The Cyber-Physical and Intelligent Robotics Laboratory has been digitally recreated, and it includes all the key elements that allow 6-axis industrial robots to perform PTP, LIN, and CIRC motions. Furthermore, the user can create a program with these motion types. The human–machine interface is also integrated into our system. It can also assist SMEs in developing their in-house training. After all, training on an industrial robot unit does not entail installation costs within the facility. Nor are there any maintenance and servicing costs. Since the lab is digital, additional robot units can be added or removed. Thus, areas for training or production can be pre-configured within each facility. Because of the customizability and virtual education format, there is no room capacity problem, and trainees can participate in the exercises in parallel. Exercises were also conducted to evaluate the program’s impact on teaching, and the results showed that using machine units can improve teaching. Even today’s digital labs cannot physically convey the sense of space or the relative weights of different elements in virtual space. Even with these features, individuals can operate a machine more effectively than relying solely on traditional, non-interactive demonstration materials.

Impairments of fingers, wrist, and hand forearm result in significant hand movement deficiencies and daily task performance. Most of the existing rehabilitation assistive robots mainly focus on either the wrist training or fingers, and they are limiting the natural motion; many mechanical parts associated with the patient’s arms, heavy and expensive. This paper presented the design and development of a new, cost-efficient Finger and wrist rehabilitation mechatronics system (FWRMS) suitable for either hand right or left. The proposed machine aimed to present a solution to guide individuals with severe difficulties in their everyday routines for people suffering from a stroke or other motor diseases by actuating seven joints motions and providing them repeatable Continuous Passive Motion (CPM). FWRMS approach uses a combination of; grounded-exoskeleton structure to provide the desired displacement to the hand’s four fingers flexion/extension (F/E) driven by an indirect feed drive mechanism by adopting a leading screw and nut transmission; and an end-effector structure to provide angular velocity to the wrist flexion/ extension (F/E), wrist radial/ulnar deviation (R/U), and forearm supination/pronation (S/P) driven by a rotational motion mechanism. We employed a single dual-sided actuator to power both mechanisms. Additionally, this article presents the implementation of a portable embedded controller. Moreover, this paper addressed preliminary experimental testing and evaluation process. The conducted test results of the FWRMS robot achieved the required design characteristics and executed the motion needed for the continuous passive motion rehabilitation and provide stable trajectories guidance by following the natural range of motion (ROM) and a functional workspace of the targeted joints comfortably for all trainable movements by FWRMS.

In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.

This paper presents a simulation-based study for characterizing and analyzing the performance of a commercially available thermoelectric cooler (TEC) as a generator for harvesting heat energy along with a commercial-off-the-shelf (COTS) power management integrated circuit (PMIC); LTC3108. In this model, the transformation of heat was considered in terms of an electrical circuit simulation perspective, where temperature experienced by TEC on both cold and hot sides was incorporated with voltage supply as Vth and Vtc in the circuit. When it comes to modeling a system in a simulation program with an integrated circuit emphasis (SPICE) like environment, the selection of thermoelectric generator (TEG) and extraction methods are not straightforward as well as the lack of information from manufacturer’s datasheets can limit the grip over the analysis parameters of the module. Therefore, it is mandatory to create a prototype before implementing it over a physical system for energy harvesting circuit (EHC) optimization. The major goal was to establish the basis for devising the thermal energy scavenging based Internet of Things (IoT) system with two configurations of voltage settings for the same TEG model. This study measured the data in terms of current, voltage, series of resistive loads and various temperature gradients for generating the required power. These generated power levels from EHC prototype were able to sustain the available IoT component’s power requirement, hence it could be considered for the implementation of IoT based applications.

MATEC Web of Conferences, open access proceedings in Materials science, Engineering and Chemistry

Maintenance activities are integral within an industrial setting. The efficiency of these activities is associated with the total productivity of an industrial process/machine. A highly efficient maintenance policy/strategy usually results in relatively high levels of plant productivity. AR and VR may be incorporated into a maintenance strategy. AR and VR technology would enhance maintenance activities, and facilitate somewhat complex tasks. A maintenance strategy includes a range of activities and may be categorized into administrative, technical and management processes. AR technology contains digital data, as well as other technical details, and is able to provide information about industrial machinery-equipment, without the need for equipment disassembling. In this regard, we employed AR technology in developing a unique navigation system to replace/reduce the installation costs of traditional AGV navigation systems. The proposed AR system consists of a camera, which observes the QR-Code/Markers, and a processing unit. Augmented Reality enables visualisation of any data and information, as well as control of a running process. It means it is possible to read various data within any equipment, during its operation and in real-time. This facilitates analysis of “black box” systems.