In Situ Topology Measurement of Micro Structured Surfaces with a Confocal Chromatic Sensor on a Desktop Sized Machine Tool

2016 ◽  
Vol 1140 ◽  
pp. 392-399
Author(s):  
Christopher Müller ◽  
Ingo G. Reichenbach ◽  
Martin Bohley ◽  
Jan Christian Aurich
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Machining Process ◽  
Measuring System ◽  
Micro Machining ◽  
Structured Surfaces ◽  
System A ◽  
Micro Structures ◽  
Tool Diameter

In this research a confocal chromatic point sensor was implemented in a desktop sized machine tool. The sensor was used to detect the surface in z-direction. Data from the machine control of the x- and y-axes is extracted and combined with the z- information of the sensor to directly scan surfaces. With the presented sensor, micro structures as small as 5 μm can be characterized. Based on the possibilities of this measuring system, face milling before the actual micro machining can be avoided by determining tilts and waviness of the workpiece. Also the effective tool diameter can be determined and compensated. After machining, the structure can be measured for quality control. Based on this measurement system, a micro machining process was developed broadening the potential for the use of desktop sized machine tools.

scholarly journals On the Application of Impeller in Multi Axis CNC Machine Tools

2021 ◽  
Vol 2066 (1) ◽  
pp. 012113
Author(s):  
Weiwen Ye
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Complex Surface ◽  
Machining Process ◽  
Cnc Machine Tools ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Surface Processing ◽  
The Third ◽  
Machining Characteristics

Abstract Multi axis CNC machine tool has good linkage processing effect. Through the application of integral impeller in CNC machine tools, to improve the adaptability of CNC machine tools to complex surface processing parts, to improve the accuracy of multi axis CNC machine tools. The first part of this paper introduces the integral impeller and its machining characteristics; the second part introduces the basic NC machining process of integral impeller; the third part discusses the application of impeller in multi axis CNC machine tools from the creation of guide track, the simulation of integral impeller, software processing and generation. The purpose is to provide some reference for the processing and production of integral impeller.

Dynamic optimization method with applications for machine tools based on approximation model

2017 ◽  
Vol 232 (11) ◽  
pp. 2009-2022 ◽  
Author(s):  
TJ Li ◽  
XH Ding ◽  
K Cheng ◽  
T Wu
Keyword(s):  
Machine Tool ◽  
Performance Optimization ◽  
Machine Tools ◽  
Optimization Design ◽  
Natural Frequencies ◽  
Dynamic Performance ◽  
Machining Process ◽  
Machining Accuracy ◽  
Approximation Model ◽  
Dynamic Behaviors

Natural frequencies and modal shapes of machine tools have position-dependent characteristics owing to their dynamic behaviors changing with the positions of moving parts. It is time-consuming and difficult to evaluate the dynamic behaviors of machine tools and their machining accuracy at different positions. In this paper, a Kriging approximation model coupled with finite element method is proposed to substitute the dynamic equations for obtaining the position-dependent natural frequencies of a machine tool, as well as relative positions between the tool and the workpiece during the machining process. Based on the proposed method, dynamic performance optimization design of the machine tool is conducted under the condition of minimum relative positions. Three case studies are illustrated to demonstrate the implementation of the proposed method.

Mathematical Approach for Geometric Error Modeling of Three Axis CNC Vertical Milling Machine

2016 ◽  
Vol 842 ◽  
pp. 303-310 ◽  
Author(s):  
Widyanti Kwintarini ◽  
Agung Wibowo ◽  
Yatna Yuwana Martawirya
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Machining Process ◽  
Cnc Machine Tools ◽  
Milling Machine ◽  
Cnc Machine ◽  
Geometrical Errors ◽  
Vertical Milling ◽  
Vertical Milling Machine ◽  
Accuracy And Repeatability

