Alan Myers

University of Huddersfield, Huddersfield, England, United Kingdom

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Publications (47)13.36 Total impact

  • ARCHIVE Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science 1989-1996 (vols 203-210) 08/2014; 228(13):2357-2371. · 0.63 Impact Factor
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    ABSTRACT: This paper describes evaluation of a method of measuring the straightness of motion of machine tool axes using a taut wire and an optical sensor head mounted at the tool point location. In contrast to commonly used taut wire instruments, straightedges or laser-based methods, this solution combines low cost, simplicity of setup and automated data capture while achieving state of the art accuracy suitable for application on precision machine tools. A series of tests are discussed which examine the performance of the new sensing head and different wires which highlight the suitability of the taut wire properties as a straightness reference. Experimental results obtained on a production machine tool are provided with respect to the accuracy and repeatability of both the proposed taut wire system and a laser interferometer operated under the same conditions. The reference errors of wires made of different materials are compared and the wire catenary is separated from the measurement results. The uncertainty budget for taut wire and laser systems is presented and expanded uncertainty of 4 μm obtained for both. During the experiment, the method showed excellent repeatability with two standard deviations of 1.5 μm over a measuring range of 1.5 m; this performance matches that of a commercial laser interferometer-based straightness reference to within 0.1 μm.
    Precision Engineering 07/2014; · 1.39 Impact Factor
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    ABSTRACT: This paper proposes and discusses an active dual-sensor autofocusing method for measuring the positioning errors of arrays of small holes on complex curved surfaces. The dual-sensor unit combines an optical vision sensor and a tactile probe and is designed to achieve rapid automated measurements in a way that can be adapted to be suitable for deployment on a manufacturing machine tool. Mathematical analysis is performed to establish the magnitude of the deviation from the optimal focal length that is induced by the autofocussing method. This evaluation is based on the geometrical relationship and interaction between the radius of the tactile probe with both the measured holes and the complex-curved surface. A description is provided of a laboratory-based standalone dual-sensor autofocusing unit and test rig that was built to perform experimental validation of the method. This system is estimated to have a focusing uncertainty of 11 μm deriving mainly from the inaccuracy of the X-Z translation stage and the maximum permissible error of the tactile probe. A case study is presented which evaluates the accuracy of a pattern of Ø 0.5 mm small holes on an elliptic cylinder. A mathematical analysis of that problem and practical results from both the tactile and optical sensors are provided and discussed. It is estimated that the deviation in optimal focusing induced by this automated method is between -23 μm and +95 μm. This is sufficiently accurate to ensure that the optical device can capture the entire space outline of each of the small holes on the complex curve surface clearly and can therefore identify its centroid from the image to provide a measurement of the position.
    Sensors and Actuators A Physical 04/2014; · 1.84 Impact Factor
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    ABSTRACT: This paper presents and evaluates an active dual-sensor autofocusing system that combines an optical vision sensor and a tactile probe for autofocusing on arrays of small holes on freeform surfaces. The system has been tested on a two-axis test rig and then integrated onto a three-axis computer numerical control (CNC) milling machine, where the aim is to rapidly and controllably measure the hole position errors while the part is still on the machine. The principle of operation is for the tactile probe to locate the nominal positions of holes, and the optical vision sensor follows to focus and capture the images of the holes. The images are then processed to provide hole position measurement. In this paper, the autofocusing deviations are analyzed. First, the deviations caused by the geometric errors of the axes on which the dual-sensor unit is deployed are estimated to be 11 μm when deployed on a test rig and 7 μm on the CNC machine tool. Subsequently, the autofocusing deviations caused by the interaction of the tactile probe, surface, and small hole are mathematically analyzed and evaluated. The deviations are a result of the tactile probe radius, the curvatures at the positions where small holes are drilled on the freeform surface, and the effect of the position error of the hole on focusing. An example case study is provided for the measurement of a pattern of small holes on an elliptical cylinder on the two machines. The absolute sum of the autofocusing deviations is 118 μm on the test rig and 144 μm on the machine tool. This is much less than the 500 μm depth of field of the optical microscope. Therefore, the method is capable of capturing a group of clear images of the small holes on this workpiece for either implementation.
