A Fuzzy-Taguchi Approach for Improving Dimensional Accuracy of Fused Deposition Modelling (FDM) Built Parts

Fused deposition modeling is one of rapid prototyping system that produces prototypes from plastic materials such as ABS (acrylonitrile-butadiene-styrene) by laying the tracks of semi-molten plastic filament onto a platform in a layer-wise manner from bottom to top. The present work attempts experimental investigations to study influence of important process parameters viz., layer thickness, part orientation, raster angle, air gap and raster width along with their interactions on dimensional accuracy of Fused Deposition Modelling (FDM) processed part. The part produced from FDM machine does not match with dimension of CAD model due to presence of shrinkage. However, shrinkage is more prominent in length and width direction but a positive deviation is observed in thickness direction. It is essential to study the effect of each parameter on responses such as percentage change in length, width, and thickness of specimen. A design of experiment (DOE) is used to study the effect of process parameters on responses. Optimum parameters setting to minimize percentage change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each performance characteristic are different. There are number of techniques available for predicting responses using input parameters e.g. genetic algorithm, artificial neural network, fuzzy inference system (FIS) etc. But present work uses Fuzzy Inference System (Mamdani Fuzzy logic) to predict the dimensional accuracy in part produced by FDM machine. This method is capable of taking into account the uncertainty and impreciseness in measurements which is commonly encountered in shop floor. The model uses all input and output variables in linguistic terms enabling it convenient for practitioners. The inference engine in Mamdani type FIS uses rules which are obtained with help of design of experiment technique (DOE)

Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches

A Novel Approach for Enhancing Thermal Performance of Battery Modules Based on Finite Element Modeling and Predictive Modeling Mechanism

Electric vehicles (EVs) are estimated as the most sustainable solutions for future transportation requirements. However, there are various problems related to the battery pack module and one such problem is invariable high-temperature differences across the battery pack module due to the discharging and charging of batteries under operating conditions of EVs. High-temperature differences across the battery module contribute to the degradation of maximum charge storage and capacity of Li-ion batteries which ultimately affects the performance of EVs. To address this problem, a finite element modeling (FEM) based automated neural network search (ANS) approach is proposed. The research methodology constitutes of four stages: design of air-cooled battery pack module, setup of the FEM constraints and thermal equations, formulating the predictive model on generated data using ANS, and lastly performing multi-objective response optimization of the best fit predictive model to formulate optimum design constraints for the air-cooled battery module. For efficient thermal management of the battery module, an empirical model is formulated using the mentioned methodology for minimizing the maximum temperature differences, standard deviation of temperature across the battery pack module, and battery pack volume. The results obtained are as follows: (1) the battery pack module volume is reduced from 0.003279 m3 to 0.002321 m3 by 29.21%, (2) the maximum temperature differences across the eight cells of battery pack module declines from 6.81 K to 4.38 K by 35.66%, and (3) the standard deviation of temperature across battery pack decreases from 4.38 K to 0.93 K by 78.69%. Thus, the predictive empirical model enhances the thermal management and safety factor of battery module

A combined experimental-numerical framework for residual energy determination in spent lithium-ion battery packs

The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum

A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction

Lithium-ion batteries (LIBs) have recently been used widely in moving devices. Understand status of the batteries can help to predict the failure and improve the effectiveness of using them. There are some lithium-ion information that define the battery health over time. These are state-of-charge (SOC), state-of-health (SOH), and remaining-useful-life (RUL). Normally, a LIB is working under charging and discharging cycles continuously. In this paper, we will focus on the data dependency of different time-slots in a cycle and in a sequence of cycles to retrieve RUL. We leverage multi-channel inputs such as temperature, voltage, current and the nature of peaks cross the cycles to improve our prediction. Comparing to existing methods, the experiments show that we can improve from 0.040 to 0.033 (reduce 17.5%) in RMSE loss, which is significant

Exercise Training in Patients with Heart Failure and Preserved Ejection Fraction: A Meta-analysis of Randomized Control Trials.

