is NVidia Xavier dev board with 4K camera and integrated APU and the second is Raspberry PI 4 with external APU Intel Neural Compute Stick 2. The last part of the paper shows the implementation of the presented principle to the virtual device (HTC Vive Pro) for assisted assembly tasks to train and check workers during the teaching process.

The paper describes design of smart production system with full digitalization for requirements of Industry 4.0. The system consists of three level of technologies: production technologies, inspection subsystem and digitalization software system. Production technologies creates parts for assembly: small CNC, Rapid prototyping device and standardized parts storage with assisted assembly process by collaborative robot combined with mixed reality device. This system provides assisted assembly work cell. Inspection subsystems consist of RFID readers with passive tags, vision system with basic 3D inspection, multi-spectrum light for error detection and 3D profilometer precise measuring. Digitalization software is represented as Digital Twin model implemented to server, which uses OPC communication for data transfer from production system to Cloud Platform with additional data from IoT devices.

This article deals with the creation of a digital twin for an experimental assembly system based on a belt conveyor system and an automatized line for quality production check. The point of interest is a Bowden holder assembly from a 3D printer, which consists of a stepper motor, plastic components, and some fastener parts. The assembly was positioned in a fixture with ultra high frequency (UHF) tags and internet of things (IoT) devices for identification of status and position. The main task was parts identification and inspection, with the synchronization of all data to a digital twin model. The inspection system consisted of an industrial vision system for dimension, part presence, and errors check before and after assembly operation. A digital twin is realized as a 3D model, created in CAD design software (CDS) and imported to a Tecnomatix platform to simulate all processes. Data from the assembly system were collected by a programmable logic controller (PLC) system and were synchronized by an open platform communications (OPC) server to a digital twin model and a cloud platform (CP). Digital twins can visualize the real status of a manufacturing system as 3D simulation with real time actualization. Cloud platforms are used for data mining and knowledge representation in timeline graphs, with some alarms and automatized protocol generation. Virtual digital twins can be used for online optimization of an assembly process without the necessity to stop that is involved in a production line. © 2020 by the authors., European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-CurieEuropean Union (EU) [734713]; Ministry of Industry and Trade of the Czech Republic [FV20419]; Ministry of Education of the Slovak Republic [VEGA 1/0700/20, 055TUKE-4/2020]

This paper describes a new methodology for hybrid data collection from IoT (internet of things) devices by three separate technologies: GSM (Global System for Mobile Communications) (LTE-M/NB-IoT (Long-Term Evolution for Machines/Narrowband Internet of Things)), Sigfox, and LoRa/LoRaWAN. We discuss experiments involving event-based switching with actual monitoring of the different IoT communication technologies’ signal quality. The experiments demonstrated that combination of IoT technology can provide stable data flow from a production process to cloud platform systems. We tested the signal quality in a highly urbanized environment and a more sparsely inhabited environment by monitoring errors during travel between two towns. The next area of study is the problem of connection and transformation of digitalized data to a cloud platform with adequate synchronization. The tested open-source cloud platform was built on an InfluxDB database using the Grafana visualization framework. Data from industrial devices—for example, RFID (radiofrequency identification) and vision—can be transferred by a local network with an IoT gateway and an OPC (Open Platform Communications) server to commercial cloud platforms.

Forecasting energy consumption in buildings is crucial for achieving effective energy management as well as reducing environmental impacts. With the availability of large amounts of relevant data through smart metering, gas consumption forecasting is becoming an integral part of smart building design so that these requirements are met. In this study, we investigate week-ahead forecasting of daily gas consumption in three types of buildings characterized by different gas consumption profiles during a five-year period. As gas consumption in buildings is highly correlated with the average outdoor temperature, regression models with additional residual modeling are used for forecasting. However, conventional regression models with autoregressive moving averages (ARMA) errors (regARMA) perform poorly when the temperature forecasts are inaccurate. To address this, a new forecasting model termed genetic-algorithm-optimized regression wavelet neural network (GA-optimized regWANN) is proposed. It uses the wavelet decomposition of the residuals of temperature regression time-series, which are modeled by multiple nonlinear autoregressive (NAR) models based on sigmoid neural networks. The appropriate delays in the regression vectors of the NAR models are selected using a binary GA. Compared with regARMA and seasonal regARMA, the GA-optimized regWANN model achieved in the three buildings a reduction of 22.6%, 17.7%, and 57% in the mean absolute error (MAE) values in ex post forecasting with recorded temperatures, and a 52.5%, 27%, and 43.6% reduction in the MAE values in ex ante forecasting with week-ahead forecasted temperatures, even under conditions of relatively significant errors in the forecasted temperature.

One of the most monotonous activities in using convolutional neural networks for image recognition is preparation of the learning data. It involves creating samples (2D images of object) at different angles of view, different backgrounds/materials and partial overlay of the object. Input data must include a relatively large number of frames, typically about 100 and more images per object to make the learning precision useful. In the paper there is proposed a new approach to creating these data fully automated based on a virtual 3D model of the standardized parts. Automation principle is generating 2D images from the imported 3D construction model, including the following variable parameters: the angle of rotation, background and the material of the component. We used for verification pretrained DNN model Faster RCNN Inception v2 with single shot detection (SSD). The learned convolutional network was next tested by real samples to verify a new approach of learning by virtual models and recognition of real objects (parts).

