Application of Artificial Intelligence (AI) methods for designing and analysis of Reconfigurable Cellular Manufacturing System (RCMS)

This work focuses on the design and control of a novel hybrId manufacturing system: Reconfigurable Cellular Manufacturing System (RCMS) by using Artificial Intelligence (AI) approach. It is hybrid as it combines the advantages of Cellular Manufacturing System (CMS) and Reconfigurable Manufacturing System (RMS). In addition to inheriting desirable properties from CMS and RMS, RCMS provides additional benefits including flexibility and the ability to respond to changing products, product mix and market conditions during its useful life, avoiding premature obsolescence of the manufacturing system. The emphasis of this research is the formation of Reconfigurable Manufacturing Cell (RMC) which is the dynamic and logical clustering of some manufacturing resources, driven by specific customer orders, aiming at optimally fulfilling customers' orders along with other RMCs in the RCMS

Reconfiguring process plans: A mathematical programming approach

Increased global competition and frequent unpredictable market changes are current challenges facing manufacturing enterprises. Unpredictable changes of part design and engineering specifications trigger frequent and costly changes in process plans, which often require changes in the functionality and design of the manufacturing system. Process planning is a key logical enabler that should be further developed to cope with the changes encountered at the system level as well as to support the new manufacturing paradigms and continuously evolving products. Retrieval-based process planning predicated on rigid pre-defined boundaries of part families, does not satisfactorily support this changeable manufacturing environment. Since purely generative process planning systems are not yet a reality, a sequential hybrid approach at the macro-level has been proposed. Initially the master plan information of the part family\u27s composite part is retrieved, then modeling tools and algorithms are applied to arrive at the process plan of the new part, the definition of which does not necessarily lie entirely within the boundary of its original part family. Two distinct generative methods, namely Reconfigurable Process Planning (RPP) and Process Re-Planning were developed and compared. For RPP, a genuine reconfiguration of process plans to optimize the scope, extent and cost of reconfiguration is achieved using a novel 0-1 integer-programming model. Mathematical programming and formulation is proposed, for the first time, to reconfigure process plans to account for changes in parts\u27 features beyond the scope of the original product family. The computational time complexity of RPP is advantageously polynomial compared with the exponentially growing time complexity of its classical counterparts. As for Process Re-Planning, a novel adaptation of the Quadratic Assignment Problem (QAP) formulation has been developed, where machining features are assigned positions in one-dimensional space. A linearization of the quadratic model was performed. The proposed model cures the conceptual flaws in the classical Traveling Salesperson Problem; it also overcomes the complexity of the sub-tour elimination constraints and, for the first time, mathematically formulates the precedence constraints, which are a comer stone of the process planning problem. The developed methods, their limitations and merits are conceptually and computationally, analyzed, compared and validated using detailed industrial case studies. A reconfiguration metric on the part design level is suggested to capture the logical extent and implications of design changes on the product level; equally, on the process planning level a new criterion is introduced to evaluate and quantify impact of process plans reconfiguration on downstream shop floor activities. GAMS algebraic modeling language, its SBB mixed integer nonlinear programming solver, CPLEX solvers and Matlab are used. The presented innovative new concepts and novel formulations represent significant contributions to knowledge in the field of process planning. Their effectiveness and applicability were validated in different domains

