Modeling of instantaneous cutting force for large pitch screw with vibration consideration of the machine tool

2020 ◽  
Vol 108 (11-12) ◽  
pp. 3893-3904
Author(s):  
Zhe Li ◽  
Xiangfu Fu ◽  
Chao Li ◽  
Bin Jiang ◽  
Minli Zheng
Keyword(s):  
Machine Tool ◽  
Cutting Force

Modeling and Simulation of Process and Structure Interactions Considering Turning Operations

2009 ◽  
Author(s):  
Michael F. Zaeh ◽  
Florian Schwarz
Keyword(s):  
Machine Tool ◽  
Cutting Force ◽  
Metal Cutting ◽  
Material Behavior ◽  
Force Model ◽  
Machine Tool Structure ◽  
Simulation Based ◽  
Turning Center ◽  
Simplifying Assumptions ◽  
Input Variables

A consideration of the dynamic interaction between the machine tool structure and the cutting process is required for the prediction and optimization of machining tasks through simulation. This paper outlines a modular, analytical cutting force model applicable to common turning processes. It takes into account the dynamic material behavior and nonlinear friction ratios on the rake face as well as heat transfer phenomena in the deformation zones. In order to overcome simplifying assumptions in analytical cutting force descriptions and to incorporate the chip formation process into the analysis, specific input variables are determined in a metal cutting simulation based on the Finite Element Method (FEM). On the machine tool structure side, the setup of a parametric FEM model is presented. The accuracy of both the machine tool and cutting force models was verified experimentally on a turning center.

Effect of Workpiece Location on Manipulability Measure in 5-Axis-Controlled Machine Tools

2010 ◽  
Vol 4 (3) ◽  
pp. 268-272 ◽  
Author(s):  
Yoshio Mizugaki ◽  
Keyword(s):  
Machine Tool ◽  
Cutting Force ◽  
Machine Tools ◽  
Inverse Kinematics ◽  
Computational Results ◽  
Computer Aided Manufacturing ◽  
End Effector ◽  
Computer Aided ◽  
Manipulability Measure ◽  
Work Table

This paper clarifies the effects of workpiece location in a 5-axis-controlled machine tool from the viewpoint of Inverse kinematics including Manipulability measure: an index representing the variance of movement of end-effector in a serial linkage. Firstly the importance of Inverse kinematics in Computer Aided Manufacturing is emphasized and then Singularity and Manipulability measure are expanded for multiaxis-controlled machine tools. Secondly the computational results of Manipulability measure for different workpiece locations and tool orientations show that setting the workpiece in the centre of the rotary work-table is most preferable. Regardless of large differences in Manipulability measure at different locations, there were few differences of the resultant cutting force in machining experiments. Finally the brief conclusion is mentioned.

Simulation Technologies for the Development of an Autonomous and Intelligent Machine Tool

2013 ◽  
Vol 7 (1) ◽  
pp. 6-15 ◽  
Author(s):  
Keiichi Shirase ◽  
◽  
Keiichi Nakamoto ◽  
Keyword(s):  
Fault Detection ◽  
Machine Tool ◽  
Cutting Force ◽  
Milling Process ◽  
Planning System ◽  
Cutting Force Prediction ◽  
Intelligent Machine ◽  
Milling Operation ◽  
Digital Copy ◽  
Copy Milling

An autonomous and intelligent machine tool have been developed to solve fundamental issues with the current command method using NC programs, and simulation technologies for its realization have been introduced. The process planning system introduced here, various process plans can be created, and the best process plan can be selected to achieve flexible machining operations in accordance with changes in production planning. Digital Copy Milling, digitizing the principle of copy milling, has opened up new possibilities for machine tool control. The NC machine tool can be directly controlled with the 3D CAD data of the product shape in Digital CopyMilling. Direct machining without the need to create an NC program before milling operation, adaptive control which changes the cutting conditions in accordance with the cutting load during milling operation, and fault detection in the cutting load and avoiding tool breakages can be performed through Digital Copy Milling. Themilling process simulator with integrated milling shape simulator and cutting force simulator provides new functions. Simultaneous cutting force prediction with milling operation provides the possibility of milling process control and fault detection by comparing the measured cutting force with the predicted one.

Development of an Intelligent Stage with Sensor-Less Cutting Force and Torque Monitoring Function

2012 ◽  
Vol 6 (6) ◽  
pp. 736-741 ◽  
Author(s):  
Takafumi Kamigochi ◽  
◽  
Yasuhiro Kakinuma ◽  
Keyword(s):  
Tool Wear ◽  
Machine Tool ◽  
Cutting Force ◽  
Process Monitoring ◽  
Good Condition ◽  
Basic Information ◽  
Monitoring Method ◽  
Intelligent Machine

Intelligent machine tool is required to implement highprecision process monitoring for judging the abnormal tool conditions. Various techniques have been widely researched and studied to maintain machine tool in good condition and to detect tool wear. The occurrence of tool wear can be detected by monitoring the cutting torque, which is basic information for machining. The purpose of this study was to propose a sensor-less cutting force and torque monitoring method and to develop an intelligent stage using this method.

