Plug Force Monitoring for the Control and Optimisation of the Thermoforming Process

Thermoforming processes generally employ sheet temperature monitoring as the primary means of process control. In this paper the development of an alternative system that monitors plug force is described. Tests using a prototype device have shown that the force record over a forming cycle creates a unique map of the process operation. Key process features such as the sheet modulus, sheet sag and the timing of the process stages may be readily observed, and the effects of changes in all of the major processing parameters are easily distinguished. Continuous, cycle-to-cycle tests show that the output is consistent and repeatable over a longer time frame, providing the opportunity for development of an on-line process control system. Further testing of the system is proposed.

Process Modelling for Control of Product Wall Thickness in Thermoforming

The present paper describes the results of an investigation into the modelling of plug assisted thermoforming. The objective of this work was to improve the finite element modelling of thermoforming through an enhanced understanding of the physical elements underlying the process. Experiments were carried out to measure the effects on output of changes in major parameters and simultaneously simple finite element models were constructed. The experimental results show that the process creates conflicting and interrelated contact friction and heat transfer effects that largely dictate the final wall thickness distribution. From the simulation work it was demonstrated that a high coefficient of friction and no heat transfer can give a good approximation of the actual wall thickness distribution. However, when conduction was added to the model the results for lower friction values were greatly improved. It was concluded that further work is necessary to provide realistic measurements and models for contact effects in thermoforming.

Enhancing process insight in polymer extrusion by grey box modelling

Due to the complexity and inherent instability in polymer extrusion there is a need for process models which can be run on-line to optimise settings and control disturbances. First-principle models demand computationally intensive solution, while ‘black box’ models lack generalisation ability and physical process insight. This work examines a novel ‘grey box’ modelling technique which incorporates both prior physical knowledge and empirical data in generating intuitive models of the process. The models can be related to the underlying physical mechanisms in the extruder and have been shown to capture unpredictable effects of the operating conditions on process instability. Furthermore, model parameters can be related to material properties available from laboratory analysis and as such, lend themselves to re-tuning for different materials without extensive remodelling work.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

The effect of freeze-drying parameters on the cure characteristics of freeze-dried BSA-loaded silicone elastomer

To formulate therapeutic proteins into polymeric devices the protein is typically in the solid state, which can be achieved by the process of freeze-drying. However, freeze-drying not only risks denaturing the protein but it can adversely affect the cure characteristics of protein-loaded silicone elastomers. This study demonstrates that a variation in the parameters of the freeze-dryer can significantly affect the residual moisture content of freeze-dried BSA, which in turn has an effect on the bulk density and flow properties of the BSA. The bulk density and flow properties of the BSA subsequently affect the cure characteristics of BSA-loaded silicone elastomers. An increase in the residual moisture content results in the freeze-dried BSA having a decreased bulk density and poor flow properties which can have a detrimental effect on the cure characteristics of a freeze-dried BSA-loaded silicone elastomer. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2012

Numerical and Experimental Investigation of Aircraft Panel Deformations During Riveting Process

In collaboration with Airbus-UK, the dimensional growth of aircraft panels while being riveted with stiffeners is investigated. Small panels are used in this investigation. The stiffeners have been fastened to the panels with rivets and it has been observed that during this operation the panels expand in the longitudinal and transverse directions. It has been observed that the growth is variable and the challenge is to control the riveting process to minimize this variability. In this investigation, the assembly of the small panels and longitudinal stiffeners has been simulated using static stress and nonlinear explicit finite element models. The models have been validated against a limited set of experimental measurements; it was found that more accurate predictions of the riveting process are achieved using explicit finite element models. Yet, the static stress finite element model is more time efficient, and more practical to simulate hundreds of rivets and the stochastic nature of the process. Furthermore, through a series of numerical simulations and probabilistic analyses, the manufacturing process control parameters that influence panel growth have been identified. Alternative fastening approaches were examined and it was found that dimensional growth can be controlled by changing the design of the dies used for forming the rivets.

Estimation of Clarinet Reed Parameters by Inverse Modelling

Analysis of the acoustical functioning of musical instruments invariably involves the estimation of model parameters. The broad aim of this paper is to develop methods for estimation of clarinet reed parameters that are representative of actual playing conditions. This presents various challenges because of the di?culties of measuring the directly relevant variables without interfering with the control of the instrument. An inverse modelling approach is therefore proposed, in which the equations governing the sound generation mechanism of the clarinet
are employed in an optimisation procedure to determine the reed parameters from the mouthpiece pressure and volume ?ow signals. The underlying physical model captures most of the reed dynamics and is simple enough to be used in an inversion process. The optimisation procedure is ?rst tested by applying it to numerically synthesised signals, and then applied to mouthpiece signals acquired during notes blown by a human player. The proposed inverse modelling approach raises the possibility of revealing information about the way in which the embouchure-related reed parameters are controlled by the player, and also facilitates physics-based re-synthesis of clarinet sounds.

Digital Methods for Process Development in Manufacturing and Their Relevance to Value Driven Design

This paper examines the applicability of a digital manufacturing framework to the implementation of a Value Driven Design (VDD) approach for the development of a stiffened composite panel. It presents a means by which environmental considerations can be integrated with conventional product and process design drivers within a customized, digital environment. A composite forming process is used as an exemplar for the work which creates a collaborative environment for the integration of more traditional design drivers with parameters related to manufacturability as well as more sustainable processes and products. The environmental stakeholder is introduced to the VDD process through a customized product/process/resource (PPR) environment where application specific power consumption and material waste data has been measured and characterised in the process design interface. This allows the manufacturing planner to consider power consumption as a concurrent design driver and the inclusion of energy as a parameter in a VDD approach to the development of efficiently manufactured, sustainable transport systems.

