Throughput and Yield Improvement for a Continuous Discrete-Product Manufacturing System

A seam-welded steel pipe manufacturing process has mainly four distinct major design and/or operational problems dealing with buffer inventory, cutting tools, pipe sizing and inspection-rework facility. The general objective of this research is to optimally solve these four important problems to improve the throughput and yield of the system at a minimum cost. The first problem of this research finds the optimal buffer capacity of steel strip coils to minimize the maintenance and downtime related costs. The total cost function for this coil feeding system is formulated as a constrained non-linear programming (NLP) problem which is solved with a search algorithm. The second problem aims at finding the optimal tool magazine reload timing, magazine size and the order quantity for the cutting tools. This tool magazine system is formulated as a mixed-integer NLP problem which is solved for minimizing the total cost. The third problem deals with different type of manufacturing defects. The profit function of this problem forms a binary integer NLP problem which involves multiple integrals with several exponential and discrete functions. An exhaustive search method is employed to find the optimum strategy for dealing with the defects and pipe sizing. The fourth problem pertains to the number of servers and floor space allocations for the off-line inspection-rework facility. The total cost function forms an integer NLP structure, which is minimized with a customized search algorithm. In order to judge the impact of the above-mentioned problems, an overall equipment effectiveness (OEE) measure, coined as monetary loss based regression (MLBR) method, is also developed as the fifth problem to assess the performance of the entire manufacturing system. Finally, a numerical simulation of the entire process is conducted to illustrate the applications of the optimum parameters setting and to evaluate the overall effectiveness of the simulated system. The successful improvement of the simulated system supports this research to be implemented in a real manufacturing setup. Different pathways shown here for improving the throughput and yield of industrial systems reflect not only to the improvement of methodologies and techniques but also to the advancement of new technology and national economy

Automated Productivity Models for Earthmoving Operations

Earthmoving operations have significant importance, particularly for civil infrastructure projects. The performance of these operations should be monitored regularly to support timely recognition of undesirable productivity variances. Although productivity assessment occupies high importance in earthmoving operations, it does not provide sufficient information to assist project managers in taking the necessary actions in a timely manner. Assessment only is not capable of identifying problems encountered in these operations and their causes. Many studies recognized conditions and related factors that influence productivity of earthmoving operations. These conditions are mainly project-specific and vary from one project to another. Most of reported work in the literature focused on assessment rather than analysis of productivity. This study presents three integrated models that automate productivity measurement and analysis processes with capabilities to detect different adverse conditions that influence the productivity of earthmoving operations. The models exploit innovations in wireless and remote sensing technologies to provide project managers, contractors, and decision makers with a near-real-time automated productivity measurement and analysis. The developed models account for various uncertainties associated with earthmoving projects. The first model introduces a fuzzy-based standardization for customizing the configuration of onsite data acquisition systems for earthmoving operations. While the second model consists of two interrelated modules. The first is a customized automated data acquisition module, where a variety of sensors, smart boards, and microcontrollers are used to automate the data acquisition process. This module encompasses onsite fixed unit and a set of portable units attached to each truck used in the earthmoving fleet. The fixed unit is a communication gateway (Meshlium®), which has integrated MySQL database with data processing capabilities. Each mobile unit consists of a microcontroller equipped with a smart board that hosts a GPS module as well as a number of sensors such as accelerometer, temperature and humidity sensors, load cell and automated weather station. The second is a productivity measurement and analysis module, which processes and analyzes the data collected automatically in the first module. It automates the analysis process using data mining and machine learning techniques; providing a near-real-time web-based visualized representation of measurement and analysis outcomes. Artificial Neural Network (ANN) was used to model productivity losses due to the existence of different influencing conditions. Laboratory and field work was conducted in the development and validation processes of the developed models. The work encompassed field and scaled laboratory experiments. The laboratory experiments were conducted in an open to sky terrace to allow for a reliable access to GPS satellites. Also, to make a direct connection between the data communication gateway (Meshlium®), initially installed on a PC computer to observe the received data latency. The laboratory experiments unitized 1:24 scaled loader and dumping truck to simulate loading, hauling and dumping operations. The truck was instrumented with the microcontroller equipped with an accelerometer, GPS module, load cell, and soil water content sensor. Thirty simulated earthmoving cycles were conducted using the scaled equipment. The collected data was recorded in a micro secure digital (SD) card in a comma separated value (CSV) format. The field work was carried out in the city of Saint-Laurent, Montreal, Quebec, Canada using a passenger vehicle to mimic the hauling truck operational modes. Fifteen Field simulated earthmoving cycles were performed. In this work two roads with different surface conditions, but of equal length (1150 m) represented the haul and return roads. These two roads were selected to validate the developed road condition analysis algorithm and to study the model’s capability in determining the consequences of adverse road conditions on the haul and return durations and thus on the tuck and fleet productivity. The data collected from the lab experiments and field work was used as input for the developed model. The developed model has shown perfect recognition of the state of truck throughout the fifteen field simulated earthmoving cycles. The developed road condition analysis algorithm has demonstrated an accuracy of 83.3% and 82.6% in recognizing road bumps and potholes, respectively. Also, the results indicated tiny variances in measuring the durations compared with actual durations using time laps displayed on a smart cell telephone; with an average invalidity percentage AIP% of 1.89 % and 1.33% for the joint hauling and return duration and total cycle duration, respectively

