Internal defect and process parameter analysis during friction stir welding of Al 6061 sheets

Welding parameters like welding speed, rotation speed, plunge depth, shoulder diameter etc., influence the weld zone properties, microstructure of friction stir welds, and forming behavior of welded sheets in a synergistic fashion. The main aims of the present work are to (1) analyze the effect of welding speed, rotation speed, plunge depth, and shoulder diameter on the formation of internal defects during friction stir welding (FSW), (2) study the effect on axial force and torque during welding, (c) optimize the welding parameters for producing internal defect-free welds, and (d) propose and validate a simple criterion to identify defect-free weld formation. The base material used for FSW throughout the work is Al 6061T6 having a thickness value of 2.1 mm. Only butt welding of sheets is aimed in the present work. It is observed from the present analysis that higher welding speed, higher rotation speed, and higher plunge depth are preferred for producing a weld without internal defects. All the shoulder diameters used for FSW in the present work produced defect-free welds. The axial force and torque are not constant and a large variation is seen with respect to FSW parameters that produced defective welds. In the case of defect-free weld formation, the axial force and torque are relatively constant. A simple criterion, (a,tau/a,p)(defective) > (a,tau/a,p)(defect free) and (a,F/a,p)(defective) > (a,F/a,p)(defect free), is proposed with this observation for identifying the onset of defect-free weld formation. Here F is axial force, tau is torque, and p is welding speed or tool rotation speed or plunge depth. The same criterion is validated with respect to Al 5xxx base material. Even in this case, the axial force and torque remained constant while producing defect-free welds.

In-line shape compensation for roll forming through process parameter monitoring

Buddhika’s Phd topic is In-line shape compensation for roll forming through process parameter monitoring. It mainly discussed about defects monitoring and compensation in high strength steel roll forming for automotive applications.

Supercritical fluid extraction from fennel (Foeniculum vulgare): global yield, composition and kinetic data

Supercritical fluid extraction (SFE) from solids has proven to be technically feasible for almost any system; nonetheless, its economical viability has been proven for a restricted number of systems. A common practice is to compare the cost of manufacturing of vegetable extracts by a variety of techniques without deeply considering the huge differences in composition and functional properties among the various types of extracts obtained; under this circumstance, the cost of manufacturing do not favor SFE. Additionally, the influence of external parameters such as the agronomic conditions and the SFE system geometry are not considered. In the present work, these factors were studied for the system fennel seeds + CO2. The effects of the harvesting season and the degree of maturation on the global yields for the system fennel seeds + CO2 were analyzed at 300 bar and 40 degrees C. The effects of the pressure on the global yields were determined for the temperatures of 30 and 40 degrees C. Kinetics experiments were done for various ratios of bed height to bed diameter. Fennel extracts were also obtained by hydrodistillation and low-pressure solvent extraction. The chemical composition of the fennel extracts were determined by gas chromatography. The SFE maximum global yield (12.5%, dry basis) was obtained with dry harvested fennel seeds. Anethole and fenchone were the major constituents of the extract; the following fat acids palmitic (C16H32O2), palmitoleic stearic (C18H36O2), oleic (C18H34O2), linoleic (C18H32O2) and linolenic (C18H30O2) were also detected in the extracts. A relation between amounts of feed and solvent, bed height and diameter, and solvent flow rate was proposed. The models of Sovova, Goto et al. and Tan and Lion were capable of describing the mass transfer kinetics. (c) 2005 Elsevier B.V. All rights reserved.

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Effects Of The Flash Welding Process On Mechanical And Microstructural Properties Of Structural Steel Joints Assessed Using Dest

ASTM A529 carbon¿manganese steel angle specimens were joined by flash butt welding and the effects of varying process parameter settings on the resulting welds were investigated. The weld metal and heat affected zones were examined and tested using tensile testing, ultrasonic scanning, Rockwell hardness testing, optical microscopy, and scanning electron microscopy with energy dispersive spectroscopy in order to quantify the effect of process variables on weld quality. Statistical analysis of experimental tensile and ultrasonic scanning data highlighted the sensitivity of weld strength and the presence of weld zone inclusions and interfacial defects to the process factors of upset current, flashing time duration, and upset dimension. Subsequent microstructural analysis revealed various phases within the weld and heat affected zone, including acicular ferrite, Widmanstätten or side-plate ferrite, and grain boundary ferrite. Inspection of the fracture surfaces of multiple tensile specimens, with scanning electron microscopy, displayed evidence of brittle cleavage fracture within the weld zone for certain factor combinations. Test results also indicated that hardness was increased in the weld zone for all specimens, which can be attributed to the extensive deformation of the upset operation. The significance of weld process factor levels on microstructure, fracture characteristics, and weld zone strength was analyzed. The relationships between significant flash welding process variables and weld quality metrics as applied to ASTM A529-Grade 50 steel angle were formalized in empirical process models.

Data consistency management in an open smart home management platform

In this paper, the authors introduce a novel mechanism for data management in a middleware for smart home control, where a relational database and semantic ontology storage are used at the same time in a Data Warehouse. An annotation system has been designed for instructing the storage format and location, registering new ontology concepts and most importantly, guaranteeing the Data Consistency between the two storage methods. For easing the data persistence process, the Data Access Object (DAO) pattern is applied and optimized to enhance the Data Consistency assurance. Finally, this novel mechanism provides an easy manner for the development of applications and their integration with BATMP. Finally, an application named "Parameter Monitoring Service" is given as an example for assessing the feasibility of the system.

