Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation

Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem \cite{BestounKamalFuzzy2017}. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system \cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.Comment: 21 page

One-class classifiers based on entropic spanning graphs

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

Modeling and Optimization of Stochastic Process Parameters in Complex Engineering Systems

For quality engineering researchers and practitioners, a wide number of statistical tools and techniques are available for use in the manufacturing industry. The objective or goal in applying these tools has always been to improve or optimize a product or process in terms of efficiency, production cost, or product quality. While tremendous progress has been made in the design of quality optimization models, there remains a significant gap between existing research and the needs of the industrial community. Contemporary manufacturing processes are inherently more complex - they may involve multiple stages of production or require the assessment of multiple quality characteristics. New and emerging fields, such as nanoelectronics and molecular biometrics, demand increased degrees of precision and estimation, that which is not attainable with current tools and measures. And since most researchers will focus on a specific type of characteristic or a given set of conditions, there are many critical industrial processes for which models are not applicable. Thus, the objective of this research is to improve existing techniques by not only expanding their range of applicability, but also their ability to more realistically model a given process. Several quality models are proposed that seek greater precision in the estimation of the process parameters and the removal of assumptions that limit their breadth and scope. An extension is made to examine the effectiveness of these models in both non-standard conditions and in areas that have not been previously investigated. Upon the completion of an in-depth literature review, various quality models are proposed, and numerical examples are used to validate the use of these methodologies

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Experimental Studies on Machinability of Inconel Super Alloy during Electro-Discharge Machining: Emphasis on Surface Integrity and Metallurgical Characteristics of the EDMed Work Surface

Inconel alloys are Nickel-Chromium based high temperature super alloys widely applied in aerospace, marine, nuclear power generation; chemical, petrochemical and process industries. Execution of traditional machining operations on Inconel super alloy is quite difficult due to its very low thermal conductivity which increases thermal effects during machining operations. Inconel often exhibits strong work hardening behavior, high adhesion characteristics onto the tool face, and thereby alters cutting process parameters to a remarkable extent. Additionally, Inconel may contain hard abrasive particles and carbides that create excessive tool wear; and, hence, surface integrity of the end product appears disappointing. The extent of tool life is substantially reduced. Thus, Inconel super alloys are included in the category of ‘difficult-to-cut’ materials. In view of the difficulties faced during conventional machining, non-traditional machining routes like Electro-Discharge Machining (EDM), Wire Electro-Discharge Machining (WEDM), micro-machining (micro-electro-discharge drilling) etc. are being attempted for processing of Inconel in order to achieve desired contour and intricate geometry of the end product with reasonably good dimensional accuracy. However, low material removal rate and inferior surface integrity seem to be a challenge. In this context, the present dissertation has aimed at investigating machining and machinability aspects of Inconel super alloys (different grades) during electro-discharge machining. Effects of process control parameters (viz. peak discharge current, pulse-on time, gap voltage, duty factor, and flushing pressure) on influencing EDM performance in terms of Material Removal Rate (MRR), Electrode Wear Rate (EWR) and Surface Roughness (SR) of the EDMed Inconel specimens have been examined. Morphology along with topographical features of the EDMed Inconel work surface have been studied in view of severity of surface cracking and extent of white layer depth. Additionally, X-Ray Diffraction (XRD) analysis has been carried out to study metallurgical characteristics of the EDMed work surface of Inconel specimens (viz. phases present and precipitates, extent of grain refinement, crystallite size, and dislocation density etc.) in comparison with that of ‘as received’ parent material. Results, obtained thereof, have been interpreted with relevance to Energy Dispersive X-ray Spectroscopy (EDS) analysis, residual stress and micro-indentation hardness test data. Effort has been made to determine the most appropriate EDM parameters setting to optimize MRR, EWR, along with Ra (roughness average), relative Surface Crack Density (SCD), as well as relative White Layer Thickness (WLT) observed onto the EDMed work surface of Inconel specimens. Moreover, an attempt has been made to examine the ease of electro-discharge machining on Inconel work materials using Deep Cryogenically Treated (DCT) tool/workpiece. A unified attempt has also made to compare surface integrity and metallurgical characteristics of the EDMed Inconel work surface as compared to the EDMed A2 tool steel (SAE 304SS) as well as EDMed Titanium alloy (Ti-6Al-4V)

Assessment of contributing factors to the reduction of diarrhea in rural communities of Para, Brazil

In developing communities the occurrence of diarrhea has been reported at elevated levels as compared to those communities in more developed regions. Diarrheal diseases were linked to over one million deaths in 2012 throughout the world. While multiple pathways are present for the transmission of diarrheal diseases, water has been the focus for many aid organizations. Point-of-use (POU) water treatment methods are a common tool used by aid organizations in efforts to provide potable water. The CAWST biosand filter is a POU tool that has shown removal effectiveness of pathogenic microorganisms ranging from 90-99%. However, minimal literature was found that reported on the effectiveness of the filter within the larger body of the complex system found in all communities. Therefore a hypothesis was derived to confirm that the intervention of a CAWST biosand filter is the most significant factor in the reduction of the diarrheal health burden within households in developing regions. Communities located along the Amazon River in Para, Brazil were selected for study. Structural Equation Modeling (SEM) was utilized to aid in representing the complex set of relationships within the communities. The Mahalanobis-Taguchi Strategy (MTS) was also used to confirm variable significance in the SEM model. Results show that while the biosand filter does aid in the reduction of diarrheal occurrences it is not the most significant factor. Results varied on which factor influenced diarrheal occurrences the greatest but consistently included education, economic status, and sanitation. Further, results from the MTS analysis reported education as the largest factor influencing household health. Continued work is needed for further understanding of these factors and their relationships to diarrhea reduction. --Abstract, page iv

