FASTory assembly line power consumption data.

Machine learning (ML) techniques are widely adopted in manufacturing systems for discovering valuable patterns in shopfloor data. In this regard, machine learning models learn patterns in data to optimize process parameters, forecast equipment deterioration, and plan maintenance strategies among other uses. Thus, this article presents the dataset collected from an assembly line known as the FASTory assembly line. This data contains more than 4,000 data samples of conveyor belt motor driver's power consumption. The FASTory assembly line is equipped with web-based industrial controllers and smart 3-phase energy monitoring modules as an expansion to these controllers. For data collection, an application was developed in a timely manner. The application receives a new data sample as JavaScript Object Notation (JSON) every second. Afterwards, the application extracts the energy data for the relevant phase and persists it in a MySQL database for the purpose of processing at a later stage. This data is collected for two separate cases: static case and dynamic case. In the static case, the power consumption data is collected under different loads and belt tension values. This data is used by a prognostic model (Artificial Neural Network (ANN)) to learn the conveyor belt motor driver's power consumption pattern under different belt tension values and load conditions. The data collected during the dynamic case is used to investigate how the belt tension affects the movement of the pallet between conveyor zones. The knowledge obtained from the power consumption data of the conveyor belt motor driver is used to forecast the gradual behavioural deterioration of the conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems.

Methanol electro oxidation on Ni-Pt-CrO/CNFs composite: morphology, structural, and electrochemical characterization.

In this work, prepared nanoparticle samples of Ni1-xCrx with a fixed ratio of platinum (3%) were synthesized and loaded onto carbon nanofibers, which were produced by an electrospinning technique and carbonized at 900 °C for 7 h in an argon atmosphere. A variety of analysis techniques were applied to examine the stoichiometry, structure, surface morphology, and electrochemical activity. The carbonization process produces carbon nanofibers decorated with metal nanoparticles. Typical fibre diameters are 250-520 nm. The fibre morphologies of the treated samples don't exhibit any overt alterations. A study of the samples' methanol electrocatalytic capabilities was conducted. Cyclic voltammetry, chronoamperometry, and electrochemical impedance measurements were used to investigate catalytic performance and electrode stability as a function of electrolyte concentration, scan rate, and reaction time. The electrooxidation reaction's activation energy is increased, and the electrode's stability is increased, when Cr is added to Ni. In sample C3, the maximum current density (JPE) was 170.3 mA/cm2 at 0.8 V with an onset potential of 0.352 V. Utilizing our electrocatalysts, the electrooxidation of methanol involves a mix of kinetic and diffusion control limiting reactions. This study has shown how to fabricate a powerful Ni-Pt-Cr-based methanol electrooxidation catalyst using a novel approach.

Investigation of Transport Mechanism and Nanostructure of Nylon-6,6/PVA Blend Polymers.

A casting technique was used to prepare poly(vinyl alcohol) (PVA) blend polymers with different concentrations of Nylon-6,6 to increase the free-volume size and control the ionic conductivity of the blended polymers. The thermal activation energy for some blends is lower than that of pure polymers, indicating that their thermal stability is somewhere in between that of pure Nylon-6,6 and pure PVA. The degree of crystallinity of the blend sample (25.7%) was lower than that of the pure components (41.0 and 31.6% for pure Nylon-6,6 and PVA, respectively). The dielectric properties of the blended samples were investigated for different frequencies (50 Hz-5 MHz). The σac versus frequency was found to obey Jonscher's universal power law. The calculated values of the s parameter were increased from 0.53 to 0.783 for 0 and 100 wt.% Nylon-6,6, respectively, and values less than 1 indicate the hopping conduction mechanism. The barrier height (Wm) was found to increase from 0.33 to 0.72 for 0 and 100 wt.% Nylon-6,6, respectively. The ionic conductivity decreases as the concentration of Nylon-6,6 is blended into PVA because increasing the Nylon-6,6 concentration reduces the number of mobile charge carriers. Positron annihilation lifetime (PAL) spectroscopy was used to investigate the free volume's nanostructure. The hole volume size grows exponentially with the concentration of Nylon-6,6 mixed with PVA. The Nylon-6,6/PVA blends' free-volume distribution indicates that there is no phase separation in the blended samples. Mixing PVA and Nylon-6,6 resulted in a negative deviation (miscible blends), as evidenced by the interaction parameter's negative value. The strong correlation between the free-volume size and other macroscopic properties like ionic conductivity suggests that the free-volume size influences these macroscopic properties.

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications.

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.

FASTory digital twin data.

The vast adoption of machine learning techniques in developing smart solutions increases the need of training and testing data. This data can be either collected from physical systems or created using simulation tools. In this regard, this paper presents a set of data collected using a digital twin known as the FASTory Simulator. The data contains more than 100 K events which are collected during a simulated assembly process. The FASTory simulator is a replica of a real assembly line with web-based industrial controllers. The data have been collected using specific-developed orchestrator. During the simulated process, the orchestrator was able to record all the events that occurred in the system. The provided data contains raw JavaScript Object Notation (JSON) formatted data and filtered Comma Separated Values (CSV) formatted data. This data can be exploited in machine learning for modelling the behaviour of the production systems or as testing data for optimization solution for the production system. Finally, this data has been utilized in a research for comparing different data analysis approaches including Knowledge-based systems and data-based systems.

Epitaxial growth and superconducting properties of thin-film PdFe/VN and VN/PdFe bilayers on MgO(001) substrates.

Single-layer vanadium nitride (VN) and bilayer Pd0.96Fe0.04/VN and VN/Pd0.92Fe0.08 thin-film heterostructures for possible spintronics applications were synthesized on (001)-oriented single-crystalline magnesium oxide (MgO) substrates utilizing a four-chamber ultrahigh vacuum deposition and analysis system. The VN layers were reactively magnetron sputtered from a metallic vanadium target in Ar/N2 plasma, while the Pd1- x Fe x layers were deposited by co-evaporation of metallic Pd and Fe pellets from calibrated effusion cells in a molecular beam epitaxy chamber. The VN stoichiometry and Pd1- x Fe x composition were controlled by X-ray photoelectron spectroscopy. In situ low-energy electron diffraction and ex situ X-ray diffraction show that the 30 nm thick single-layer VN as well as the double-layer VN(30 nm)/Pd0.92Fe0.08(12 nm) and Pd0.96Fe0.04(20 nm)/VN(30 nm) structures have grown cube-on-cube epitaxially. Electric resistance measurements demonstrate a metallic-type temperature dependence for the VN film with a small residual resistivity of 9 µΩ·cm at 10 K, indicating high purity and structural quality of the film. The transition to the superconducting state was observed at 7.7 K for the VN film, at 7.2 K for the Pd0.96Fe0.04/VN structure and at 6.1 K for the VN/Pd0.92Fe0.08 structure with the critical temperature decreasing due to the proximity effect. Contrary to expectations, all transitions were very sharp with the width ranging from 25 mK for the VN film to 50 mK for the VN/Pd0.92Fe0.08 structure. We propose epitaxial single-crystalline thin films of VN and heteroepitaxial Pd1- x Fe x /VN and VN/Pd1- x Fe x (x ≤ 0.08) structures grown on MgO(001) as the materials of a choice for the improvement of superconducting magnetic random access memory characteristics.