    Applied Optics 04/2014; 53(10):2246-2255. · 1.69 Impact Factor
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    ABSTRACT: This paper presents an evaluation method for the rapid and automatic detection of position errors of arrays of small holes on complex-curved and freeform surfaces that can satisfy the special demands of the aviation and automobile industries. The evaluation is based on the dual-sensor autofocusing method. The dual-sensor unit is the combination of a tactile probe and an optical vision sensor. The tactile probe detects the focal position for the optical vision sensor by probing the distance between the objective lens of the microscope and the location of each small hole. The optical vision sensor focuses to this position for capturing the image of the artifact under inspection. As a case study, a pattern of φ 0.5 mm small holes centripetally drilled with equal-angular distribution on the circumference of an elliptical cylinder shell is investigated. The autofocusing errors caused by the radius of the tactile probe and the position errors of the small holes are evaluated mathematically. Subsequently, a standalone dual-sensor autofocusing unit is built and integrated into a user-controllable 3D coordinate test rig. It is used to autofocus and capture the images of small holes. The centroid positions and deviations of the holes are automatically and rapidly detected from the captured images.
    International Journal of Precision Engineering and Manufacturing 02/2014; 15(2):209-217. · 1.59 Impact Factor
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    ABSTRACT: To meet the requirement of both high speed and high accuracy 3D measurements for dimensional metrology, multi-sensor measuring systems have been developed to measure, analyse and reverse engineer the geometry of objects. This paper presents a new development in coordinate unification called the “centroid of spherical centres” method, which can be used instead of the traditional method which uses three datum-points to perform the geometric transformation and unification of tactile and optical sensors. The benefits of the proposed method are improved accuracy in coordinate unification and the method is used to integrate a coordinate measuring machine (CMM) and optical sensors (structured light scanning system and FaroArm laser line probe). A sphere-plate artefact is developed for data fusion of the multi-sensor system and experimental results validate the accuracy and effectiveness of this method.
    Optics and Lasers in Engineering 01/2014; 55:189–196. · 1.92 Impact Factor
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    ABSTRACT: Reducing downtime and assuring a high degree of accuracy of production machines, especially machine tools, have become increasingly important as the demand for higher production rates and closer tolerance continues to grow. The growing understanding of the importance of both calibration and maintenance in the evolving industrial scenario and the technological advancements of recent years has yielded the development of advanced metrology equipment and predictive maintenance programs. Predictive maintenance and similar programmes are tools that have been designed to reduce downtime by avoiding unpredictable machine failures. These programmes have been adopted in some industries to improve operational efficiency and reduce machine breakdown. However, extensive diagnostic procedures can take machines out of service for longer periods than are acceptable for some manufacturers. Studies in the field of predictive maintenance have resulted in cost calculations for the downtime associated with machine failure. Models have been presented to determine optimal intervals between repairs by minimising global maintenance costs. However, very little work has concentrated on optimising the frequency of machine tool calibration by assessing the downtime cost considering the contribution of both technical and commercial factors. This paper give an introduction to causes of machine tools failures with respect to production of non-conforming parts and the importance of calibration and then it addresses the key factors to a cost function parameters that forms the basis of a strategy for scheduling machine tool calibration which takes into account these influences on part tolerance.
    Computing and Engineering Researchers’ Conference, University of Huddersfield, December 2013; 12/2013
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    ABSTRACT: This research work describes an intelligent and modular architecture for controlling the milling process. For this purpose, it is taking into account the admissible input cutting parameters given by the stability lobes and restrictions of the system, calculating the quasi-optimal cutting parameters. The quasi-optimal cutting parameters are obtained considering a cost function with multi-objective purpose. Furthermore, parallel multi-estimation adaptive control architecture is proposed in order to allow adaptation of the system when the cutting parameters are changed, for example, due to production requirements. It incorporates an intelligent supervisory system to address the problem of choosing the most adequate control signal at each required time. The fundamental idea of the control system is to work automatically, with a simple interface with the operator, based around the admissible cutting parameter space given by the well-known stability lobes.
    Advanced Manufacturing Engineering and Technologies NEWTECH 2013, Stockholm; 10/2013
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    ABSTRACT: Thermal errors are often quoted as being the largest contributor to inaccuracy of CNC machine tools, but they can be effectively reduced using error compensation. Success in obtaining a reliable and robust model depends heavily on the choice of system variables involved as well as the available input-output data pairs and the domain used for training purposes. In this paper, a new prediction model “Grey Neural Network model with Convolution Integral (GNNMCI(1, N))” is proposed, which makes full use of the similarities and complementarity between Grey system models and Artificial Neural Networks (ANNs) to overcome the disadvantage of applying a Grey model and an artificial neural network individually. A Particle Swarm Optimization (PSO) algorithm is also employed to optimize the Grey neural network. The proposed model is able to extract realistic governing laws of the system using only limited data pairs, in order to design the thermal compensation model, thereby reducing the complexity and duration of the testing regime. This makes the proposed method more practical, cost-effective and so more attractive to CNC machine tool manufacturers and especially end-users. Conference Proceeding: http://kth.diva-portal.org/smash/get/diva2:660817/FULLTEXT08.pdf
    The 3rd International Conference on Advanced Manufacturing Engineering and Technologies, Stockholm, Sweden; 10/2013
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    ABSTRACT: This paper presents a novel control architecture system which is composed of a multi-objective cost function which Pareto optimizes the programming of cutting parameters while controlling the milling process to new cutting conditions if new constraints appear. The paper combines a self-optimized module which looks for and finds Pareto optimal cutting parameters according to multi-objective purposes and, a multi-model control module which keeps the cutting forces under prescribed upper safety limits independently of programmed cutting conditions and material properties while maintaining the performance of the process. An intelligent algorithm acts as decision supportsoftware to automatically switch to the best performance tracking controller among those available at each required time.