ADAPTIVE SEARCH AND THE PRELIMINARY DESIGN OF GAS TURBINE BLADE COOLING SYSTEMS

This research concerns the integration of Adaptive Search (AS) technique such as the Genetic Algorithms (GA) with knowledge based software to develop a research prototype of an Adaptive Search Manager (ASM). The developed approach allows to utilise both quantitative and qualitative information in engineering design decision making. A Fuzzy Expert System manipulates AS software within the design environment concerning the preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry models have been developed for the project in collaboration with Rolls Royce plc. The research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection (ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design solutions as potential design options. ARTS is a GA technique that is particularly suitable for real world problems having multiple sub-optima. KBHC uses information gathered during the ARTS search as well as information from the designer to perform a deterministic hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design solution sensitivity, design variable sensitivities and constraint sensitivities are calculated following Taguchi's methodology, which extracts sensitivity information with a very small number of model evaluations. Each potential design option is then qualitatively evaluated separately for manufacturability, choice of materials and some designer's special preferences using the knowledge of domain experts. In order to guarantee that the qualitative evaluation module can evaluate any design solution from the entire design space with a reasonably small number of rules, a novel knowledge representation technique is developed. The knowledge is first separated in three categories: inter-variable knowledge, intra-variable knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then integrated using a concept of compromise. Information about the "good" design solutions is presented to the designer through a designer's interface for decision support.Rolls Royce plc., Bristol (UK

Study on Parametric Optimization of Fused Deposition Modelling (FDM) Process

Rapid prototyping (RP) is a generic term for a number of technologies that enable fabrication of physical objects directly from CAD data sources. In contrast to classical methods of manufacturing such as milling and forging which are based on subtractive and formative principles espectively, these processes are based on additive principle for part fabrication. The biggest advantage of RP processes is that an entire 3-D (three-dimensional) consolidated assembly can be fabricated in a single setup without any tooling or human intervention; further, the part fabrication methodology is independent of the mplexity of the part geometry. Due to several advantages, RP has attracted the considerable attention of manufacturing industries to meet the customer demands for incorporating continuous and rapid changes in manufacturing in shortest possible time and gain edge over competitors. Out of all commercially available RP processes, fused deposition modelling (FDM) uses heated thermoplastic filament which are extruded from the tip of nozzle in a prescribed manner in a temperature controlled environment for building the part through a layer by layer deposition method. Simplicity of operation together with the ability to fabricate parts with locally controlled properties resulted in its wide spread application not only for prototyping but also for making functional parts. However, FDM process has its own demerits related with accuracy, surface finish, strength etc. Hence, it is absolutely necessary to understand the shortcomings of the process and identify the controllable factors for improvement of part quality. In this direction, present study focuses on the improvement of part build methodology by properly controlling the process parameters. The thesis deals with various part quality measures such as improvement in dimensional accuracy, minimization of surface roughness, and improvement in mechanical properties measured in terms of tensile, compressive, flexural, impact strength and sliding wear. The understanding generated in this work not only explain the complex build mechanism but also present in detail the influence of processing parameters such as layer thickness, orientation, raster angle, raster width and air gap on studied responses with the help of statistically validated models, microphotographs and non-traditional optimization methods. For improving dimensional accuracy of the part, Taguchi‟s experimental design is adopted and it is found that measured dimension is oversized along the thickness direction and undersized along the length, width and diameter of the hole. It is observed that different factors and interactions control the part dimensions along different directions. Shrinkage of semi molten material extruding out from deposition nozzle is the major cause of part dimension reduction. The oversized dimension is attributed to uneven layer surfaces generation and slicing constraints. For recommending optimal factor setting for improving overall dimension of the part, grey Taguchi method is used. Prediction models based on artificial neural network and fuzzy inference principle are also proposed and compared with Taguchi predictive model. The model based on fuzzy inference system shows better prediction capability in comparison to artificial neural network model. In order to minimize the surface roughness, a process improvement strategy through effective control of process parameters based on central composite design (CCD) is employed. Empirical models relating response and process parameters are developed. The validity of the models is established using analysis of variance (ANOVA) and residual analysis. Experimental results indicate that process parameters and their interactions are different for minimization of roughness in different surfaces. The surface roughness responses along three surfaces are combined into a single response known as multi-response performance index (MPI) using principal component analysis. Bacterial foraging optimisation algorithm (BFOA), a latest evolutionary approach, has been adopted to find out best process parameter setting which maximizes MPI. Assessment of process parameters on mechanical properties viz. tensile, flexural, impact and compressive strength of part fabricated using FDM technology is done using CCD. The effect of each process parameter on mechanical property is analyzed. The major reason for weak strength is attributed to distortion within or between the layers. In actual practice, the parts are subjected to various types of loadings and it is necessary that the fabricated part must withhold more than one type of loading simultaneously.To address this issue, all the studied strengths are combined into a single response known as composite desirability and then optimum parameter setting which will maximize composite desirability is determined using quantum behaved particle swarm optimization (QPSO). Resistance to wear is an important consideration for enhancing service life of functional parts. Hence, present work also focuses on extensive study to understand the effect of process parameters on the sliding wear of test specimen. The study not only provides insight into complex dependency of wear on process parameters but also develop a statistically validated predictive equation. The equation can be used by the process planner for accurate wear prediction in practice. Finally, comparative evaluation of two swarm based optimization methods such as QPSO and BFOA are also presented. It is shown that BFOA, because of its biologically motivated structure, has better exploration and exploitation ability but require more time for convergence as compared to QPSO. The methodology adopted in this study is quite general and can be used for other related or allied processes, especially in multi input, multi output systems. The proposed study can be used by industries like aerospace, automobile and medical for identifying the process capability and further improvement in FDM process or developing new processes based on similar principle