The aim of this paper overviews about to find out the errors that come from three axis CNC vertical milling machine. The errors come from, the CNC milling machine can be modelled into mathematical models and later on these error models will be used to analyse the errors in the measured data. Many errors from CNC machine tools have given significant effects toward the accuracy and repeatability of manufacturing process. There are two error sources come from CNC machine tools such as tool deflection and thermal distortions of machine tool structure. These errors later on will contribute to result in the geometrical deviations of moving axis in CNC vertical milling machine. Geometrical deviations of moving axis such as linear positioning errors, roll, pitch and yaw can be designated as volumetric errors in three axis machine tool. Geometrical deviations of moving axises happen at every axis in three axis CNC vertical milling machine. Geometrical deviations of moving axises in linear and angular movement has the amount of errors up to twenty one errors. Moreover, this geometrical errors play the major role in the total amount of errors and for that particular reason extra attention towards the geometrical deviation errors will be needed along machining process. Each of geometrical error of three axes vertical machining center is modeled using a homogeneous transformation matrix (HTM). The developed mathematical model is used to calculate geometrical errors at each axis and to predict the resultant error vector at the interface of machine tool and workpiece for error compensation.

scholarly journals Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

2018 ◽  
Vol 232 ◽  
pp. 01006
Author(s):  
Sanping Wang ◽  
Junwen Chen ◽  
Wei Yan
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Machine Tool ◽  
Process Parameters ◽  
Machine Tools ◽  
Machining Process ◽  
Machining Parameters ◽  
Nc Machine Tool ◽  
Nc Machine

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters.

Mini Special Issue on Machining Control and Process Monitoring

2014 ◽  
Vol 8 (6) ◽  
pp. 791-791
Author(s):  
Tojiro Aoyama
Keyword(s):  
Machine Tool ◽  
Process Monitoring ◽  
Machine Tools ◽  
Cutting Tools ◽  
Machining Process ◽  
Machining Accuracy ◽  
Special Issue ◽  
Practical Proposal ◽  
Machining Control ◽  
Novel Approaches

Control and process monitoring are key technologies supporting high machining accuracy and efficiency. This special issue features six papers taking novel approaches to controlling machine and cutting tools and monitoring the machining process. The motion control of machine tools and cutting tools are introduced. A new challenge for monitoring the machining process by referring to NC control servo signals implements a practical proposal. The precise identification of friction at driving elements of machine tool components is an important factor in improving machine tool control motion accuracy. I would like to express my sincere appreciation to the authors and reviewers whose invaluable efforts have helped make the publication of this manuscript possible.

scholarly journals Toward a Generalized Energy Prediction Model for Machine Tools

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Raunak Bhinge ◽  
Jinkyoo Park ◽  
Kincho H. Law ◽  
David A. Dornfeld ◽  
Moneer Helu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Process Planning ◽  
Machine Tools ◽  
Prediction Models ◽  
Machining Process ◽  
Data Driven ◽  
Process Data ◽  
Energy Prediction

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Special Issue on Modeling and Simulation of Cutting Process

2010 ◽  
Vol 4 (3) ◽  
pp. 213-213
Author(s):  
Keiichi Shirase
Keyword(s):  
Machine Tool ◽  
Modeling And Simulation ◽  
Process Modeling ◽  
Machine Tools ◽  
Numerical Control ◽  
Cutting Process ◽  
Machining Process ◽  
Special Issue ◽  
Machine Tool Dynamics ◽  
Tool Dynamics