    IEEE International Conference on Systems, Man and Cybernetics, Manchester; 10/2013
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    ABSTRACT: Machine tool failures in industrial organisations disturb production operations and cause production loss. Predictive maintenance is one approach which has been successfully applied in some circumstances to allow scheduled production stoppages. It is an approach that reduces the need for reactive maintenance. Predictive maintenance is a tool that has been adopted in some industries to improve operational efficiency and reduce maintenance cost. As a result, monitoring equipment providing information about the systems conditions have evolved rapidly over the last years. Machine tools can change or drift over time and usage in both their mechanical and electrical performance and so reduce in accuracy. This paper proposes a new method for maintaining machine tool accuracy that is complimentary to the predictive maintenance paradigm. This strategy, called predictive calibration, is a methodology that depends on the prediction of the degradation in machine tool accuracy based upon regular data capture. Although introducing such a strategy will introduce a new cost, the aim is to offset this investment by optimising the operational efficiency and reduce the downtime cost. The main objective is achieved by monitoring the condition of the machine tool by collecting data using quick check measurement techniques or post-process quality data. Calibration should, therefore, be driven by the data measured from either the machine or the part. Building a database of inspection history by measuring the machine on a regular basis with relatively non-invasive methods will make the decision of scheduling extensive calibration accurate better informed process. The project presents a new method of identifying new boundaries of machine tool working tolerance. These boundaries of tolerance reflect the degradation level corresponding to production capacities and the quality of the part produced. The significance of this work is that machine tool accuracy is critical for high value manufacturing. Over-measuring the machine to ensure accuracy reduces productivity. This piece of work seeks to optimise the frequency of calibration to reduce unnecessary downtime while maintaining the machine at the required tolerance.
    89MANUFACTURING THE FUTURE CONFERENCE 2013, Cranfield University; 09/2013
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    ABSTRACT: This paper describes a novel method to measure straightness error of an axis of motion with a system utilising taut wire, optical sensor and reference error cancellation technique. In contrast to commonly used taut wire, straightedge or laser-based methods it combines simplicity of setup and low cost with high levels of automation, accuracy and repeatability. An error cancellation technique based on two-point method is applied for the first time to a versatile reference object which can be mounted at any place of machine's working volume allowing direct measurement of motion straightness of a tool point. Experimental results on a typical machine tool validate performance of the proposed taut wire system with a commercial laser interferometer operating in the same conditions is used as a reference. The proposed method shows highly repeatable results of better than ±0.25 μm over the range of 0.48 m and measurement accuracy comparable to the interferometer of ±0.5 μm.
    Optics and Lasers in Engineering 08/2013; 51(8):978–985. · 1.92 Impact Factor
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    ABSTRACT: Machine tools are susceptible to exogenous influences, which mainly derive from varying environmental conditions such as the day and night or seasonal transitions during which large temperature swings can occur. Thermal gradients cause heat to flow through the machine structure and results in non-linear structural deformation whether the machine is in operation or in a static mode. These environmentally stimulated deformations combine with the effects of any internally generated heat and can result in significant error increase if a machine tool is operated for long term regimes. In most engineering industries, environmental testing is often avoided due to the associated extensive machine downtime required to map empirically the thermal relationship and the associated cost to production. This paper presents a novel offline thermal error modelling methodology using finite element analysis (FEA) which significantly reduces the machine downtime required to establish the thermal response. It also describes the strategies required to calibrate the model using efficient on-machine measurement strategies. The technique is to create an FEA model of the machine followed by the application of the proposed methodology in which initial thermal states of the real machine and the simulated machine model are matched. An added benefit is that the method determines the minimum experimental testing time required on a machine; production management is then fully informed of the cost-to-production of establishing this important accuracy parameter. The most significant contribution of this work is presented in a typical case study; thermal model calibration is reduced from a fortnight to a few hours. The validation work has been carried out over a period of over a year to establish robustness to overall seasonal changes and the distinctly different daily changes at varying times of year. Samples of this data are presented that show that the FEA-based method correlated well with the experimental results resulting in the residual errors of less than 12 μm.
    Precision Engineering. 04/2013; 37(2):372–379.