Method and system for diagnostics of apparatus

Proposed is a method, implemented in software, for estimating fault state of an apparatus outfitted with sensors. At each execution period the method processes sensor data from the apparatus to obtain a set of parity parameters, which are further used for estimating fault state. The estimation method formulates a convex optimization problem for each fault hypothesis and employs a convex solver to compute fault parameter estimates and fault likelihoods for each fault hypothesis. The highest likelihoods and corresponding parameter estimates are transmitted to a display device or an automated decision and control system. The obtained accurate estimate of fault state can be used to improve safety, performance, or maintenance processes for the apparatus

Integrating the finite element method and genetic algorithms to solve structural damage detection and design optimisation problems

This thesis documents fundamental new research in to a specific application of structural box-section beams, for which weight reduction is highly desirable. It is proposed and demonstrated that the weight of these beams can be significantly reduced by using advanced, laminated fibre-reinforced composites in place of steel. Of the many issues raised during this investigation two, of particular importance, are considered in detail; (a) the detection and quantification of damage in composite structures and (b) the optimisation of laminate design to maximise the performance of loaded composite structuress ubject to given constraints. It is demonstrated that both these issues can be formulated and solved as optimisation problems using the finite element method, in which an appropriate objective function is minimised (or maximised). In case (a) the difference in static response obtained from a loaded structure containing damage and an equivalent mathematical model of the structure is minimised by iteratively updating the model. This reveals the damage within the model and subsequently allows the residual properties of the damaged structure to be quantified. Within the scope of this work is the ability to resolve damage, that consists of either penny-shaped sub-surface flaws or tearing damage of box-section beams from surface experimental data. In case (b) an objective function is formulated in terms of a given structural response, or combination of responses that is optimised in order to return an optimal structure, rather than just a satisfactory structure. For the solution of these optimisation problems a novel software tool, based on the integration of genetic algorithms and a commercially available finite element (FE) package, has been developed. A particular advantage of the described method is its applicability to a wide range of engineering problems. The tool is described and its effectiveness demonstrated with reference to two inverse damage detection and quantification problems and one laminate design optimisation problem. The tool allows the full suite of functions within the FE software to be used to solve non-convex optimisation problems, formulated in terms of both discrete and continuous variables, without explicitly stating the form of the stiffness matrix. Furthermore, a priori knowledge about the problem may be readily incorporated in to the method

Remote sensing applications for the assessment of the geomorphic response of fluvial systems to the Holocene Climate Changes