In the 5 decades-plus since the first numerical control (NC) machine tool was demonstrated at the Massachusetts Institute of Technology in Boston, MA, USA, advances such as high-speed, multi-axis and multi-tasking machine tools have been introduced widely to achieve high quality and productivity in machining operations. In order to handle these sophisticated machine tools freely and effectively, sophisticated NC programs are conventionally required in advance for problem-free machining. Computer simulation and optimization of cutting processes by considering process physics, machine tool dynamics and kinematics and process constraints are helpful in the strategic process planning operation and useful in preparing sophisticated NC programs. However, challenges and models quantitatively predicting cutting process performance remain to be developed. Topics of interests in this special issue include but are not limited to - machining process modeling - machine tool dynamics modeling - cutting force, cutting temperature, surface roughness, etc., prediction - machining stability prediction - simulation-based machining-process diagnostics - optimization using machining simulation The review paper and ten research works accepted are related to state-of-the-art modeling and simulation applicable to the machining and manufacturing domains. Besides traditional machining, nontraditional machining such as laser machining for micromachining have been explored. Also the machining of calcium polyphosphate (CPP) for tissue engineering applications has been investigated. The articles in this special issue are sure to prove interesting, informative, and inspiring to our readers on advances in cutting process modeling and simulation. Finally, we thank the authors, reviewers, and editors for their invaluable contributions and generous efforts in enabling this issue to be published.

The Printed Machine Tool for Micro Machining

2014 ◽  
Vol 1018 ◽  
pp. 433-440 ◽  
Author(s):  
Christoph Batke ◽  
Karl Heinz Wurst ◽  
Armin Lechler ◽  
Alexander Verl
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Ball Screw ◽  
Work Piece ◽  
Micro Machining ◽  
Motion Generation ◽  
Alternative Drive ◽  
Space Saving ◽  
Cost Efficient ◽  
Ball Screw Drives

Machine tools for micro machining are so far not adapted to work piece sizes and process forces. They feature hardly any modularity and do not allow reconfiguration in a significant process change. One possibility to adapt the machines is to produce them from plastic or composite materials through generative methods. This “printed” machine is a reconfigurable, monolithic module, in which drives are integrated. By a cooperative motion generation, larger workspaces can be realized while the installation spaces decreases. This gives the possibility to use alternative drive technologies for example piezo-drives. Based on these methods, two small generatively produced machine tools are designed. These machine tools use two different drive principles. The first machine tool is equipped with ball screw drives which are cost efficient and space saving. The second machine tool uses piezo-actuators, which are very dynamic in motion generation. Further has to be examined, which tolerances and rigidities are needed at critical points and which parts can be produced generatively and which in a conventional way.

Study using position silicon detectors for performance testing of computer numerically controlled machine tools

2002 ◽  
Vol 216 (5) ◽  
pp. 725-733 ◽  
Author(s):  
W Jywe
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Dynamic Performance ◽  
Performance Testing ◽  
Measuring System ◽  
Cnc Machine Tools ◽  
Small Radius ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Computer Numerically Controlled

In this paper, various contouring test systems for computer numerically controlled (CNC) machine tools are reviewed. It is the first time a laser diode and a quadrant sensor have been employed to build a simple contouring measuring system for testing dynamic performance of a CNC machine tool. The experimental work on a CNC machine tool with a Fanuc OM controller for various contouring paths under specified feed rates is carried out. Then, the compensation work is executed with the assistance of this developed contouring system. After the compensation, the contouring error, especially at a high feed rate and small radius, is reduced significantly.

Experimental Identification of Dynamic Parameters of Rolling Element Bearings in Machine Tools

1998 ◽  
Vol 122 (1) ◽  
pp. 95-101 ◽  
Author(s):  
D. M. Shamine ◽  
S. W. Hong ◽  
Y. C. Shin
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Response Functions ◽  
Rolling Element ◽  
Element Bearing ◽  
Joint Parameters

In-situ identification is essential for estimating bearing joint parameters involved in spindle systems because of the inherent interaction between the bearings and spindle. This paper presents in-situ identification results for rolling element bearing parameters involved in machine tools by using frequency response functions (FRF’s). An indirect estimation technique is used for the estimation of unmeasured FRF’s, which are required for identification of joint parameters but are not available. With the help of an index function, which is devised for indicating the quality of estimation or identification at a particular frequency, the frequency region appropriate for identification is selected. Experiments are conducted on two different machine tool spindles. Repeatable and accurate joint coefficients are obtained for both machine tool systems. [S0022-0434(00)02501-6]

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