Aggregate Cost Model for Scalability in Manufacturing Systems

Manufacturing continues to face escalated cost challenges on a global scale. To gain a competitive advantage among their rivals, manufacturing firms continuously strive to lower their manufacturing costs than their competitors. This dissertation introduces mathematical optimization model based on an Activity-Based Costing (ABC) method, which considers the relationship between hourly rates and annual hours on each machine/workcentre. Several constraints are considered in the proposed models, such as the cost of reconfiguration, capacity, available machining hours, a decision on facility expansion and a cost-benefit analysis on industry 4.0 implementation. The model outputs are the optimum hourly rates, deciding which jobs to accept or reject, and determining reconfiguration\u27s financial feasibility. Reconfiguration in this dissertation describes system-level reconfiguration (investing in additional equipment/machinery) and/or machine-level reconfiguration (extra module to a piece of existing equipment) as well as factory-level (in terms of expanding additional factory segments to the existing facility). The model will be applied to a real-life case study of a global original equipment manufacturer (OEM) of machinery. The mathematical models proposed in this dissertation are developed based on a multinational hydraulic-press manufacturing company. The company owns a local machine shop (one of the sister companies in North America) for building hydraulic presses meant to be delivered to companies producing engineered wood products (such as OSB (oriented Strand Board), PB (Particle Board), and MDF Board (Medium-Density Fibre) …etc.). The sister company in North America occupies a footprint of 5,000 meters squared with a number of capabilities such as machining (turning and machining centres, welding, assembly, material handling…etc.). Several aspects of the model proposed in this dissertation had been implemented in the company such as the bi-directional relationship between total hours and hourly rates which assisted the company in gaining more jobs and projects. In addition, connectivity between strategic suppliers and company branched has been established (enabler of Industry 4.0). The proposed model\u27s novelty incorporates the bi-directional relationship between hourly rates and annual hours in each workcentre. It provides a managerial decision-making tool for the investment level required to pursue new business and gaining a competitive advantage over rivals. Furthermore, a cost-benefit analysis is performed on the implementation of Industry 4.0. The primary aspect considered in industry 4.0 is Information Communication Technology (ICT) infrastructure with strategic suppliers to intensify interconnection between the manufacturing firm and the strategic suppliers. This research\u27s significance is focused on cost analysis and provides managers in manufacturing facilities with the required decision-making tools to decide on orders to accept or decline, as well as investing in additional production equipment, facility expansion, as well as Industry 4.0. In addition, this research will also help manufacturing companies achieve a competitive edge among rivals by reducing hourly rates within their facility. Furthermore, the implementation of the model reduced hourly rates for workcentres by up to 25% as a result of accepting more jobs (and accordingly, machining hours) on the available workcentres, and hence, reducing the hourly rates. This implementation has helped the company gain a competitive advantage among rivals since pricing of products submitted to customer was reduced. Additional benefits and significance are (1) providing manufacturing companies with a method to quantify the decision-making process for right-sizing their manufacturing space, (2) the ability to justify growing a scalable system (machine level, system-level and factory level) using costing (not customer demand), (3) expanding market share and, (4) reducing operational cost and allowing companies a numerical model to justify scaling the manufacturing system

Evaluation of machining systems from a complexity and cost perspectives

Manufacturing systems, specifically machining, are typically designed as either dedicated or flexible; representing two very different paradigms. Measures for manufacturing flexibility have been proposed; generally, according to behaviour of system or product mix. Attempts have also been made to relate flexibility to subsequent costs. In this thesis, System Design is presented as a property of inherent attributes determined at the design stage. This provides the \u27Flexibility Level\u27 and its measurement is based on physical-functional attributes. Hence, System Design is viewed as a continuous quality, which describes both the level of flexibility and/or dedicated nature of a system. This metric is related to cost in a model which describes system design in its entirety; including manufacturing complexity in relation to cost as a tool to minimize manufacturing costs. Consequently, system behaviour is investigated given alternate manufacturing conditions such as varying product mix and production volume requirements. Industrial examples are used

Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

An on-demand fixture manufacturing cell for mass customisation production systems.

Master of Science in Engineering. University of KwaZulu-Natal, Durban, 2017.Increased demand for customised products has given rise to the research of mass customisation production systems. Customised products exhibit geometric differences that render the use of standard fixtures impractical. Fixtures must be configured or custom-manufactured according to the unique requirements of each product. Reconfigurable modular fixtures have emerged as a cost-effective solution to this problem. Customised fixtures must be made available to a mass customisation production system as rapidly as parts are manufactured. Scheduling the creation/modification of these fixtures must now be treated together with the production scheduling of parts on machines. Scheduling and optimisation of such a problem in this context was found to be a unique avenue of research. An on-demand Fixture Manufacturing Cell (FxMC) that resides within a mass customisation production system was developed. This allowed fixtures to be created or reconfigured on-demand in a cellular manufacturing environment, according to the scheduling of the customised parts to be processed. The concept required the research and development of such a cell, together with the optimisation modelling and simulation of this cell in an appropriate manufacturing environment. The research included the conceptualisation of a fixture manufacturing cell in a mass customisation production system. A proof-of-concept of the cell was assembled and automated in the laboratory. A three-stage optimisation method was developed to model and optimise the scheduling of the cell in the manufacturing environment. This included clustering of parts to fixtures; optimal scheduling of those parts on those fixtures; and a Mixed Integer Linear Programming (MILP) model to optimally synchronise the fixture manufacturing cell with the part processing cell. A heuristic was developed to solve the MILP problem much faster and for much larger problem sizes – producing good, feasible solutions. These problems were modelled and tested in MATLAB®. The cell was simulated and tested in AnyLogic®. The research topic is beneficial to mass customisation production systems, where the use of reconfigurable modular fixtures in the manufacturing process cannot be optimised with conventional scheduling approaches. The results showed that the model optimally minimised the total idle time of the production schedule; the heuristic also provided good, feasible solutions to those problems. The concept of the on-demand fixture manufacturing cell was found to be capable of facilitating the manufacture of customised products

