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Ph.DDOCTOR OF PHILOSOPH

Feature Based Machine Tool Accuracy Analysis Method

AbstractMachine tool accuracy is the most important performance parameters which affect the part quality. At present, a systematic machine tool accuracy evaluation method is necessary for the machine tool selection in process planning and shop-floor scheduling. This paper proposes an efficient feature based machine tool accuracy analysis method to enable machine tool capability evaluation about accuracy, and the mapping from the machine tool accuracy to the part feature tolerance is established in this method. The cutter is used as a bridge to transform the machine tool error to feature tolerance. The deviation of the cutter between the actual position & orientation and the nominal position & orientation is converted from the machine tool error according to the rigid body kinematics method. Then the feature error in the form of GD&T is calculated from the profile of the feature and the deviation of the cutter. A prototype system has been developed based on this research. An industrial case study shows that the methodology is effective

Analysis and optimization of temperature distribution in carbon fiber reinforced composite materials during microwave curing process

Vacuum assisted microwave curing technologies and modified optical sensing systems have been employed to investigate the influence of ply orientation and thickness on through-thickness temperature distribution of carbon fiber reinforced composite laminates. Two different types of epoxy systems have been studied. The results demonstrated that the ply orientation did not affect the temperature distribution of composite materials. However, the thickness was an important influencing factor. Nearly 10 ◦C temperature difference was found in 22.5 mm thick laminates. Through analyzing the physical mechanisms during microwave curing, the temperature difference decreased when the heat-loss in surface laminates was reduced and the absorption of microwave energy in the center laminates was improved. The maximum temperature difference of the samples formed using the modified microwave curing technologies in this research could be reduced by 79% to 2.1 ◦C. Compared with the 5.29 ◦C temperature difference of laminates using thermal heating process, the maximum temperature difference in laminates using modified microwave curing technologies was reduced by 60%, and the curing time was cut down by 25%

Innovative product development (Editorial)

This special issue on innovative product development contains seven invited papers from the CIRP-sponsored 6th International Conference of Digital Enterprise Technology, held at the University of Hong Kong on the 14th – 16th December 2009 (DET 2009). The conference addresses aspects of electronic business and digital enterprise technology, bringing academic rigour and novelty to industrial applications. Over 120 delegates from 12 countries attended the conference

The 9th International Conference on Digital Enterprise Technology – Intelligent Manufacturing in the Knowledge Economy Era

Digital Enterprise Technology (DET) is “the collection of systems and methods for the digital modelling, simulation and optimization of the collaborative product development, factory and manufacturing processes planning, along their lifecycle”. The main aim of DET 2016 is to provide an international forum for the exchange of leading edge scientific knowledge and industrial experiences, regarding the development, integration and applications of the various aspects of Digital Enterprise Technologies, in the global manufafturing of the knowledge economy era. The guiding idea of the conference is to find a common understanding of employing Digital Enterprise Technologies in the factories of the future, moving from automated, to flexible, digital, sustainable, smart and intelligent manufacturing

On-line part deformation prediction based on deep learning

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a Conventional Neural Network and a Recurrent Neural Network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data

A new concept to improve microwave heating uniformity through data-driven process modelling

[EN] For a long time, the heating pattern of the workpiece within a multimode microwave oven was considered to be highly sophisticated. As a consequence, the uneven microwave heating problem can only be partly alleviated by a random movement between the electromagnetic field and the workpiece, like the rotating turntable or mode stirrers. In this paper, we reported that the heating behavior has a specific correspondence with the power ratio of multiple microwave sources under certain conditions. The influence factors of this relationship and the corresponding mechanisms were systematically studied by both theoretical analysis and experimental investigations. On this basis, a data-driven process model was introduced to learn the material’s dynamic temperature behaviors during microwave heating process, and a new concept to improve the microwave heating uniformity by temperature monitoring and active compensation was presented.This project was supported by National Science and Technology Major Project of China (Grant no. 2017ZX04002001).Zhou, J.; Li, Y.; Li, D. (2019). A new concept to improve microwave heating uniformity through data-driven process modelling. En AMPERE 2019. 17th International Conference on Microwave and High Frequency Heating. Editorial Universitat Politècnica de València. 301-308. https://doi.org/10.4995/AMPERE2019.2019.9753OCS30130

Laplace neural operator for complex geometries

Neural operators have emerged as a new area of machine learning for learning mappings between function spaces. Recently, an expressive and efficient architecture, Fourier neural operator (FNO) has been developed by directly parameterising the integral kernel in the Fourier domain, and achieved significant success in different parametric partial differential equations. However, the Fourier transform of FNO requires the regular domain with uniform grids, which means FNO is inherently inapplicable to complex geometric domains widely existing in real applications. The eigenfunctions of the Laplace operator can also provide the frequency basis in Euclidean space, and can even be extended to Riemannian manifolds. Therefore, this research proposes a Laplace Neural Operator (LNO) in which the kernel integral can be parameterised in the space of the Laplacian spectrum of the geometric domain. LNO breaks the grid limitation of FNO and can be applied to any complex geometries while maintaining the discretisation-invariant property. The proposed method is demonstrated on the Darcy flow problem with a complex 2d domain, and a composite part deformation prediction problem with a complex 3d geometry. The experimental results demonstrate superior performance in prediction accuracy, convergence and generalisability.Comment: 21 pages, 15 figure