Development of an optimum factory model for a manufacturing operation through computer simulation

Often manufacturing enterprises will maintain high levels of Work In Progress (WIP) to maximise production throughput. Using computer simulation, this thesis investigates a manufacturing facility and identifies an optimal level of WIP. At this optimum throughput is maximised, manufacturing lead times are minimised, and the WIP level is significantly reduced.

Computer simulation based parameter selection for resistance exercise

In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenologi-cal models. Scarcer yet is the application of said models in practice. We present a framework which allows resistance training practitioners to employ a recently proposed neu-romuscular model in actual training program design. The first novelty concerns the monitoring aspect of coaching. A method for extracting training performance characteristics from loosely constrained video sequences, effortlessly and with minimal human input, using computer vision is described. The extracted data is subsequently used to fit the underlying neuromuscular model. This is achieved by solving an inverse dynamics problem corresponding to a particular exercise. Lastly, a computer simulation of hypothetical training bouts, using athlete-specific capability parameters, is used to predict the effected adaptation and changes in performance. The software described here allows the practitioner to manipulate hypothetical training parameters and immediately see their effect on predicted adaptation for a specific athlete. Thus, this work presents a holistic view of the monitoring-assessment-adjustment loop.

A mathematical model of neuromuscular adaptation to resistance training and its application in a computer simulation of accommodating loads

A large corpus of data obtained by means of empirical study of neuromuscular adaptation is currently of limited use to athletes and their coaches. One of the reasons lies in the unclear direct practical utility of many individual trials. This paper introduces a mathematical model of adaptation to resistance training, which derives its elements from physiological fundamentals on the one side, and empirical findings on the other. The key element of the proposed model is what is here termed the athlete’s capability profile. This is a generalization of length and velocity dependent force production characteristics of individual muscles, to an exercise with arbitrary biomechanics. The capability profile, a two-dimensional function over the capability plane, plays the central role in the proposed model of the training-adaptation feedback loop. Together with a dynamic model of resistance the capability profile is used in the model’s predictive stage when exercise performance is simulated using a numerical approximation of differential equations of motion. Simulation results are used to infer the adaptational stimulus, which manifests itself through a fed back modification of the capability profile. It is shown how empirical evidence of exercise specificity can be formulated mathematically and integrated in this framework. A detailed description of the proposed model is followed by examples of its application—new insights into the effects of accommodating loading for powerlifting are demonstrated. This is followed by a discussion of the limitations of the proposed model and an overview of avenues for future work.

Simulation of dynamic recrystallization using random grid cellular automata

Computer simulation is a powerful tool to predict microstructure and its evolution in dynamic and post-dynamic recrystallization. CAFE proposed as an appropriate approach by combining finite element (FE) method and cellular automata (CA) for recrystallization simulation. In the current study, a random grid cellular automaton (CA), as micro-scale model, based on finite element (FE), as macro-scale method, has been used to study initial and evolving microstructural features; including nuclei densities, dislocation densities, grain size and grain boundary movement during dynamic recrystallization in a C-Mn steel. An optimized relation has been established between mechanical variables and evolving microstructure features during recrystallization and grain growth. In this model, the microstructure is defined as cells located within grains and grain boundaries while dislocations are randomly dispersed throughout microstructure. Changes of dislocation density during deformation are described considering hardening, recovery and recrystallization. Recrystallization is assumed to initiate near grain boundaries and nucleation rate was considered constant (site-saturated condition). The model produced a mathematical formulation which captured the initial and evolving microstructural entities and linked their effects to measurable macroscopic variables (e.g. stress).

Modelling and simulation for manufacturing systems : challenges and benefits

Computer modelling and simulation is an indispensable tool of the information age, used extensively in design, analysis, operations, decision-making, optimization, and education and training. Manufacturing, production and design relies upon simulation to develop efficient production systems and factories that produce quality products. Computer simulation allows scientists and engineers to understand and predict three-dimensional and time-dependent phenomena in science and engineering discipline. This talk will focus on challenges associated with modelling and simulation in the manufacturing sector and through a number of case studies highlights the benefits gained through the use of such technologies.

Modeling and simulation for large scale production systems

Computer modeling and simulation provide a foundation upon which industrial processes and systems can be transformed and innovation dramatically accelerated. Computer modeling and simulation is also an indispensable tool of the information age, used extensively in design, analysis, operations, decision-making, optimization, and education and training. Manufacturing, production and design relies upon simulation to develop efficient production systems and factories that produce quality products. Simulation in industry has yet to meet its full potential. The development of models is very time consuming, particularly for geometries of complex engineering systems such as manufacturing plants, automobiles, aircraft and ships. Computer simulation allows scientists and engineers to understand and predict three-dimensional and time-dependent phenomena in science and engineering discipline. This talk will focus on challenges associated with modeling and simulation in the manufacturing sector and through a number of case studies highlight the benefits gained through the use of such technologies.

Managing realtime information flow between distributed discrete event simulation models

Investigates the creation of a method for the connection and communication of commercial off the shelf discrete-event simulation packages for simulation models of manufacturing systems. Through this research a method to connect different commercial off the shelf discrete-event simulation packages was successfully developed facilitating parallel development of models and the creation of extremely large models.

Simulation-based learning for control of complex conveyor networks

This research investigated the problem of path planning in complex conveyor networks. A reinforcement learning approach was applied to derive a control strategy for routing traffic. The derived strategy was verified in real world systems and was found to improve network performance by prioritising traffic flows and balancing network load.

Ethical trust and social moral norms simulation : a bio-inspired agent-based modelling approach

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.

A robust and reproducible procedure for cross-linking thermoset polymers using molecular simulation

Molecular simulation can provide valuable guidance in establishing clear links between structure and function to enable the design of new polymer-based materials. However, molecular simulation of thermoset polymers in particular, such as epoxies, present specific challenges, chiefly in the credible preparation of polymerised samples. Despite this need, a comprehensive, reproducible and robust process for accomplishing this using molecular simulation is still lacking. Here, we introduce a clear and reproducible cross-linking protocol to reliably generate three dimensional epoxy cross-linked polymer structures for use in molecular simulations. This protocol is sufficiently detailed to allow complete reproduction of our results, and is applicable to any general thermoset polymer. Amongst our developments, key features include a reproducible procedure for calculation of partial atomic charges, a reliable process for generating and validating an equilibrated liquid precursor mixture, and establishment of a novel, robust and reproducible protocol for generating the three-dimensional cross-linked solid polymer. We use these structures as input to subsequent molecular dynamics simulations to calculate a range thermo-mechanical properties, which compare favourably with experimental data. Our general protocol provides a benchmark for the process of simulating epoxy polymers, and can be readily translated to prepare and model epoxy samples that are dynamically cross-linked in the presence of surfaces and nanostructures.

Joint space-time trellis decoding and channel estimation in correlated fading channels

This letter addresses the issue of joint space-time trellis decoding and channel estimation in time-varying fading channels that are spatially and temporally correlated. A recursive space-time receiver which incorporates per-survivor processing (PSP) and Kalman filtering into the Viterbi algorithm is proposed. This approach generalizes existing work to the correlated fading channel case. The channel time-evolution is modeled by a multichannel autoregressive process, and a bank of Kalman filters is used to track the channel variations. Computer simulation results show that a performance close to the maximum likelihood receiver with perfect channel state information (CSI) can be obtained. The effects of the spatial correlation on the performance of a receiver that assumes independent fading channels are examined.