Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc) for high accuracy. However it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper an approach for surface profile pre-evaluation in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and free rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy

From computer-aided to intelligent machining: Recent advances in computer numerical control machining research

The aim of this paper is to provide an introduction and overview of recent advances in the key technologies and the supporting computerized systems, and to indicate the trend of research and development in the area of computational numerical control machining. Three main themes of recent research in CNC machining are simulation, optimization and automation, which form the key aspects of intelligent manufacturing in the digital and knowledge based manufacturing era. As the information and knowledge carrier, feature is the efficacious way to achieve intelligent manufacturing. From the regular shaped feature to freeform surface feature, the feature technology has been used in manufacturing of complex parts, such as aircraft structural parts. The authors’ latest research in intelligent machining is presented through a new concept of multi-perspective dynamic feature (MpDF), for future discussion and communication with readers of this special issue. The MpDF concept has been implemented and tested in real examples from the aerospace industry, and has the potential to make promising impact on the future research in the new paradigm of intelligent machining. The authors of this paper are the guest editors of this special issue on computational numerical control machining. The guest editors have extensive and complementary experiences in both academia and industry, gained in China, USA and UK

Research on hybrid manufacturing using industrial robot

The applications of using industrial robots in hybrid manufacturing overcome many restrictions of the conventional manufacturing methods, such as small part building size, long building period, and limited material choices. However, some problems such as the uneven distribution of motion accuracy within robot working volume, the acceleration impact of robot under heavy external loads, few methods and facilities for increasing the efficiency of hybrid manufacturing process are still challenging. This dissertation aims to improve the applications of using industrial robot in hybrid manufacturing by addressing following three categories research issues. The first research issue proposed a novel concept view on robot accuracy and stiffness problem, for making the maximum usage of current manufacturing capability of robot system. Based on analyzing the robot forward/inverse kinematic, the angle error sensitivity of different joint and the stiffness matrix properties of robot, new evaluation formulations are established to help finding the best position and orientation to perform a specific trajectory within the robot\u27s working volume. The second research issue focus on the engineering improvements of robotic hybrid manufacturing. By adopting stereo vision, laser scanning technology and curved surface compensation algorithm, it enhances the automation level and adaptiveness of hybrid manufacturing process. The third research issue extends the robotic hybrid manufacturing process to the broader application area. A mini extruder with a variable pitch and progressive diameter screw is developed for large scale robotic deposition. The proposed robotic deposition system could increase the building efficiency and quality for large-size parts. Moreover, the research results of this dissertation can benefit a wide range of industries, such as automation manufacturing, robot design and 3D printing --Abstract, page iv

Doctor of Philosophy

dissertationThe medial axis of an object is a shape descriptor that intuitively presents the morphology or structure of the object as well as intrinsic geometric properties of the object’s shape. These properties have made the medial axis a vital ingredient for shape analysis applications, and therefore the computation of which is a fundamental problem in computational geometry. This dissertation presents new methods for accurately computing the 2D medial axis of planar objects bounded by B-spline curves, and the 3D medial axis of objects bounded by B-spline surfaces. The proposed methods for the 3D case are the first techniques that automatically compute the complete medial axis along with its topological structure directly from smooth boundary representations. Our approach is based on the eikonal (grassfire) flow where the boundary is offset along the inward normal direction. As the boundary deforms, different regions start intersecting with each other to create the medial axis. In the generic situation, the (self-) intersection set is born at certain creation-type transition points, then grows and undergoes intermediate transitions at special isolated points, and finally ends at annihilation-type transition points. The intersection set evolves smoothly in between transition points. Our approach first computes and classifies all types of transition points. The medial axis is then computed as a time trace of the evolving intersection set of the boundary using theoretically derived evolution vector fields. This dynamic approach enables accurate tracking of elements of the medial axis as they evolve and thus also enables computation of topological structure of the solution. Accurate computation of geometry and topology of 3D medial axes enables a new graph-theoretic method for shape analysis of objects represented with B-spline surfaces. Structural components are computed via the cycle basis of the graph representing the 1-complex of a 3D medial axis. This enables medial axis based surface segmentation, and structure based surface region selection and modification. We also present a new approach for structural analysis of 3D objects based on scalar functions defined on their surfaces. This approach is enabled by accurate computation of geometry and structure of 2D medial axes of level sets of the scalar functions. Edge curves of the 3D medial axis correspond to a subset of ridges on the bounding surfaces. Ridges are extremal curves of principal curvatures on a surface indicating salient intrinsic features of its shape, and hence are of particular interest as tools for shape analysis. This dissertation presents a new algorithm for accurately extracting all ridges directly from B-spline surfaces. The proposed technique is also extended to accurately extract ridges from isosurfaces of volumetric data using smooth implicit B-spline representations. Accurate ridge curves enable new higher-order methods for surface analysis. We present a new definition of salient regions in order to capture geometrically significant surface regions in the neighborhood of ridges as well as to identify salient segments of ridges

