A stable and accurate control-volume technique based on integrated radial basis function networks for fluid-flow problems

Radial basis function networks (RBFNs) have been widely used in solving partial differential equations as they are able to provide fast convergence. Integrated RBFNs have the ability to avoid the problem of reduced convergence-rate caused by differentiation. This paper is concerned with the use of integrated RBFNs in the context of control-volume discretisations for the simulation of fluid-flow problems. Special attention is given to (i) the development of a stable high-order upwind scheme for the convection term and (ii) the development of a local high-order approximation scheme for the diffusion term. Benchmark problems including the lid-driven triangular-cavity flow are employed to validate the present technique. Accurate results at high values of the Reynolds number are obtained using relatively-coarse grids

Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm

The residual stresses induced during shaping and machining play an important role in determining the integrity and durability of metal components. An important issue of producing safety critical components is to find the machining parameters that create compressive surface stresses or minimise tensile surface stresses. In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The new method employs a hierarchical optimisation structure to improve the modelling efficiency, where two learning mechanisms cooperate together: NSGA-II is used to improve the model’s structure while the gradient descent method is used to optimise the numerical parameters. This hybrid approach is then successfully applied to the problem that concerns the prediction of machining induced residual stresses in aerospace aluminium alloys. Based on the developed reliable prediction models, NSGA-II is further applied to the multi-objective optimal design of aluminium alloys in a ‘reverse-engineering’ fashion. It is revealed that the optimal machining regimes to minimise the residual stress and the machining cost simultaneously can be successfully located

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Inverse determination of constitutive equations and cutting force modelling for complex tools using oxley's predictive machining theory

In analysis of machining processes, finite element analysis is widely used to predict forces, stress distributions, temperatures and chip formation. However, constitutive models are not always available and simulation of cutting processes with complex tool geometries can lead to extensive computation time. This article presents an approach to determine constitutive parameters of the Johnson-Cook's flow stress model by inverse modelling as well as a methodology to predict process forces and temperatures for complex three-dimensional tools using Oxley's machining theory. In the first part of this study, an analytically based computer code combined with a particle swarm optimization (PSO) algorithm is used to identify constitutive models for 70MnVS4 and an aluminium-alloyed ultra-high-carbon steel (UHC-steel) from orthogonal milling experiments. In the second part, Oxley's predictive machining theory is coupled with a multi-dexel based material removal model. Contact zone information (width of cut, undeformed chip thickness, rake angle and cutting speed) are calculated for incremental segments on the cutting edge and used as input parameters for force and temperature calculations. Subsequently, process forces are predicted for machining using the inverse determined constitutive models and compared to actual force measurements. The suggested methodology has advantages regarding the computation time compared to finite element analyses.BMBF/02PN205

Nature-Inspired Adaptive Architecture for Soft Sensor Modelling

This paper gives a general overview of the challenges present in the research field of Soft Sensor building and proposes a novel architecture for building of Soft Sensors, which copes with the identified challenges. The architecture is inspired and making use of nature-related techniques for computational intelligence. Another aspect, which is addressed by the proposed architecture, are the identified characteristics of the process industry data. The data recorded in the process industry consist usually of certain amount of missing values or sample exceeding meaningful values of the measurements, called data outliers. Other process industry data properties causing problems for the modelling are the collinearity of the data, drifting data and the different sampling rates of the particular hardware sensors. It is these characteristics which are the source of the need for an adaptive behaviour of Soft Sensors. The architecture reflects this need and provides mechanisms for the adaptation and evolution of the Soft Sensor at different levels. The adaptation capabilities are provided by maintaining a variety of rather simple models. These particular models, called paths in terms of the architecture, can for example focus on different partition of the input data space, or provide different adaptation speeds to changes in the data. The actual modelling techniques involved into the architecture are data-driven computational learning approaches like artificial neural networks, principal component regression, etc

An optimized prediction of FRP bars in concrete bond strength employing soft computing techniques

The precise estimation of the bonding strength between concrete and fiber-reinforced polymer (FRP) bars holds significant importance for reinforced concrete structures. This study introduces a new methodology that utilizes soft computing methods to enhance the prediction of FRP bars’ bonding strength. A significant compilation of experimental bond strength tests is assembled, covering various variables. Significant variables that affect bonding strength are found in the study of this database. The prediction process is optimized using soft computing methods, particularly Gene Expression Programming (GEP) and the Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR).The proposed soft computing approaches accommodate complex relationships and optimize prediction accuracy depending on the input variables. Results demonstrate its effectiveness in predicting bond strength and comparing it with existing codes and other models from the literature. The results have shown that the MOGA-EPR and the GEP models have high R2 values between 0.91 and 0.94. The proposed new models enhance the reliability and efficiency of designing and assessing FRP-reinforced concrete

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm. &nbsp

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently apromising and leading area of research for component repair and restoration. The Issues of high cost and timeconsumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover,the potential challenges in dealing with complex components for repair and restoration in the (AM) domain requirethe establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimizationmethod must cover all important parameters for the complex configuration of structural components underrestoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration ofmonitoring. This improvement is based on facilitating the real-time identification of failures with accuracy andgiving a clear monitoring vision according to the intended targets like geometric distortions, residual stressesevaluation, and defect characterization. The improvement involves overcoming a number of challenges such as thepre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with someoptions that improve the current procedure. Also, this study will conclude and suggest a further framework andnew knowledge for restoration and product life cycle extension. This developed ANN can be used at the real paceof modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN.This model development using a neural network has attained a good manipulation of AM. The predicted data fromANN model that was determined and achieved in this study can be used to facilitate and enhance any further studyas base knowledge in merging the ANN with another AI to form a hybrid algorithm. &nbsp