Field equations for incompressible non-viscous fluids using artificial intelligence

In this article, we introduce new field equations for incompressible non-viscous fluids, which can be treated similarly to Maxwell’s electromagnetic equations based on artificial intelligence algorithms. Lagrangian and Hamiltonian formulations are used to arrive at field equations that are solved using convolutional neural networks. Four linear differential equations, which describe the two fields, namely, the dynamic pressure and the vortex fields, are derived, and these can be used in place of Euler’s equation. The only assumption while deriving this equation is that the dynamic pressure and vortex fields obey the superposition principle. The important finding to be noted is that Euler’s fluid equations can be converted into field equations analogous to Maxwell’s electromagnetic equations. We solve the flow problem for laminar flow past a cylinder, sphere, and cone in two dimensions similar to the conduction in a uniform electric field and arrive at closed-form expressions. These closed-form expressions, which are obtained for the potentials of fluid flow, are similar to the streamline potential functions in the case of fluid dynamics.

     

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.     

Mixed-Mode Stress Intensity Factor Determination of Riveted Lap Joints Used in Aircraft Fuselage Structures

Journal of Failure Analysis and Prevention - In the present work, mixed-mode stress intensity factor (SIF) of multiple cracks in a riveted lap joint has been determined, with and without the...     

Application of fuzzy quality function deployment for sustainable design of consumer electronics products: a case study

With a steep increase in the demand for consumer electronics products, the contemporary manufacturers are committed toward sustainable development of such products. There exists a scope for developing a methodology for enabling sustainable development of consumer electronics products. In this context, fuzzy quality function deployment (QFD) approach has been presented in this article in order to prioritize relevant customer requirements, sustainability parameters and sustainability initiatives. Key influential parameters for sustainable development of consumer electronics products have been identified from the literature. In the first phase of fuzzy QFD, parameters influencing sustainable development have been prioritized in accordance with customer requirements. In the second phase, environmental design initiatives have been prioritized based on critical sustainability parameters. From phase I of fuzzy QFD, ‘reduction in environmental release’ has been found as the most significant sustainability parameter with a crisp value of 22.83, and from phase II, environmental impact assessment is proved to be the significant design method with a crisp value of 20.40. The methodology would provide a comprehensive understanding to practitioners on the interrelationships among customer requirements, sustainability parameters and environmentally benign initiatives for development of consumer electronic products. The generic model developed can be applied to most of the consumer electronics product     

Optimization of A-TIG process parameters using response surface methodology

In the present work, the influence of process parameters such as welding current (I), welding speed (S), and flux coating density (F) on different aspects of weld bead geometry for example depth of penetration (DOP), bead width (BW), depth to width ratio (D/W), and weld fusion zone area (WA) were investigated by using the central composite design (CCD). 9–12% Cr ferritic stainless steel (FSS) plates were welded using A-TIG welding. It was observed that all input variables have a direct influence on the DOP, BW, and D/W. However, flux coating density has no significant effect on WA. Mathematical models were generated from the obtained responses to predict the weld bead geometry. An optimized DOP, BW, D/W, and WA of 6.95?mm, 8.76?mm, 0.80, and 41.99?mm², respectively, were predicted at the welding current of 213.78 A, the welding speed of 96.22?mm/min, and the flux coating density of 1.99?mg/cm². Conformity test was done to check the practicability of the developed models. The conformity test results were in good agreement with the predicted values. Arc constriction and reversal in Marangoni convection were considered as major mechanisms for the deep and narrow weld bead during A-TIG welding.     

Concurrent consideration of evacuation safety and productivity in manufacturing facility planning using multi-paradigm simulations

Manufacturing facilities are expected to maintain a high level of production and at the same time, employ strict safety standards to ensure the safe evacuation of the people in the event of emergencies (fire is considered in this paper). These two goals are often conflicting. This paper presents a methodology to evaluate evacuation safety versus productivity concurrently for various, widely known manufacturing layouts. While the safety performance indicators such as evacuation times are inferred from the crowd (agent based) simulation, the productivity performance indicators (e.g. throughput) are analyzed using the discrete event simulation. To this end, this research focuses on creating innovative techniques for developing accurate crowd simulations, where Belief-Desire-Intention (BDI) agent framework is employed to build each person’s individual actions and the interactions between them. The data model and rule based action algorithms for each agent are reverse-engineered from the human-in-the-loop experiments in the immersive virtual reality environments. Finally, experiments are conducted using the constructed simulations to compare safety and productivity for different layouts. To demonstrate the proposed methodology, an automotive power-train (engine and transmission) manufacturing plant was used. Initial results look quite promising.     

B2B e-commerce supply chain integration and performance: A contingency fit perspective on the role of environment

Drawing upon contingency theory “fit” research in the IT and supply chain management literature, we applied the “fit” concept to the relationship between B2B e-commerce supply chain integration and performance. The results demonstrated that the effect of B2B supply chain integration on financial, market, and operational performance decreased as product turbulence and demand unpredictability jointly increased. Managerial implications include the conditions under which IT investments yield performance improvement and the need for firms to actively manage demand uncertainty.     

A semi-parametric generalization of the Cox proportional hazards regression model: Inference and Applications

The assumption of proportional hazards (PH) fundamental to the Cox PH model sometimes may not hold in practice. In this paper, we propose a generalization of the Cox PH model in terms of the cumulative hazard function taking a form similar to the Cox PH model, with the extension that the baseline cumulative hazard function is raised to a power function. Our model allows for interaction between covariates and the baseline hazard and it also includes, for the two sample problem, the case of two Weibull distributions and two extreme value distributions differing in both scale and shape parameters. The partial likelihood approach can not be applied here to estimate the model parameters. We use the full likelihood approach via a cubic B-spline approximation for the baseline hazard to estimate the model parameters. A semi-automatic procedure for knot selection based on Akaike’s information criterion is developed. We illustrate the applicability of our approach using real-life data.     

Effect of alternating and direct current in an electrocoagulation process on the removal of cadmium from water

The main objective of this study was to investigate the effects of AC and DC on the removal of cadmium from water using iron as anode and cathode. The various operating parameters on the removal efficiency of cadmium were investigated. The results showed that the optimum removal efficiency of 98.1 and 97.3% with the energy consumption of 0.734 and 1.413 kWh/kL was achieved at a current density of 0.2 A/dm(2), at pH of 7.0 using AC and DC respectively. The adsorption process follows second order kinetics and the temperature studies showed that adsorption was endothermic and spontaneous in nature.     

Towards locally and globally shape-aware reverse 3D modeling

The process of re-creating CAD models from actual physical parts, formally known as digital shape reconstruction (DSR) is an integral part of product development, especially in re-design. While, the majority of current methods used in DSR are surface-based, our overarching goal is to obtain direct parameterization of 3D meshes, by avoiding the actual segmentation of the mesh into different surfaces. As a first step towards reverse modeling physical parts, we extract (1) locally prominent cross-sections (PCS) from triangular meshes, and (2) organize and cluster them into sweep components, which form the basic building blocks of the re-created CAD model. In this paper, we introduce two new algorithms derived from Locally Linear Embedding (LLE) (Roweis and Sauk, 2000 [3]) and Affinity Propagation (AP) (Frey and Dueck, 2007 [4]) for organizing and clustering PCS. The LLE algorithm analyzes the cross-sections (PCS) using their geometric properties to build a global manifold in an embedded space. The AP algorithm, then clusters the local cross sections by propagating affinities among them in the embedded space to form different sweep components. We demonstrate the robustness and efficiency of the algorithms through many examples including actual laser-scanned (point cloud) mechanical parts.