Research on Special Purpose Machine Tool Used for Ladder-Shaped Micro-Hole Automation Machining

2014 ◽  
Vol 556-562 ◽  
pp. 1034-1037
Author(s):  
Yang Zhou ◽  
Xiao Dong Fu ◽  
Pei Feng ◽  
Chong Chang Yang
Keyword(s):  
Tool Wear ◽  
Machine Tool ◽  
Existing Problems ◽  
Micro Hole ◽  
Hole Machining

this paper introduced the existing ladder-shaped micro hole machining method, analyzed the existing problems of present micro hole machining equipment; solved some key problems such as how to clean up the iron filings in micro blind holes rapidly and completely, compensate tool wear and other problems; And finally developed a special purpose machine tool for machining ladder-shaped micro hole, which can achieve fully automated machining.

Experimental Study on Ultrasonic Vibration-Assisted Grinding of High Volume Fraction SiCp/Al Composites Micro-Hole

2016 ◽  
Vol 1136 ◽  
pp. 359-364
Author(s):  
Shou Xiang Lu ◽  
Hang Gao ◽  
Chao Sun ◽  
Yong Jie Bao ◽  
Zi Cheng Bao
Keyword(s):  
Experimental Study ◽  
Tool Wear ◽  
Ultrasonic Vibration ◽  
Thrust Force ◽  
Volume Fraction ◽  
High Volume ◽  
Micro Hole ◽  
Ultrasonic Vibration Assisted Grinding ◽  
Hole Machining ◽  
Conventional Drilling

Aiming at the difficulty of 65% volume fraction SiCp/Al composites micro-hole machining and the problem of exit defects, the ultrasonic vibration-assisted grinding (UVAG) method for micro-hole machining was experimental studied taking Φ2-3mm hole for example. Results indicated that the thrust force was reduced by 79.8% through UVAG with lower fluctuation and the tool wear was much less than conventional drilling (CD). Lower thrust force and better exit quality were obtained with the increasing of the ultrasonic amplitude.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

scholarly journals Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3817 ◽  
Author(s):  
Xuefeng Wu ◽  
Yahui Liu ◽  
Xianliang Zhou ◽  
Aolei Mou
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Automatic Detection ◽  
Milling Process ◽  
Face Milling ◽  
Machining Process ◽  
Model Parameters

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

Research of Micro Hole Vibration Drilling Process and Experiment

2011 ◽  
Vol 697-698 ◽  
pp. 161-165 ◽  
Author(s):  
Peng Zhang ◽  
Xing Yu Guo ◽  
Cheng Ge Wu
Keyword(s):  
Manufacturing Industry ◽  
Defense Industry ◽  
Hole Drilling ◽  
Drilling Process ◽  
Drilling Machine ◽  
Micro Hole ◽  
The Common ◽  
Position Precision ◽  
Vibration Drilling ◽  
Hole Machining

It is always the difficulties for micro hole machining in the machine manufacturing industry, even more in the defense industry. The vibration drilling new craft, with the incomparable advantages in micro hole drilling, is different from the common one. The precision NC micro hole vibration drilling machine is developed, and the micro hole drilling experiments are conducted. The vibration drilling can not only improve the drill life more than ten times compared with the normal one, but also improve the centering ability and position precision.

A New Approach to Blade Design With Constant Rake and Relief Angles for Face-Hobbing of Bevel Gears

2015 ◽  
Vol 138 (3) ◽  
Author(s):  
Mohsen Habibi ◽  
Zezhong Chevy Chen
Keyword(s):  
Tool Wear ◽  
Machine Tool ◽  
Computer Aided Design ◽  
Cutting Edge ◽  
Wear Characteristics ◽  
Bevel Gears ◽  
Cutting Velocity ◽  
Productive Process ◽  
Aided Design ◽  
Cutting System

Face-hobbing is a productive process to manufacture bevel and hypoid gears. Due to the complexity of face-hobbing, few research works have been conducted on this process. In face-hobbing, the cutting velocity along the cutting edge varies because of the intricate geometry of the cutting system and the machine tool kinematics. Due to the varying cutting velocity and the specific cutting system geometry, working relief and rake angles change along the cutting edge and have large variations at the corner which cause the local tool wear. In this paper, a new method to design cutting blades is proposed by changing the geometry of the rake and relief surfaces to avoid those large variations while the cutting edge is kept unchanged. In the proposed method, the working rake and relief angles are kept constant along the cutting edge by considering the varying cutting velocity and the machine tool kinematics. By applying the proposed method to design the blades, the tool wear characteristics are improved especially at the corner. In addition, in this paper, complete mathematical representations of the cutting system are presented. The working rake and relief angles are measured on the computer-aided design (CAD) model of the proposed and conventional blades and compared with each other. The results show that, unlike the conventional blade, in case of the proposed blade, the working rake and relief angles remain constant along the cutting edge. In addition, in order to validate the better tool wear characteristics of the proposed blade, finite element (FE) machining simulations are conducted on both the proposed and conventional blades. The results show improvements in the tool wear characteristics of the proposed blade in comparison with the conventional one.

Vibration Drilling the 0Cr18Ni9Ti Stainless Steel Micro-Hole

2012 ◽  
Vol 217-219 ◽  
pp. 1592-1595 ◽  
Author(s):  
Peng Zhang ◽  
Chang Hong Mei ◽  
Xing Yu Guo
Keyword(s):  
Stainless Steel ◽  
Hole Drilling ◽  
Drilling Process ◽  
Drill Bit ◽  
Drilling Force ◽  
Process Mechanism ◽  
Micro Hole ◽  
Vibration Drilling ◽  
Hole Machining

Austenite 0Cr18Ni9Ti stainless steel is one of difficult-to-cut materials. It has poor dilling process, especially for micro-hole machining. The main reasons are the tiny drill, poor rigidity, easy to deviation. Moreover, the chip is difficult to discharge, so the drilling force is increased and the drill bit is easy to break, or even it is impossible for micro-hole drilling. In this paper, the vibration drilling process is adopted. The vibration drilling 0Cr18Ni9Ti stainless steel micro-hole process mechanism is researched. The stainless steel micro-hole drilling experiments are conducted. The results show that the vibration drilling can be a better solution for 0Cr18Ni9Ti stainless steel micro-hole processing.

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.

Sign in / Sign up

Export Citation Format

Share Document