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Dynamically and partially reconfigurable hardware architectures for high performance microarray bioinformatics data analysis

The field of Bioinformatics and Computational Biology (BCB) is a multidisciplinary field that has emerged due to the computational demands of current state-of-the-art biotechnology. BCB deals with the storage, organization, retrieval, and analysis of biological datasets, which have grown in size and complexity in recent years especially after the completion of the human genome project. The advent of Microarray technology in the 1990s has resulted in the new concept of high throughput experiment, which is a biotechnology that measures the gene expression profiles of thousands of genes simultaneously. As such, Microarray requires high computational power to extract the biological relevance from its high dimensional data. Current general purpose processors (GPPs) has been unable to keep-up with the increasing computational demands of Microarrays and reached a limit in terms of clock speed. Consequently, Field Programmable Gate Arrays (FPGAs) have been proposed as a low power viable solution to overcome the computational limitations of GPPs and other methods. The research presented in this thesis harnesses current state-of-the-art FPGAs and tools to accelerate some of the most widely used data mining methods used for the analysis of Microarray data in an effort to investigate the viability of the technology as an efficient, low power, and economic solution for the analysis of Microarray data. Three widely used methods have been selected for the FPGA implementations: one is the un-supervised Kmeans clustering algorithm, while the other two are supervised classification methods, namely, the K-Nearest Neighbour (K-NN) and Support Vector Machines (SVM). These methods are thought to benefit from parallel implementation. This thesis presents detailed designs and implementations of these three BCB applications on FPGA captured in Verilog HDL, whose performance are compared with equivalent implementations running on GPPs. In addition to acceleration, the benefits of current dynamic partial reconfiguration (DPR) capability of modern Xilinx’ FPGAs are investigated with reference to the aforementioned data mining methods. Implementing K-means clustering on FPGA using non-DPR design flow has outperformed equivalent implementations in GPP and GPU in terms of speed-up by two orders and one order of magnitude, respectively; while being eight times more power efficient than GPP and four times more than a GPU implementation. As for the energy efficiency, the FPGA implementation was 615 times more energy efficient than GPPs, and 31 times more than GPUs. Over and above, the FPGA implementation outperformed the GPP and GPU implementations in terms of speed-up as the dimensionality of the Microarray data increases. Additionally, the DPR implementations of the K-means clustering have shown speed-up in partial reconfiguration time of ~5x and 17x over full chip reconfiguration for single-core and eight-core implementations, respectively. Two architectures of the K-NN classifier have been implemented on FPGA, namely, A1 and A2. The K-NN implementation based on A1 architecture achieved a speed-up of ~76x over an equivalent GPP implementation whereas the A2 architecture achieved ~68x speedup. Furthermore, the FPGA implementation outperformed the equivalent GPP implementation when the dimensionality of data was increased. In addition, The DPR implementations of the K-NN classifier have achieved speed-ups in reconfiguration time between ~4x to 10x over full chip reconfiguration when reconfiguring portion of the classifier or the complete classifier. Similar to K-NN, two architectures of the SVM classifier were implemented on FPGA whereby the former outperformed an equivalent GPP implementation by ~61x and the latter by ~49x. As for the DPR implementation of the SVM classifier, it has shown a speed-up of ~8x in reconfiguration time when reconfiguring the complete core or when exchanging it with a K-NN core forming a multi-classifier. The aforementioned implementations clearly show FPGAs to be an efficacious, efficient and economic solution for bioinformatics Microarrays data analysis

Mass Production Processes

It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge, hard experience, and the required related tools as well. The use of modern methods and techniques to produce a large quantity of products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, this book aims to reflect on the advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, it focuses on mass production processes designed according to Industry 4.0 considering different kinds of quality and improvement works in mass production systems for high productive and sustainable manufacturing. This book may be interesting to researchers, industrial employees, or any other partners who work for better quality manufacturing at any stage of the mass production processes