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Abstract The positron annihilation lifetime (PAL) spectroscopy characteristics of ethylene-propylene-diene monomer rubber (EPDM) composites reinforced with treated wheat husk fibers (WHFs) were investigated for the first time. PAL spectroscopy is employed to study the free volume of polymers. The use of lignocellulosic materials as reinforcement in polymeric composites has gained attention due to their low cost, availability, and eco-friendliness. In this study, the impact of the loading concentration on the interfacial adhesion between the EPDM matrix and WHFs is quantified, along with the evaluation of swelling measurement and tensile properties. Additionally, the nanoscopic properties derived from PAL spectroscopy correlate with the composites’ macroscopic properties. In addition, the dielectric properties of the investigated samples have been studied, and their conductivity has been calculated. To determine the conduction mechanism within these samples and how it is affected by the addition of WHF, the change in electrical conductivity with the frequency of the external electric field applied to the samples was studied, and from this, the conduction mechanism was determined, and the barrier height value was calculated. The experimental results provide insights into the relationship between the structure and properties of EPDM-WHF biocomposites, offering valuable knowledge for developing sustainable and high-performance materials.

Vision and Robotic technologies are progressively becoming ubiquitous for automating and digitizing quality control in the food industry. This paper examines the crucial role of advanced automation technologies, including versatile or dedicated robotic systems, specialized end-effectors, machine vision, and efficient material handling systems, which collectively enhance food processing efficiency. This paper aspires to provide a high-level technical review on the crucial role of advanced automation technologies, including versatile or dedicated robotic systems, specialized end-effectors, machine vision, and efficient material handling systems, which collectively enhance food processing efficiency. While the manuscript aims to document the various automation sub-systems utilized generally in food processing, it places a particular emphasis on the primary processing phase of food production. Most food products in the primary processing phase exhibit a plethora of complex physical properties and manipulation conditions, making it difficult to reliably automate the various processes. This research aims to outline the contemporary advances and requirements for integrating various automation technologies, to enhance the efficiency and precision of primary food processing. Furthermore, it aspires to serve as a valuable, up-to-date survey and analysis of the latest advances in automation and vision technologies and their capability to automate a food processing line.

Abstract 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.

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.

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−xFex 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−xFex 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−xFex/VN and VN/Pd1−xFex (x ≤ 0.08) structures grown on MgO(001) as the materials of a choice for the improvement of superconducting magnetic random access memory characteristics.

In recent years, Industry 4.0 has provided many tools to replicate, monitor, and control physical systems. The purpose is to connect production assets to build cyber-physical systems that ensure the safety, quality, and efficiency of production processes. Particularly, the concept of digital twins has been introduced to create the virtual representation of physical systems where both elements are connected to exchange information. This general definition encompasses a series of major challenges for the developers of those functionalities. Among them is how to introduce the human perspective into the virtual replica. Therefore, this paper presents an approach for incorporating human factors in digital twins. This approach introduces a methodology to offer suggestions about employee rotations based on their previous performance during a shift. Afterward, this method is integrated into a digital twin to perform human performance assessments to manage workers’ jobs. Furthermore, the presented approach is mainly comprised of a human skills modelling engine and a human scheduling engine. Finally, for demonstrating the approach, a simulated serial single-product manufacturing assembly line has been introduced.

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.

Human–robot collaboration (HRC) is one of the key aspects of Industry 4.0 (I4.0) and requires intuitive modalities for humans to communicate seamlessly with robots, such as speech, touch, or bodily gestures. However, utilizing these modalities is usually not enough to ensure a good user experience and a consideration of the human factors. Therefore, this paper presents a software component, Multi-Modal Offline and Online Programming (M2O2P), which considers such characteristics and establishes a communication channel with a robot with predefined yet configurable hand gestures. The solution was evaluated within a smart factory use case in the Smart Human Oriented Platform for Connected Factories (SHOP4CF) EU project. The evaluation focused on the effects of the gesture personalization on the perceived workload of the users using NASA-TLX and the usability of the component. The results of the study showed that the personalization of the gestures reduced the physical and mental workload and was preferred by the participants, while overall the workload of the tasks did not significantly differ. Furthermore, the high system usability scale (SUS) score of the application, with a mean of 79.25, indicates the overall usability of the component. Additionally, the gesture recognition accuracy of M2O2P was measured as 99.05%, which is similar to the results of state-of-the-art applications.

