Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

In Vivo and In Vitro Assessment of Antinutritional Factors in Peanut and Soy

Raw and heat processed peanut flours were found to contain higher in vitro trypsin inhibitor activity and lectin content than similarly processed soy flour. When fed to weanling rats at a level of 10% protein, the response pattern in food consumption, growth, liver function, and serum chemistries was not consistent among or between the groups. It is concluded that the levels of antinutritional factors in legumes do not correlate with their overall biological impact in feeding studies with the rat.     

Investigation of the optimum differential gear ratio for real driving cycles by experiment design and genetic algorithm

Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condition that affects its torque and speed range. The aim of the study is to obtain the optimum differential gear ratio with fast and accurate optimization calculation without affecting drivability characteristics of the vehicle according to certain driving cycles that represent the new working conditions of the truck. The study is carried on a mining dump truck YT3621 with 9 forward shift manual transmission. Two loading conditions,no load and 40 t,and four on road real driving cycles have been discussed. The truck powertrain is modeled using GT-drive,and DOE-post processing tool of the GT-suite is used for DOE analysis and genetic algorithm optimization.     

An interpretability security framework for intelligent decision support systems based on saliency map

International Journal of Information Security - Benefiting from the high-speed transmission and super-low latency, the Fifth Generation (5G) networks are playing an important role in contemporary...     

Cu<Superscript>2+</Superscript> and Al<Superscript>3+</Superscript> co-substituted cobalt ferrite: structural analysis,morphology and magnetic properties

Cu–Al substituted Co ferrite nanopowders, Co_1?x Cu _x Fe_2?x Al _x O₄ (0.0 ≤ x ≤ 0.8) were synthesized by the co-precipitation method. The effect of Cu–Al substitution on the structural and magnetic properties have been investigated. X-ray diffraction (XRD) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FESEM) and vibrating sample magnetometer (VSM) are used for studying the effect of variation in the Cu–Al substitution and its impact on particle size, magnetic properties such as M _s and H _c . Cu–Al substitution occurs and produce a secondary phase, α-Fe ₂ O ₃. The crystallite size of the powder calcined at 800 ^°C was in the range of 19–26 nm. The lattice parameter decreases with increasing Cu–Al content. The nanostructural features were examined by FESEM images. Infrared absorption (IR) spectra shows two vibrational bands; at around 600 (v ₁) and 400 cm ^?1 (v ₂). They are attributed to the tetrahedral and octahedral group complexes of the spinel lattice, respectively. It was found that the physical and magnetic properties have changed with Cu–Al contents. The saturation magnetization decreases with the increase in Cu–Al substitution. The reduction of coercive force, saturation magnetization and magnetic moments are may be due to dilution of the magnetic interaction.     

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged. Hence, in this paper, it is proposed that the DWT method can be implemented on a field-programmable gate array in a digital architecture (FPGA-DA). We examined the effects of quantization on DWT performance in classification problems to demonstrate its reliability concerning fixed-point math implementations. The Advanced Encryption Standard (AES) algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks (ANN) method. By reducing hardware area by 57%, the proposed system has a higher throughput rate of 88.72%, reliability analysis of 95.5% compared to the other standard methods.     

Destructive and non-destructive evaluation of concrete for optimum sand to aggregate volume ratio

Aggregates are the biggest contributor to concrete volume and are a crucial parameter in dictating its mechanical properties. As such, a detailed experimental investigation was carried out to evaluate the effect of sand-to-aggregate volume ratio (s/a) on the mechanical properties of concrete utilizing both destructive and non-destructive testing (employing UPV (ultrasonic pulse velocity) measurements). For investigation, standard cylindrical concrete samples were made with different s/a (0.36, 0.40, 0.44, 0.48, 0.52, and 0.56), cement content (340 and 450 kg/m³), water-to-cement ratio (0.45 and 0.50), and maximum aggregate size (12 and 19 mm). The effect of these design parameters on the 7, 14, and 28 d compressive strength, tensile strength, elastic modulus, and UPV of concrete were assessed. The careful analysis demonstrates that aggregate proportions and size need to be optimized for formulating mix designs; optimum ratios of s/a were found to be 0.40 and 0.44 for the maximum aggregate size of 12 and 19 mm, respectively, irrespective of the W/C (water-to-cement) and cement content.     

Decentralized state estimation in large-scale systems

A novel approach to decentralized state estimation in a large-scale interconnected system is proposed. The method assumes a known model for the local subsystem only, and therefore is suitable when the other subsystem models and the interaction matrices are partially or totally unknown. An innovation representation suitable for decentralized subsystem state estimation is derived. The state estimation problem is then solved through the parametric identification of the innovation representation. The identification algorithm is based upon a pseudo-linear regression (PLR) principle that attempts minimization of the innovation variances.     

Autoregressive models in stochastic approximation algorithms for parameter estimation

This paper describes an on-line procedure for estimating the parameters of linear discrete time systems when input and output are subjected to measurement noise of unknown statistics. The algorithm is derived through stochastic approximation, To ensure unbiased parameter estimates, the correlated part of the residuals are first estimated by modelling the residuals as an autoregressive series, and then subtracted from the estimated residuals. The algorithm estimates the system parameters and noise parameters simultaneously. Three gain expressions are derived for the estimation algorithm. They are («) scalar gain, (b) diagonal matrix gain, and (c) square matrix gain.     

Influence of Methanol on Prion Reduction during High Temperature and High Pressure Oleochemical Processes

Prion‐reduction in standard biodiesel processes is caused by acidic and alkaline conditions. In the alternative RepCat biodiesel process, efficient prion‐reduction can be achieved by high‐temperature/pressure. However, a possible increase in thermostability of prions in the presence of methanol during these conditions has not previously been investigated, and is verified in this work. Samples are spiked with prions, treated with methanol, and incubated at 220 °C at 80 bar for 30 min. No traces of protease‐resistant prion protein (as proxy for prions) are detected in tallow or glycerine (as the final by‐product) after treatment. Serial dilutions of spiked prions show at least 6 log₁₀ prion reduction. More importantly, similar effects are detected using milder conditions of 200 °C at 70 bar for 15 min, representing the worst‐case conditions of the process. In conclusion, this study shows that methanol does not increase the thermostability of prions and the RepCat process can efficiently eliminate prions and is therefore safe for the usage of category 1 tallow. Practical Applications: The study further supports the applicability of RepCat process in reducing prion‐contamination in the presence of methanol. Furthermore, the conditions leads to a considerable reduction of prions in glycerine obtained as by‐product.