The manufacturing industry is constantly looking for ways to reduce production costs and at the same time to increase productivity. Automation of common manufacturing operations is one of these methods. By automating common manufacturing operations; various machines, robots, control systems and information technologies are used to reduce the overall human input requirement (mental and physical). Recent advances in technology have made it possible to now also automate (or facilitate) the maintenance requirement of these machines and tools. Modern tools and machines, which can estimate when it will fail or when failure is imminent have obvious advantages for predictive maintenance purposes. Another function of this technology is to determine how efficiently a tool or machine operates, or what the quality of the produced goods is. Predictive maintenance can decrease manufacturing plant or machine down times – which have a positive effect on cost-savings – has gained considerable importance over the last two decades.

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The development of manufacturing systems is faced with progressively tightening time frames, along with growing requirements on planning quality and engineering accuracy. These demands result from significant cost constraints, shortening of product life-cycles, increasing number of product variants and economic needs for rapid time-to-market. Thus, an efficient production ramp-up including the commissioning as the crucial part, becomes more and more important for engineering companies to stay profitable. Virtual Commissioning (VC) is widely considered as promising method to address the challenges associated with real commissioning, but the simulation model building necessary for VC is affiliated with considerable effort and required expertise. VC of manufacturing systems has been a research topic in academia and industry for far more than a decade. Positive results are reported from large companies e.g. from the automotive industry, which are mostly utilising the complex and costly suites of tools in the context of the Digital Factory, rarely from SMEs. However, in particular also SMEs are forced to improve their engineering and commissioning processes, but suites of tools and methodologies used in large companies are not reasonably transferable to SMEs. Rationale for the rare use of VC, besides its general complexity, are a high modelling effort to build the necessary virtual plant models and a lack of availability of methodologies for systematic implementation and reasonable execution of VC. Thus, the main goal of this research is the development of a new systematic simulation study methodology as general guideline for planning, implementation and execution of VC. It is intended to be notably beneficial for engineers from SMEs, as helpful guideline for planning, implementation and execution of VC and to facilitate the substantially high modelling effort required for VC of manufacturing systems. Besides clarifying the requirements and specifying an environment for VC, the criteria to select an appropriate simulation tool have been established. The proposed modular, component based simulation model building has been split into specified procedures for “Low-level Component Modelling”, to be conducted for the components of the decomposed real manufacturing system, and subsequent “High-level Plant Modelling” of the virtual manufacturing system. The applicability of these new approaches has been validated by planning, implementing and conducting a VC for a trackbound transportation system with self-driving transport cars on passive tracks, which is the major subsystem of the manufacturing system used as test-bed at the UASA Hannover. As one main result, a novel workflow for Low-level Component Modelling has been proposed that aims for the gradual relocation of this modelling task as far as possible to the origin of components, in the end the component manufacturers should provide together with the deliverable components their mechatronic component models. This is related to a novel proposal for exchangeable mechatronic component models and an outlined possible implementation with AutomationML.

In this research, an analytical model for analyzing a production line consisting of a series of automated workstations with infinite buffers is developed. Automated workstations are assumed to have deterministic processing times, and independent exponentially distributed operating time between failures and repair times. The analytical model starts with existing results from a Markov chain model of two automated workstations in series, where analytical expressions are developed for the average number of jobs in the second workstation and its queue. This research focuses on the development of a set of linking equations that can be used to analyze larger systems using a two workstation decomposition approach. These linking equations utilize probabilities computed in the two-workstation Markov chain model to compute workstation parameters for a single workstation such that the first two moments of the inter-departure process from the two-workstation system and the single workstation are the same. Simulations of a number of different 3-workstation and 10-workstation systems were carried out employing a range of workstation utilizations and processing time coefficients of variation. The results from these simulations were compared with those calculated with the analytical model and various two-parameter GI/G/1 approximations and linking equations present in the literature. The analytical model resulted in an average absolute percentage difference of less than 5% in the systems studied, and performed much better than general two parameter G/G/1 approximations. The analytical model was also robust in ranking the queues in the order of the average number of jobs present in the queues. / Graduation date: 2004