A Novel Framework for Real-time Traffic Flow Parameter Estimation from Aerial Videos

Thesis (Master's)--University of Washington, 2016-06

Mean and variance of estimates of the bispectrum of a harmonic random process-an analysis including leakage effects

Analytical expressions are derived for the mean and variance, of estimates of the bispectrum of a real-time series assuming a cosinusoidal model. The effects of spectral leakage, inherent in discrete Fourier transform operation when the modes present in the signal have a nonintegral number of wavelengths in the record, are included in the analysis. A single phase-coupled triad of modes can cause the bispectrum to have a nonzero mean value over the entire region of computation owing to leakage. The variance of bispectral estimates in the presence of leakage has contributions from individual modes and from triads of phase-coupled modes. Time-domain windowing reduces the leakage. The theoretical expressions for the mean and variance of bispectral estimates are derived in terms of a function dependent on an arbitrary symmetric time-domain window applied to the record. the number of data, and the statistics of the phase coupling among triads of modes. The theoretical results are verified by numerical simulations for simple test cases and applied to laboratory data to examine phase coupling in a hypothesis testing framework

Reputation modelling in citizen science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, as the gathered information is from the crowd, the data quality is always hard to manage. There are many ways to manage data quality, and reputation management is one of the common approaches. In recent year, many research teams have deployed many audio or image sensors in natural environment in order to monitor the status of animals or plants. The collected data will be analysed by ecologists. However, as the amount of collected data is exceedingly huge and the number of ecologists is very limited, it is impossible for scientists to manually analyse all these data. The functions of existing automated tools to process the data are still very limited and the results are still not very accurate. Therefore, researchers have turned to recruiting general citizens who are interested in helping scientific research to do the pre-processing tasks such as species tagging. Although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Therefore, this research aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we aim to investigate how to use reputation management to enhance data reliability. Reputation systems have been used to solve the uncertainty and improve data quality in many marketing and E-Commerce domains. The commercial organizations which have chosen to embrace the reputation management and implement the technology have gained many benefits. Data quality issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. However, research on reputation management in this area is relatively new. We therefore start our investigation by examining existing reputation systems in different domains. Then we design novel reputation management approaches for Citizen Science projects to categorise participants and data. We have investigated some critical elements which may influence data reliability in Citizen Science projects. These elements include personal information such as location and education and performance information such as the ability to recognise certain bird calls. The designed reputation framework is evaluated by a series of experiments involving many participants for collecting and interpreting data, in particular, environmental acoustic data. Our research in exploring the advantages of reputation management in Citizen Science (or crowdsourcing in general) will help increase awareness among organizations that are unacquainted with its potential benefits.

Utilising hospital data to inform product safety prioritisation : the application of RAPEX severity rankings in ICD-coded burn data

Background The implementation of the Australian Consumer Law in 2011 highlighted the need for better use of injury data to improve the effectiveness and responsiveness of product safety (PS) initiatives. In the PS system, resources are allocated to different priority issues using risk assessment tools. The rapid exchange of information (RAPEX) tool to prioritise hazards, developed by the European Commission, is currently being adopted in Australia. Injury data is required as a basic input to the RAPEX tool in the risk assessment process. One of the challenges in utilising injury data in the PS system is the complexity of translating detailed clinical coded data into broad categories such as those used in the RAPEX tool. Aims This study aims to translate hospital burns data into a simplified format by mapping the International Statistical Classification of Disease and Related Health Problems (Tenth Revision) Australian Modification (ICD-10-AM) burn codes into RAPEX severity rankings, using these rankings to identify priority areas in childhood product-related burns data. Methods ICD-10-AM burn codes were mapped into four levels of severity using the RAPEX guide table by assigning rankings from 1-4, in order of increasing severity. RAPEX rankings were determined by the thickness and surface area of the burn (BSA) with information extracted from the fourth character of T20-T30 codes for burn thickness, and the fourth and fifth characters of T31 codes for the BSA. Following the mapping process, secondary data analysis of 2008-2010 Queensland Hospital Admitted Patient Data Collection (QHAPDC) paediatric data was conducted to identify priority areas in product-related burns. Results The application of RAPEX rankings in QHAPDC burn data showed approximately 70% of paediatric burns in Queensland hospitals were categorised under RAPEX levels 1 and 2, 25% under RAPEX 3 and 4, with the remaining 5% unclassifiable. In the PS system, prioritisations are made to issues categorised under RAPEX levels 3 and 4. Analysis of external cause codes within these levels showed that flammable materials (for children aged 10-15yo) and hot substances (for children aged <2yo) were the most frequently identified products. Discussion and conclusions The mapping of ICD-10-AM burn codes into RAPEX rankings showed a favourable degree of compatibility between both classification systems, suggesting that ICD-10-AM coded burn data can be simplified to more effectively support PS initiatives. Additionally, the secondary data analysis showed that only 25% of all admitted burn cases in Queensland were severe enough to trigger a PS response.