Design for manufacture using machining features on CNC machining centers

Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems have become more and more needed and useful in the machining processes environment. In order to achieve competitive advantage, companies adopted new manufacturing methods. As a consequence, and in machining processes context, the interaction of CAD and CAM has growth over the years in order to increase the production efficiency, as well as to reduce costs and time. The development of this work started with an extensive literature review. In that review, the author found that only a few articles approached the interaction or integration of CAD and CAM systems. Moreover, the authors that studied this interaction focused on systems for turning parts. Thus, there is a gap in the literature related to the integration and automation of these systems when applied to milling parts. Therefore, the purpose of this dissertation is to enable the interaction of these systems in order to provide a completely automated process since the design stage until the machining stage. Finally, the process’ implementation showed that the developed algorithm was able to satisfy the initial requirements of this work, i.e., when given a set of initial parameters, the program drew the required geometry, and then generated the required G-code, such that this code can be sent to the CAM software to machine the workpiece, thereby obtaining the final product.Os sistemas Computer-Aided Design (CAD) and Computer-Aided Manufacturing(CAM) estão, cada vez mais, a ser mais necessários e úteis no contexto da maquinagem. De modo a conseguir vantagem competitiva, as empresas têm adotado novos métodos de produção. Consequentemente, no contexto da indústria da maquinagem, a interação entre CAD e CAM tem crescido nos últimos anos, de modo a permitir uma maior eficácia na produção, assim como também redução de tempo e custo. O desenvolvimento deste trabalho começou com uma extensa revisão da literatura. Nesta revisão, o autor apercebeu-se que apenas alguns artigos se debruçaram sobre a interação ou integração dos sistemas CAD e CAM. Para além disso, os autores desses artigos focaram-se em sistemas para torneamento. Assim, constata-se que existe um espaço livre na literatura no que diz respeito à integração destes sistemas quando aplicados à fresagem. Por isso, o objetivo desta dissertação é permitir a interação dos dois sistemas referidos, de forma a promover um processo completamente automático desde o design até à maquinagem. Por fim, a implementação do processo mostrou que o algoritmo desenvolvido alcançou os objetivos iniciais do trabalho, ou seja, baseando-se apenas nos parâmetros fornecidos, o programa desenhou as geometrias necessárias, sendo depois capaz de gerar o código G respetivo, para que este possa ser transferido para o centro de maquinagem, de modo a que o material possa ser maquinado, dando origem ao produto final

Integrating tolerances in G and M codes using neural networks

Continuous integrated solutions from CAD down to the preparation of NC programs were developed in the recent years. However, if tolerances should be considered, the interaction of human experts is still necessary. A way to fill this gap in the production process is shown in this thesis. The study builds a relationship between the given design tolerances and including these tolerances in machining by generating respective G and M codes. The study focuses on physical phenomena and their inter-relationship while manufacturing. For example how the speed of machining, torque, power, depth of cut, etc. influences machining under specified tolerances. Artificial neural networks (ANN) have been used to generate required outputs because of their capability to learn from a given set of data points. Four different kinds of neural networks, as a module, have been used. with different kinds of learning rules (algorithms) depending on the type of inputs and outputs. The whole model incorporates retrieval of tolerances from a CAD software and running the algorithms for (i) Dimensional tolerance analysis, (ii) Control of feed rate, spindle speed, depth of cut and cutting forces, (iii) Propagation of errors in multistage machining, and (iv) Vectorization of geometrical tolerances. Machining processes would include (i) Milling, (ii) Turning, and (iii) Drilling. Then the corresponding outputs are interpreted and analyzed to generate G and M codes. This study has shown how ANN can revolutionize NC machine manufacturing. A case study illustrates the effectiveness of the proposed method

Active and Passive Control of Machine Tool Vibrations for High Speed and Accuracy

High-performance mechatronic systems are widely used in precision manufacturing equipment such as CNC machine tools, 3D-Printers, photolithography systems, industrial robots, and Coordinate Measuring Machines (CMMs). These equipment are utilized in producing parts and components for aviation, semiconductor, optics, and many other emerging industries, with geometric features and surface properties within micrometer-, or even in some cases nanometer-level accuracy. To keep up with the rapidly increasing productivity and accuracy demands, it is crucial that mechatronic systems of these manufacturing equipment deliver high-speed motion with high precision. In this dissertation, motion control strategies are presented to increase dynamic positioning accuracy and productivity of such mechatronic systems. First, a novel trajectory generation method is presented to avoid exciting low frequency structural vibration modes of machine tools and 3D-Printers, without compromising from productivity. The trajectory generation problem is posed as a convex optimization problem, and a practical windowing method is presented to implement the proposed strategy in real-time for long and realistic manufacturing scenarios. The proposed algorithm is validated on an industrial 3-Axis machine tool, and 4-6 times attenuation of the column vibration mode is achieved with 1[g] acceleration commands, without increasing the cycle time compared to state-of-the-art trajectory generation methods. This is followed by proposition of a data-driven trajectory shaping algorithm designed to eliminate dynamic positioning errors induced by flexible motion transmission components (such as ball-screw drives) and nonlinear friction forces typically caused by mechanical bearings and guiding units. The proposed algorithm is used for optimizing trajectory pre-filters through machine-in-the-loop iterations, in a data-driven fashion, and therefore it can be applied on a wide variety of systems without requiring elaborate dynamic modeling. Effectiveness of the proposed technique is validated on a linear-motor-driven planar motion stage and an industrial 3-Axis machine tool, and it is shown that dynamic errors are reduced by 3-5 times compared to industry-standard approaches. Finally, an active tool position control strategy is proposed to mitigate self-excited (chatter) vibrations for improving stability margins of turning processes. Two motion control algorithms are developed to control the dynamic process defined by the interaction of the tool and the workpiece. An industrial lathe (turning center) is utilized for validating the effectiveness of proposed algorithms. A piezo-actuator driven tool-assembly is utilized to control tool position during the machining process, utilizing tool acceleration feedback, and the experiments show that 4-5 times increase in productivity (widths of cut) is achieved by the proposed strategy