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    ABSTRACT: This paper presents a novel control architecture system which is composed of a multi-objective cost function which Pareto optimises the programming of cutting parameters while adapting the milling process to new cutting conditions if new constraints appear. The paper combines a self-optimised module which looks for and finds Pareto optimal cutting parameters according to multi-objective purposes and, a multi-estimation adaptive control module which keeps the cutting forces under prescribed upper safety limits independently of programmed cutting conditions and material properties while maintaining the performance of the process. A supervised controller acts as decision support-software to automatically switch to best performance tracking adaptive controller among those available at each required time.
    Lamdamap 10th International Conference, Chicheley Hall Chicheley, Chester, England; 03/2013
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    ABSTRACT: Thermal errors can have significant effects on CNC machine tool accuracy. The errors usually come from thermal deformations of the machine elements created by heat sources within the machine structure or from ambient change. The performance of a thermal error compensation system inherently depends on the accuracy and robustness of the thermal error model. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) techniques were employed to design four thermal prediction models: ANFIS by dividing the data space into rule patches (ANFIS-Scatter partition model); ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid partition model); ANN with a back-propagation algorithm (ANN-BP model) and ANN with a PSO algorithm (ANN-PSO model). Grey system theory was also used to obtain the influence ranking of the input sensors on the thermal drift of the machine structure. Four different models were designed, based on the higher-ranked sensors on thermal drift of the spindle. According to the results, the ANFIS models are superior in terms of the accuracy of their predictive ability; the results also show ANN-BP to have a relatively good level of accuracy. In all the models used in this study, the accuracy of the results produced by the ANFIS models was higher than that produced by the ANN models. Full paper available at: http://eprints.hud.ac.uk/17011/4/ABDULSHAHED_Lamdamap2013.pdf
    Lamdamap 10th International Conference; 01/2013
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    ABSTRACT: Understanding a machine tool?s capability is fundamental for applications where a high level of accuracy is required, such as aerospace manufacturing. Therefore, calibrating machine tools to International Standards is an important process and should be executed thoroughly. The calibration process requires firstly identifying the errors component of a machine tool, followed by the selection of an appropriate test method and instrumentation. The emphasis on minimising machine down-time requires expert knowledge to ensure that the calibration process is both complete and optimal. The paper shows the process of planning a machine tool calibration and is followed by a description of how using automated planning techniques will improve machine too calibration planning.
    Proceedings of The Queen?s Diamond Jubilee Computing and Engineering Annual Researchers? Conference 2012: CEARC?12, Edited by Gary Lucas, 03/2012: pages 57-62; University of Huddersfield.
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    ABSTRACT: This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input - output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities.
    Journal of Physics Conference Series 01/2012; 364(1).
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    ABSTRACT: A literature search has indicated that artificially intelligent planners have not previously been used to address the planning problem of machine tool calibration, even though there are potential advantages. The complexity of machine tool calibration planning requires the understanding and examination of many influential factors, such as the machine?s configuration and available instrumentation. In this paper we show that machine tool calibration planning can be converted into a Hierarchical Task Network by the process of task decomposition. It is then shown how the Simple Hierarchical Ordered Planner architecture can be used to provide all the identified complete process plans in a given time frame, and secondly, how the branch-and-bound optimisation algorithm can find the optimal solution in the same frame. The results for generating the process plans and optimal process plans for both a three and five axis machine are evaluated to examine the planner?s performance.
    Proceedings of The 29th Workshop of the UK Planning and Scheduling Special Interest Group (PlanSIG2011). 12/2011;
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    ABSTRACT: Thermally induced errors have a major significance on the positional accuracy of a machine tool. Heat generated during the machining process produces thermal gradients that flow through the machine structure causing linear and nonlinear thermal expansions and distortions of associated complex discrete structures, producing deformations that adversely affect structural stability. The heat passes through structural linkages and mechanical joints where interfacial parameters such as the roughness and form of the contacting surfaces affect the thermal resistance and thus the heat transfer coefficients. This paper presents a novel offline technique using finite element analysis (FEA) to simulate the effects of the major internal heat sources such as bearings, motors and belt drives of a small vertical milling machine (VMC) and the effects of ambient temperature pockets that build up during the machine operation. Simplified models of the machine have been created offline using FEA software and evaluated experimental results applied for offline thermal behaviour simulation of the full machine structure. The FEA simulated results are in close agreement with the experimental results ranging from 65% to 90% for a variety of testing regimes and revealed a maximum error range of 70 µm reduced to less than 10 µm.
    Measurement Science and Technology 07/2011; 22(8):085107. · 1.44 Impact Factor
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    17th International Conference on Automation and Computing, ICAC 2011, Huddersfield, United Kingdom, September 10, 2011; 01/2011