BACKGROUND: -Heart failure with preserved ejection fraction (HFPEF) is common and characterized by exercise intolerance and lack of proven effective therapies. Exercise training has been shown to be effective in improving cardiorespiratory fitness (CRF) in patients with systolic heart failure. In this meta-analysis, we aim to evaluate the effects of exercise training on CRF, quality of life and diastolic function in patients with HFPEF. METHODS AND RESULTS: -Randomized controlled clinical trials that evaluated the efficacy of exercise training in patients with HFPEF were included in this meta-analysis. Primary outcome of the study was change in CRF (measured as change in peak oxygen uptake). Impact of exercise training on quality of life (estimated using Minnesota living with heart failure score), left ventricular systolic and diastolic function was also assessed. The study included 276 patients that were enrolled in 6 randomized controlled trials. In the pooled data analysis, HFPEF patients undergoing exercise training had significantly improved CRF (L/min) (Mean difference: 2.72; 95% CI: 1.79 to 3.65) and quality of life (Mean difference: -3.97; 95% CI: -7.21 to -0.72) as compared with the control group. However, no significant change was observed in the systolic function [Ejection Fraction - Weighted Mean difference (WMD): 1.26; 95% CI: -0.13% to 2.66%] or diastolic function [E/A - WMD: 0.08; 95% CI:-0.01 to 0.16] with exercise training in HFPEF patients. CONCLUSIONS: -Exercise training in patients with HFPEF is associated with an improvement in CRF and quality of life without significant changes in left ventricular systolic or diastolic function

Correlation of aspergillus skin hypersensitivity with the duration and severity of asthma

Asthma is a significant health problem worldwide and Allergic Bronchopulmonary aspergillosis (ABPA) complicates the course of 1-2% of patients of asthma. Aspergillus skin hypersensitivity (AH) is the first step for diagnosis of ABPA. This study was conducted to know the correlation of AH with severity and duration of asthma. Patients, age >15 years, of asthma attending this hospital from January 2015 to December 2015 were included. Asthma was diagnosed clinically and confirmed with spirometry. Of 282 patients 206 patients were AH positive. The AST-positivity in patients having severe asthma (96.8%) was higher than that in patients having mild (36.8%) and moderate asthma (80.4%). The median (IQR) duration of asthma of AH positive patients was 5.0 yrs. This study emphasized the need of ABPA screening by intradermal skin test especially in patients having severe asthma and/or those having asthma for longer duration in order for early diagnosis of ABPA

Modelling of manufacturing processes by a computational intelligence approach

Modelling is a term widely used in System Identification (SI), which is referred to as the art and science of building mathematical models of systems using some measured data. The systems of interest in this thesis are additive manufacturing processes such as fused deposition modelling, machining processes such as turning, and finishing processes such as vibratory finishing. These processes comprise multiple input and output variables, making their operating mechanisms complex. In addition, it can be costly to obtain the process data and therefore there is a strong need for effective and efficient ways of modelling these systems. The models developed for a system can help to reveal hidden information such as the dominant input variables and their appropriate settings for operating the system in an optimal way. The models formulated must not only predict the values of output variables accurately on the testing samples but should also be able to capture the dynamics of the systems. This is known as a generalization problem in modelling. The generalization of data obtained from manufacturing systems is a capability highly demanded by the industry. Several modelling methods and types of models were studied by classifying SI in different ways, such as (1) black box, grey box and white box, (2) parametric and non-parametric, and (3) linear SI, non-linear SI and evolutionary SI. A study of the literature also reveals that extensive focus has been paid to computational intelligence (CI) methods such as genetic programming (GP), M5ʹ, adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN), support vector regression (SVR), etc. for modelling the output variables of the systems because of their ability to formulate the models based only on data obtained from the system. It was also learned that by embedding the features of several methods from different fields of SI into a given method, it is possible to improve its generalization ability. Popular variants of GP such as multi-gene genetic programming (MGGP), which evolves the model structure and its coefficients automatically, has been applied extensively. However, the full potential of MGGP has not been achieved due to some shortcomings leading to its poor generalization ability. In the present work, four variants/methods of MGGP are proposed to counter the four shortcomings identified, namely (1) inappropriate procedure of formulation of the MGGP model, (2) inappropriate complexity measure of the MGGP model, (3) difficulty in model selection, and (4) ensuring greater trustworthiness of prediction ability of the model on unseen samples. A robust CI approach was also developed by applying these four variants of MGGP and the M5ʹ method in parallel. These methods are applied in modelling of output variables of various manufacturing systems such as turning, fused deposition modelling and vibratory finishing process. The performance is compared to those of the other methods such as MGGP, SVR, ANFIS and ANN. The statistical comparison conducted reveals that the generalization ability achieved from the four variants of MGGP and robust CI approach is better than those of the other methods. Furthermore, the sensitivity and parametric analysis conducted validates the robustness of the proposed models by unveiling the dominant input variables and hidden non-linear relationships.DOCTOR OF PHILOSOPHY (MAE