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Due to the competitive nature of the global economy, organisations are continuously seeking ways of cutting costs and increasing efficiency to gain a competitive advantage. Field service organisations that offer after sales support seek to gain a competitive advantage through downtime minimisation. Downtime is the time between service requests made by a customer or triggered by equipment failure and the completion of the service to rectify the problem by the field service team. Researchers have identified downtime as one of the key performance indicators for field service organisations. The lack of real-time access to information and inaccuracy of information are factors which contribute to the poor management of downtime. Various technology advancements have been adopted to address some of the challenges faced by field service organisations through automation. The emergence of an Internet of Things (IoT), has brought new enhancement possibilities to various industries, for instance, the manufacturing industry. The main research question that this study aims to address is “How can an Internet of Things be used to optimise field service automation?” The main research objective was to develop and evaluate a model for the optimisation of field services using an IoT’s features and technologies. The model aims at addressing challenges associated with the inaccuracy or/and lack of real-time access to information during downtime. The model developed is the theoretical artefact of the research methodology used in this study which is the Design Science Research Methodology (DSRM). The DSRM activities were adopted to fulfil the research objectives of this research. A literature review in the field services domain was conducted to establish the problems faced by field service organisations. Several interviews were held to verify the problems of FSM identified in literature and some potential solutions. During the design and development activity of the DSRM methodology, an IoT model for FSA was designed. The model consists of:The Four Layered Architecture; The Three Phase Data Flow Process; and Definition and descriptions of IoT-based elements and functions. The model was then used to drive the design, development, and evaluation of “proof of concept” prototype, the KapCha prototype. KapCha enables the optimisation of FSA using IoT techniques and features. The implementation of a sub-component of the KapCha system, in fulfilment of the research. The implementation of KapCha was applied to the context of a smart lighting environment in the case study. A two-phase evaluation was conducted to review both the theoretical model and the KapCha prototype. The model and KapCha prototype were evaluated using the Technical and Risk efficacy evaluation strategy from the Framework for Evaluation of Design Science (FEDS). The Technical Risk and Efficacy strategy made use of formative, artificial-summative and summative-naturalistic methods of evaluation. An artificial-summative evaluation was used to evaluate the design of the model. Iterative formative evaluations were conducted during the development of the KapCha. KapCha was then placed in a real-environment conditions and a summative-naturalistic evaluation was conducted. The summative-naturalistic evaluation was used to determine the performance of KapCha under real-world conditions to evaluate the extent it addresses FSA problems identified such as real-time communication and automated fault detection.

M.Ing. / As the manufacturing environment evolved over the past century, the nature of investments in manufacturing capabilities changed dramatically. Automation can be seen as the single biggest driver of this evolution; enabling the manufacturing fraternity to develop smarter technology in order to exploit the opportunities that were created by the volatility that exist in most markets. This lead to the development of flexible manufacturing technology. Constructing a definition of manufacturing flexibility is difficult mainly due to the various views and perspectives that exist of flexibility. In short, flexibility can be defined as the ability to react ( to any change ) with little penalty in time, effort, cost or performance. These technologies that enable a manufacturing system to be flexible in a certain manner are generally difficult to justify in terms of traditional financial yardsticks. This can be contributed to the diverse benefits to be gained from these investments; and often these benefits are of a nonfinancial nature. Furthermore, when reviewing investments in flexible manufacturing technology within a process environment there appears to be an even bigger problem. The relatively fixed nature of the design output of process equipment, and the enormous quantities of capital outlay initially required to erect and commission process plants, often makes it near impossible to justify any investment that does not deliver good financial returns within the short term. Thus it becomes clear that the traditional methods of investment appraisal within the process environment have become generally unsuitable; and this call for a re-evaluation of the processes applied to guide value adding investments. This study set out to deliver a logical approach to appraising investments in manufacturing flexibility by defining a framework to be applied. The proposed framework consists of the following 4 primary steps. Firstly the strategic direction followed by the business is defined; then an analysing of the manufacturing flexibility required is performed. The third step is to evaluate the manufacturing technology available and furthermore a suitable performance measured criteria is defined to evaluate the proposed investment. This model is set within the strategic context of the manufacturing strategy of a business and thus should ensure the development of manufacturing capabilities that will ensure business growth over the medium to long term.