A Structured Linear Quadratic Gaussian Based Control Design Algorithm for Machine Tool Controllers Including Both Feed Drive and Process Dynamics

2005 ◽  
Vol 11 (7) ◽  
pp. 949-983 ◽  
Author(s):  
E. M. Elbeheiry ◽  
W. H. Elmaraghy ◽  
H. A. Elmaraghy
Keyword(s):  
Machine Tool ◽  
Cutting Force ◽  
End Milling ◽  
Cross Coupling ◽  
Control Input ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Design Algorithm ◽  
Design And Optimization ◽  
Model Driven

A new extension of the stochastic linear quadratic Gaussian (LQG) regulator problem is developed and used for the design of new suboptimal cross-coupling controllers for machine tool drives. This new extension allowed us to combine both the drive and the cutting dynamics into a unified model driven by the static and the dynamic portions of the cutting force. The dynamic portion of the cutting force is considered as a stochastic random process in end milling contouring processes. The outputs of the axes are corrected by the cutting tool deflections which result from the cutting force-workpiece resistance interactive dynamics. Most importantly, the LQG extension developed here is directly applicable to the design and optimization of centralized, decentralized, and hierarchical machine tool controllers that have previously appeared in the literature. This is possible because our extension allows the assignment of a different control structure for each control input even if more than one control input are contributing to the same axis. Furthermore, the method admits each controller to function in any chosen subset of the available measurements. Thus, it provides us with a powerful means for designing any of the above-mentioned controllers using the same approach. The results of our suboptimal cross-coupling controllers were magnificent when compared to the commercially available positioning controllers.

Development of Innovative Intelligent Machine Tool Based on CAM-CNC Integration Concept – Adaptive Control Based on Predicted Cutting Force –

2019 ◽  
Vol 13 (3) ◽  
pp. 373-381
Author(s):  
Isamu Nishida ◽  
Ryo Tsuyama ◽  
Keiichi Shirase ◽  
Masahiro Onishi ◽  
Katsuyuki Koarashi ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Machine Tool ◽  
Cutting Force ◽  
Real Time ◽  
Machining Efficiency ◽  
Intelligent Machine ◽  
Computer Aided ◽  
Machine Control ◽  
Digital Copy ◽  
Copy Milling

A new methodology to generate instruction commands for prompt machine control as a replacement for the previously prepared numerical control (NC) programs is developed to realize an innovative intelligent machine tool. This machine tool can eliminate NC program preparation, achieve cutting process control, reduce the production lead time, and realize an autonomous distributed factory. In this study, the innovative intelligent machine tool based on the computer-aided manufacturing-computer NC integrated concept is developed. The special feature of this system is to generate instruction commands in real time for prompt machine control instead of using NC programs. Digital Copy Milling, which is a digitized version of traditional copy milling, is realized by using only the computer-aided design model of the product. In this system, the cutting-force simulation is performed simultaneously with the real-time tool path generation. Then, the tool feed rate can be controlled according to the predicted cutting force. Therefore, both the improvement of the machining efficiency and the avoidance of machining problems can be achieved. The instantaneous cutting force model predicts the cutting force. In this system, the work material is represented by the voxel model, and the uncut chip thickness is calculated discretely from the number of voxels removed. Thus, it is possible to predict the cutting force in the case of non-uniform contact between the tool and the work material. In this study, a machining simulation is conducted to validate the proposed method. The results of the simulation show successful tool feed speed adaptation based on the predicted cutting force. The results also show the effective reduction of the machining time. A case study of a custom-made product for dental prosthetics is examined as a good application of both the proposed adaptive control and the Digital Copy Milling system. Through this method, it is possible to improve the machining efficiency and prevent tool breakage.

scholarly journals Stacked Auto-Encoder Based CNC Tool Diagnosis Using Discrete Wavelet Transform Feature Extraction

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 456
Author(s):  
Jonggeun Kim ◽  
Hansoo Lee ◽  
Jeong Woo Jeon ◽  
Jong Moon Kim ◽  
Hyeon Uk Lee ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Machine Tool ◽  
Discrete Wavelet Transform ◽  
Cutting Force ◽  
Condition Based Maintenance ◽  
Discrete Wavelet ◽  
Tool Condition ◽  
Diagnosis Model ◽  
Force Data

Machining processes are critical and widely used components in the manufacturing industry because they help to precisely make products and reduce production time. To keep the previous advantages, a machine tool should be installed at the designated place and condition of the machine tool should be maintained appropriately to working environment. In various maintenance methods for keeping the condition of machine tool, condition-based maintenance can be robust to unpredicted accidents and reduce maintenance costs. Tool monitoring and diagnosis are some of the most important components of the condition based maintenance. This paper proposes stacked auto-encoder based CNC machine tool diagnosis using discrete wavelet transform feature extraction to diagnose a machine tool. The diagnosis model, which only uses cutting force data, cannot sufficiently reflects tool condition. Hence, we modeled diagnosis model using features extracted from a cutting force, a current signal, and coefficients of the discrete wavelet transform. The experimental results showed that the model which uses feature data has better performance than the model that uses only cutting force data. The feature based models are lower false negative rate (FNR) and false positive rate. Moreover, squared prediction error using normalized residual vector also reduced FNR because normalization reduces weight bias.

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