The general goal of this thesis is the identification and description of the geomorphological responses of the fluvial system to the Holocene Climate Changes, proposing a multi-sensor remote sensing approach. In particular, the specific aim of this work is the improvement of the present knowledge on the Holocene and historical morphodynamics of the Lower Mesopotamian waterscape, especially on the paleo-hydrology of the ancient Tigris-Euphrates fluvial system, focusing on the specific process in the dynamics of the waterscapes which plays a key role in the drainage network evolution in lowland areas. Crevasse splays represent significant geomorphological features for understanding the fluvial morphodynamics in lowland areas where avulsion processes prevail. The southern Mesopotamian Plain is the area where the ancient State of Lagash developed between the prehistoric Ubaid Period (c. 5200 - c. 3500 BC) and the late Parthian era (247 BC - AD 244), representing an ideal case study, where the Italian Archaeological Mission has been recently carried on extensive field-works at Tell Zurghul archaeological site. Here, an interdisciplinary approach, combining field surveys and geomorphological mapping through remote sensing techniques, has been applied for analyzing the function and role of the waterscape on the early civilization. Indeed, the geomorphological analysis through a remote sensing approach and the archaeological surveys are both essential for the reconstruction of a complex environmental system, where landforms due to different morphogenetic processes occur, related to the presence of a wide fluvial-deltaic paleo-system and early human societies. The main aim of the focus on this archaeological site is to contribute to the reconstruction of the surrounding waterscape and know more about waterscape-human interactions during the Holocene. The question of human-waterscape relationship worldwide has been and still is a central topic in geomorphological, environmental, and archaeological research. During the Holocene, the Tigris-Euphrates river system, in the lower sector of the Mesopotamian Plain (Iraq), has been characterized by complex morphodynamics in response to both climate fluctuations and extensive construction of artificial canals, dug since the first human settlements belonging to the Early River Valley Civilizations. The Lower Mesopotamian Plain (LMP) coincides with the southern Tigris and Euphrates deltaic plain, developed starting since the mid Holocene. During the early Holocene, the sea-level rise caused a general and rapid northward shifting of the Persian Gulf shoreline: the maximum marine ingression reached the area where the present towns of Nasiriyah and Al-Amara are located about 6000 yrs BP; after which the widespread progradation of the Tigris and Euphrates delta system accounted for the southward shoreline regression up to the present position. The development of a typical bird-foot delta guaranteed an amount of water indispensable for agriculture, cattle, settlements, and transport. Indeed, the high mobility of the channels and the frequent occurrence of avulsion processes (i.e., levees break and related crevasse splays formation) are the main features typically connected to a multi-channel system, guarantying the water supply through seasonal floods. In the area, the water management during the mid Holocene, digging an extensive network of canals and building several dams, can either improve the socio-economic conditions of a settlement or cause the end of another one. Within a wide floodplain characterized by very low elevation ranges such as the LMP, a remote sensing, multi-sensor approach is a suitable method for identifying the main geomorphological features related to the fluvial avulsion processes, describing the associated morphogenetic processes. Optical and multispectral Landsat 8 satellite images have been processed for computing NDVI and Clay Ratio indices, as well as to extract the Regions of Interest (ROIs) focused on the main features that made up a crevasse splay (i.e., crevasse channel, crevasse levee and crevasse deposit). The spectral signatures from active and abandoned crevasse splays have been extracted and compared among them, adopting four different methods of Supervised Classification. The analysis of the crevasse splays has been integrated with the investigation of the micro-topography leading to recognize the crevasse channels and levees, the upward convexity of the crevasse deposits and the distal or proximal position of the parent channel; the re-classification of different DEM sources, such as the optical AW3D30 and GDEM2 datasets with ground resolution of 1 arcsec (i.e., 30 m cell-1), leads to highlighting the “above-floodplain” topographic configuration of these landforms. The analysis here performed leads to investigating the entire Lower Mesopotamian Plain through both large and medium scale geomorphological investigation, identifying active and abandoned channels, discerning between active and abandoned avulsion processes and distinguishing crevasse channels, levees, and deposits. In like manner, human features are recognized, allowing the evaluation of human-environmental interactions

Process planning for robotic wire ARC additive manufacturing

Robotic Wire Arc Additive Manufacturing (WAAM) refers to a class of additive manufacturing processes that builds parts from 3D CAD models by joining materials layerupon- layer, as opposed to conventional subtractive manufacturing technologies. Over the past half century, a significant amount of work has been done to develop the capability to produce parts from weld deposits through the additive approach. However, a fully automated CAD-topart additive manufacturing (AM) system that incorporates an arc welding process has yet to be developed. The missing link is an automated process planning methodology that can generate robotic welding paths directly from CAD models based on various process models. The development of such a highly integrated process planning method for WAAM is the focus of this thesis

Essays in development economics and public finance

This dissertation studies a range of topics in development economics and public finance. The first two chapters contain empirical studies on India addressing the impact of financial development on poverty and informality. Using time and state-level variation across Indian states, the first study examines the effect of financial liberalization in 1991on poverty and investigates the underlying mechanisms. The second study examines the effect of financial deepening and bank outreach on informality using micro data of Indian manufacturing sector. The next two chapters investigate the optimal government policy to reduce tax evasion in a value-added tax (VAT) system. Chapter three addresses the problem of misreporting by registered traders in the VAT. The last chapter models the role of inter-sectoral linkages on tax evasion of informal firms in an input-output framework and justifies the results using Indian data

Optimisation of welding parameters to mitigate the effect of residual stress on the fatigue life of nozzle–shell welded joints in cylindrical pressure vessels.