A methodology is developed to determine cost"effective hierarchical computer control network designs for flexible manufacturing systems. By modeling the hierarchical control system (HCS) as a resource allocation problem, an optimal hardware configuration is identified using dynamic programing. Being independent of specific computer hardware technology, the model can address present and future automated manufacturing systems. A simulation model is developed to evaluate operational dynamics of the specified system configuration, analyze HCS component performance characteristics, and evaluate hardware and software in real simulation time. The model also simulates continuous system dynamics, as found in optimal adaptive control systems. / Ph. D.

This thesis develops a framework to guide the incremental design and implementation of a Computer-Integrated Manufacturing System (CIMS). The framework is premised upon the facts that: (1) CIMS design is accomplished through a series of evaluation decisions sequenced through time and (2) evaluation is accomplished by decomposing the entire manufacturing organization into its essential activities and transactions. The effects of computerization on these activities and transactions are determined and these effects are then related to impacts on a set of selected evaluation criteria. Formal methods for benefit quantification are not included. The user of this framework is required to: (1) specify a set of relevant evaluation criteria, (2) define essential activities and transactions of their organization, and (3) derive organization-specific affect/impact relationships. The framework structures these activities for the user and suggests a series of matrices that can be used to guide the user through the steps of the framework. Use of the framework is demonstrated as various aspects of an implementation decision currently being faced by a manufacturing organization are analyzed. The implementation decision concerns whether to implement a computerized production planning and scheduling system and aspects of the decision which are considered include impacts on organizational flexibility, responsiveness, and skills. The required changes in authority relationships and the assignment of task responsibilities are also considered. Using results from this case study, the usefulness and appropriateness of the framework was assessed. Although there was no quantitative measure available, the client deemed the framework useful for analyzing and guiding their implementation decision. Suggested improvements to the framework are presented. / Master of Science

Improvement of manufacturing productivity is dependent on the successful automation of manufacturing processes, the success of which is based in turn upon the availability of information which describes the state of manufacturing operations. Acoustic Emission (AE) signals related to the cutting process and tool wear have been recently applied to monitor manufacturing processes, and various AE parameters can be used to provide process information. For example, when cutting tools become worn, AE energy generated at the interface of tool flank and work piece increases. This study is thus an experimental investigation of the AE spectrums representing AE signals energy distribution to determine the possibility of extracting useful parameters to provide on-line information about drilled-hole quality and drill-bit wear. An experiment conducted using a radial-arm drilling machine was employed to collect on-line AE drilling process spectrums, yielding eight indicator parameters. Drill wear states were measured using a machine vision system. Assessment of the drilled hole quality was based on tolerances established in Geometric Dimensioning and Tolerancing (GD&T). Correlations among drill wear, drilled-hole quality measurements, and the AE spectrum indicator parameters were examined by regression analysis. A forward-stepwise variable selection procedure was used to select the best-fit regression model for each drilled hole quality measurement associated with the set of one AE parameter raised to different powers. According to quality measurements, drilled holes were categorized as either "acceptable" or "unacceptable" holes, using cluster analysis with a group-averaging method. The usage of AE parameters to decide to which group a drilled hole belonged was also examined. From the experimental evidence, it was observed that there are strong relationships between AE parameters and drill-wear state and the quality measurements of drilled holes. AE parameters could be useful predictor variables to provide information to controller/operators to evaluate current drilling processes. Based on the status information of drill wear and the quality measurements, drilling processes can be adjusted accordingly. / Graduation date: 1997