The paper describes the experiments with the use of deep neural networks (CNN) for robust identification of assembly parts (screws, nuts) and assembly features (holes), to speed up any assembly process with augmented reality application. The simple image processing tasks with static camera and recognized parts can be handled by standard image processing algorithms (threshold, Hough line/circle detection and contour detection), but the augmented reality devices require dynamic recognition of features detected in various distances and angles. The problem can be solved by deep learning CNN which is robust to orientation, scale and in cases when element is not fully visible. We tested two pretrained CNN models Mobilenet V1 and SSD Fast RCNN Inception V2 SSD extension have been tested to detect exact position. The results obtained were very promising in comparison to standard image processing techniques.

The Article deals with usability and advantages of embedded vision systems for surface error detection and usability of advanced algorithms, technics and methods from machine learning and artificial intelligence for error classification in machine vision systems. We provide experiments with following classification algorithms: Support Vector Machines (SVM), Random Threes, Gradient Boosted Threes, K-Nearest Neighbor and Normal Bayes Classifier. Next comparison experiment was conducted with multilayer perceptron (MLP), because currently it is very popular for classification in the field of artificial intelligence. These classification approaches are compared by precision, reliability, speed of teaching and algorithm implementation difficulty.

Varela immune controller is a kind of nonlinear controller, which is said to have good anti-delay capabilities. We compare the performance of simulated annealing optimized improved Varela immune controller and optimized PID controller for controlling a process with very long time delay (approximation of biomass-fired boiler temperature control). The results confirm that Varela immune controller is indeed capable of stabilizing the process while being very robust even to extreme changes in process parameters (time constant and time delay). In addition to that, it is also found out that properly (optimally) tuned PID controller is capable of achieving similar performance. The problem of controller tuning is relevant for both controllers but there are no tuning rules for immune controllers, which might favor the use of conventional PID controller. On the other hand, Varela controller has greater flexibility due to its more complex structure, which might help to adapt it to some special kinds of processes.

The article deals with the diagnostics of components surface after painting by camera system in real-time. This solution is especially suitable for implementation to automatized production line above the conveyor belt. The faults on the part surface can be detected as scratches, imperfect surface coverage and dirt stuck to the surface. The scratch detection is based on edge detectors, imperfect coverage are checked by histogram comparison and all other errors are detected by counter detectors. The developed software uses open source library OpenCV and is written in C++ language. The software solution is platform independent. Final algorithm is implemented to embedded device based on SoC.

Pneumatic artificial muscles belong to a category of nonconventional pneumatic actuators that are distinctive for their high power/weight ratio, simple construction and low price and maintenance costs. As such, pneumatic artificial muscles represent an alternative type of pneumatic actuator that could replace the traditional ones in certain applications. Due to their specific construction, PAM-based systems have nonlinear characteristics which make it more difficult to design a control system with good performance. In the paper, a gray-box model (basically analytical but with certain experimental parts) of the one degree-of-freedom PAM-based actuator is derived. This model interconnects the description of pneumatic and mechanical part of the system through a set of several nonlinear differential equations and its main purpose is the design of intelligent control system in simulation environment. The model is validated in both open-loop and closed-loop mode using the measurements on real plant and the results confirm that model performance is in good agreement with the performance of real actuator.

This paper deals with usability of MEMS sensors for diagnostics of mechatronics system state wirelessly. We can acquire basic kinematics and dynamics mechanism parameters (spatial position, speed, acceleration, vibration, angular rate, orientation, etc.) and some environment condition (local/remote temperature, humidity, pressure, electromagnetic noise) by MEMS sensors. Acquired data are sent to remote application in desktop computer. This system can replace expensive and separate diagnostic tools by small integrated solution with one wireless communication interface (with limitation of MEMS sensors precision). This solution can be battery powered with long operation time, because there is used new wireless technology based on Bluetooth 4 protocol (Low Energy/Smart Bluetooth). Some of integrated MEMS sensors measures same variable on different measuring principle. For example angle can be acquired from three different sensors: magnetometer, accelerometer or gyroscope. Combination of these sensor data can significantly improve value accuracy. The designed diagnostic tool can serve as an inertia measuring unit IMU or Wireless IMU (WIMU).

This article describes the vision system, which is designed for diagnostics of defects in casted products. In the first part an overview about image processing, edge and pattern recognition algorithms and current status in available free and commercial vision libraries is found. For the described task we selected open source Aforge .NET library. The next part describes common defects in casted products. Modular education system MPS 500 from Festo with conveyor and palette with plastic parts is used for simulation of production system. This system contains an industrial robot which can be used for sorting defective parts. The selected vision library is used for two level diagnostics of algorithm implementation. The first level algorithm detects position of part, its dimensions and edge disturbances. The second algorithm detects any defects inside of a part. The basic algorithm is presented only for circular shape with red color texture, but can be easily extended to other basic shapes by shape detector.

In paper the rehabilitation device for upper arm based on artificial muscles is introduced. Presented automated rehabilitation device has three degrees of freedom: 2 DOF in arm and 1 DOF in elbow that provides almost all basic rehabilitation exercises. Artificial pneumatics muscles will be tested in connection with spring and antagonistic connection. This system provides lifting and falling of arm construction based on patient force. Possibility to generate help force during rehabilitation or opposite load is there. Artificial muscles are controlled through pneumatic valve terminal from micro computer based on MCU. Higher level control system provides artificial intelligence implementation based on neural network for prediction and change of load according sensor values history (incremental sensor, force sensor). For prototype testing there is described usability of industrial robot to test load and precision of trajectories during rehabilitation. This device will help to reduce therapeutics work with patient automate and improve rehabilitation process.