Changing climate, changing process: implications for salt transportation and weathering within building sandstones in the UK

Salt weathering is a crucial process that brings about a change in stone, from the scale of landscapes to stone outcrops and natural building stone facades. It is acknowledged that salt weathering is controlled by fluctuations in temperature and moisture, where repeated oscillations in these parameters can cause re-crystallisation, hydration/de-hydration of salts, bringing about stone surface loss in the form of, for example, granular disaggregation, scaling, and multiple flaking. However, this ‘traditional’ view of how salt weathering proceeds may need to be re-evaluated in the light of current and future climatic trends. Indeed, there is considerable scope for the investigation of consequences of climate change on geomorphological processes in general. Building on contemporary research on the ‘deep wetting’ of natural building stones, it is proposed that (as stone may be wetter for longer), ion diffusion may become a more prominent mechanism for the mixing of molecular constituents, and a shift in focus from physical damage to chemical change is suggested. Data from ion diffusion cell experiments are presented for three different sandstone types, demonstrating that salts may diffuse through porous stone relatively rapidly (in comparison to, for example, dense concrete). Pore water from stones undergoing diffusion experiments was extracted and analysed. Factors controlling ion diffusion
relating to ‘time of wetness’ within stones are discussed, (continued saturation, connectivity of pores, mineralogy, behaviour of salts, sedimentary structure), and potential changes in system dynamics as a result of climate change are addressed. System inputs may change in terms of increased moisture input, translating into a greater depth of wetting front. Salts are likely to be ‘stored’ differently in stones, with salt being in solution for longer periods (during prolonged winter wetness). This has myriad implications in terms of the movement of ions by diffusion and the potential for chemical change in the stone (especially in more mobile constituents), leading to a weakening of the stone matrix/grain boundary cementing. The ‘output’ may be mobilisation and precipitation of elements leading to, for example, uneven cementing in the stone. This reduced strength of the stone, or compromised ability of the stone to absorb stress, is likely to make crystallisation a more efficacious mechanism of decay when it does occur. Thus, a delay in the onset of crystallisation while stonework is wet does not preclude exaggerated or accelerated material loss when it finally happens.

Evaluation of drilling process in Ti6Al4V using 3D FE simulation

Drilling of Ti6Al4V is investigated experimentally and numerically. A 3D finite element model developed based on Lagrangian approach using commercial finite element software ABAQUS/explicit. 3D complex drill geometry is included in the model. The drilling process simulations are performed at the combinations of three cutting speed and four feed rates. The effects of cutting parameters on the induced thrust force and torque are predicted by the developed model. For validation purpose, experimental trials have been performed in similar condition to the simulations. The forces and torques measured during experiment are compared to the results of the finite element analysis. The agreement of the experimental results for force and torque values with the FE results is very good. Moreover, surface roughness of the holes was measured for mapping of machining. Copyright © 2013 Inderscience Enterprises Ltd.

Optimisation of Kinetic Parameters for an After-treatment Catalyst

Mathematical modelling has become an essential tool in the design of modern catalytic systems. Emissions legislation is becoming increasingly stringent, and so mathematical models of aftertreatment systems must become more accurate in order to provide confidence that a catalyst will convert pollutants over the required range of conditions. 
Automotive catalytic converter models contain several sub-models that represent processes such as mass and heat transfer, and the rates at which the reactions proceed on the surface of the precious metal. Of these sub-models, the prediction of the surface reaction rates is by far the most challenging due to the complexity of the reaction system and the large number of gas species involved. The reaction rate sub-model uses global reaction kinetics to describe the surface reaction rate of the gas species and is based on the Langmuir Hinshelwood equation further developed by Voltz et al. [1] The reactions can be modelled using the pre-exponential and activation energies of the Arrhenius equations and the inhibition terms. 
The reaction kinetic parameters of aftertreatment models are found from experimental data, where a measured light-off curve is compared against a predicted curve produced by a mathematical model. The kinetic parameters are usually manually tuned to minimize the error between the measured and predicted data. This process is most commonly long, laborious and prone to misinterpretation due to the large number of parameters and the risk of multiple sets of parameters giving acceptable fits. Moreover, the number of coefficients increases greatly with the number of reactions. Therefore, with the growing number of reactions, the task of manually tuning the coefficients is becoming increasingly challenging. 
In the presented work, the authors have developed and implemented a multi-objective genetic algorithm to automatically optimize reaction parameters in AxiSuite®, [2] a commercial aftertreatment model. The genetic algorithm was developed and expanded from the code presented by Michalewicz et al. [3] and was linked to AxiSuite using the Simulink add-on for Matlab. 
The default kinetic values stored within the AxiSuite model were used to generate a series of light-off curves under rich conditions for a number of gas species, including CO, NO, C_{3H8 and C3H6. These light-off curves were used to generate an objective function.} 
This objective function was used to generate a measure of fit for the kinetic parameters. The multi-objective genetic algorithm was subsequently used to search between specified limits to attempt to match the objective function. In total the pre-exponential factors and activation energies of ten reactions were simultaneously optimized. 
The results reported here demonstrate that, given accurate experimental data, the optimization algorithm is successful and robust in defining the correct kinetic parameters of a global kinetic model describing aftertreatment processes.