Doctoral Degree. University of KwaZulu-Natal, Durban.The process of welding steel structures inadvertently causes residual stress as a result of thermal cycles that the material is subjected to. These welding-induced residual stresses have been shown to be responsible for a number of catastrophic failures in critical infrastructure installations such as pressure vessels, ship’s hulls, steel roof structures, and others. The present study examines the relationship between welding input parameters and the resultant residual stress, fatigue properties, weld bead geometry and mechanical properties of welded carbon steel pressure vessels. The study focuses on circumferential nozzle-to-shell welds, which have not been studied to this extent until now. A hybrid methodology including experimentation, numerical analysis, and mathematical modelling is employed to map out the relationship between welding input parameters and the output weld characteristics in order to further optimize the input parameters to produce an optimal welded joint whose stress and fatigue characteristics enhance service life of the welded structure. The results of a series of experiments performed show that the mechanical properties such as hardness are significantly affected by the welding process parameters and thereby affect the service life of a welded pressure vessel. The weld geometry is also affected by the input parameters of the welding process such that bead width and bead depth will vary depending on the parametric combination of input variables. The fatigue properties of a welded pressure vessel structure are affected by the residual stress conditions of the structure. The fractional factorial design technique shows that the welding current (I) and voltage (V) are statistically significant controlling parameters in the welding process. The results of the neutron diffraction (ND) tests reveal that there is a high concentration of residual stresses close to the weld centre-line. These stresses subside with increasing distance from the centre-line. The resultant hoop residual stress distribution shows that the hoop stresses are highly tensile close to the weld centre-line, decrease in magnitude as the distance from the weld centre-line increases, then decrease back to zero before changing direction to compressive further away from the weld centre-line. The hoop stress distribution profile on the flange side is similar to that of the pipe side around the circumferential weld, and the residual stress peak values are equal to or higher than the yield strength of the filler material. The weld specimens failed at the weld toe where the hoop stress was generally highly tensile in most of the welded specimens. The multiobjective genetic algorithm is successfully used to produce a set of optimal solutions that are in agreement with values obtained during experiments. The 3D finite element model produced using MSC Marc software is generally comparable to physical experimentation. The results obtained in the present study are in agreement with similar studies reported in the literature

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

Advances in System Identification and Stochastic Optimization

This work studies the framework of systems with subsystems, which has numerous practical applications, including system reliability estimation, sensor networks, and object detection. Consider a stochastic system composed of multiple subsystems, where the outputs are distributed according to many of the most common distributions, such as Gaussian, exponential and multinomial. In Chapter 1, we aim to identify the parameters of the system based on the structural knowledge of the system and the integration of data independently collected from multiple sources. Using the principles of maximum likelihood estimation, we provide the formal conditions for the convergence of the estimates to the true full system and subsystem parameters. The asymptotic normalities for the estimates and their connections to Fisher information matrices are also established, which are useful in providing the asymptotic or finite-sample confidence bounds. The maximum likelihood approach is then connected to general stochastic optimization via the recursive least squares estimation in Chapter 2. For stochastic optimization, we consider minimizing a loss function with only noisy function measurements and propose two general-purpose algorithms. In Chapter 3, the mixed simultaneous perturbation stochastic approximation (MSPSA) is introduced, which is designed for mixed variable (mixture of continuous and discrete variables) problems. The proposed MSPSA bridges the gap of dealing with mixed variables in the SPSA family, and unifies the framework of simultaneous perturbation as both the standard SPSA and discrete SPSA can now be deemed as two special cases of MSPSA. The almost sure convergence and rate of convergence of the MSPSA iterates are also derived. The convergence results reveal that the finite-sample bound of MSPSA is identical to discrete SPSA when the problem contains only discrete variables, and the asymptotic bound of MSPSA has the same order of magnitude as SPSA when the problem contains only continuous variables. In Chapter 4, the complex-step SPSA (CS-SPSA) is introduced, which utilizes the complex-valued perturbations to improve the efficiency of the standard SPSA. We prove that the CS-SPSA iterates converge almost surely to the optimum and achieve an accelerated convergence rate, which is faster than the standard convergence rate in derivative-free stochastic optimization algorithms