The rapid emerging technologies in various fields permitted the creation of simulation tools. These tools are designed to replicate physical systems in order to provide faster, cheaper and more detailed illustrative analysis of the physical system. In this regard, the concept of digital twins has been introduced to generally define these simulation tools. In fact, and according to the creator of the digital twin term Micheal Grieves, a digital twin is defined as a physical system, a digital replica of the physical system and information flow between the former parts. This definition is simple and generic for describing digital twins and yet, holistic. This broad definition creates a challenge for developers who target the development of such applications. Therefore, this paper presents a paradigm for architecting digital twins for manufacturing processes. The approach is inspired by the definitions of the ISA95 standard and the onion concept of computer applications to create multi-layer and multi-level concepts. Furthermore, and to satisfy the different required features by industries, the approach considers a multi-perspective concept that allows the separation of the digital twin views based on functionality. This paradigm aims at providing a modular, scalable, reusable, interoperable and composable approach for developing digital twins. Then, an implementation of the approach has been introduced using an ontology-based system and the IEC61499 standard. This implementation has been demonstrated on a discrete manufacturing assembly line.

This paper presents a data-driven approach for the prognosis of the gradual behavioural deterioration of conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems. The approach relies on the knowledge of the power consumption of a conveyor belt motor driver. Data are collected for two separate cases: the static case and dynamic case. In the static case, power consumption data are collected under different loads and belt tension. These data are used by a prognostic model (artificial neural network (ANN)) to learn the conveyor belt motor driver’s power consumption pattern under different belt tensions and load conditions. The data collected during the dynamic case are used to investigate how the belt tension affects the movement of pallets between conveyor zones. During the run time, the trained prognostic model takes real-time power consumption measurements and load information from a testbench (a discrete multirobot mobile assembling line) and predicts a belt tension class. A consecutive mismatch between the predicted belt tension class and optimal belt tension class is an indication of failure, i.e., a gradual loss of belt tension. Hence, maintenance steps must be taken to avoid further catastrophic situations such as belt slippages on head pulleys, material slippages and belt wear and tear.

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.

The enterprises to be competitive are constantly looking for continuous increase of productivity, quality and level of services. With the development of Industry 4.0 concept, manufacturers are more confident about new advantages of automation and systems integration. Lean management is well developed and empirically proven effective managerial approach. Combining Lean and Industry 4.0 practices seems to be necessary evolutionary step for further raise the level of operational excellence (exploitation of finance, workload, materials, machines/devices). There is an increasing number of Industry 4.0 solutions used to reduce waste (as known from Lean Management). Therefore, the main objective of this article aims at presenting the results of a literature review on the concept of ‘Lean Industry 4.0ʹ. Dynamic methodology called “Systematic Literature Network Analysis (SLNA)” was used. It combines the Systematic Literature Review approach with the quantitative analysis of bibliographic networks to detect emerging topics and the dynamic evolution of the topics. The paper is a comprehensive systematization and rationalization of knowledge about the integration of LM and I4.0 concepts, identifies the most important research trends and defines directions for future research. The article contains a framework that presents the current state of knowledge in the area of Lean Industry 4.0.

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.

The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, such as industrial machines, which are involved in production processes. The early detection of malfunctions and/or improvable system values permits the anticipation to critical issues that may delay or even disallow productivity. Advances on Information and Communication Technologies (ICT)-based technologies allows the collection of data on system runtime. In fact, the data is not only collected but formatted and integrated in computer nodes. Then, the formatted data can be further processed and analyzed. This article focuses on the utilization of standard Key Performance Indicators (KPIs), which are a set of parameters that permit the evaluation of the performance of systems. More precisely, the presented research work demonstrates the implementation and visualization of a set of KPIs defined in the ISO 22400 standard-Automation systems and integration, for manufacturing operations management. The approach is validated within a discrete manufacturing web-based interface that is currently used for monitoring and controlling an assembly line at runtime. The selected ISO 22400 KPIs are described within an ontology, which the description is done according to the data models included in the KPI Markup Language (KPIML), which is an XML implementation developed by the Manufacturing Enterprise Solutions Association (MESA) international organization.