An In-Process Neural Networkbased Adaptive Surface Roughness Control (INN-ASRC)
System in End Milling Operations
即時類神經網路可調式表面粗糙度的控制
物品表面粗糙度的要求
Business Behavior
□change from production trend to customer trend
□Delight the customer needs
□
How to enhance both quality producitvity?
In-Process Monitoring System
在製程中同時得到品質所要的參數
精實生產減少浪費,檢查動作屬於浪費
■Computerized inspection利用電腦來量測
■Analyze within each machining process
■example
tool breakage detection
tool wear monitoring
surface roughness predition
斷刀→鈍刀→表面粗糙度 可回推刀子好或不好
The Important of Surface Roughness(Ra)
A key factor in evaluating the quality of product
Accept a coating
Resist fatigue
Distribute and hold a lubricant
斷刀如能即時偵測,則可將刀具life最佳化
A factor to monitor the maching processes
Asignal of material non
傳統量法 The Measurement of Ra
Stylus profiler-measure off-line
Purpose of Study
Develop an in-process neural network-based adaptive surface roughness control(INN-ASRC)system
Real-time surface roughness prediction
Real-time adjust the machining parameters to create the desired surface roughness
讓系統決定參數
The (INN-ASRC)system
in→Machining parameters sSensing signals
The Factors of Determining Ra
Controllable factors
Machining parameters:Spindle speed, feed rate & depth of cut
Uncontrollable factors →Sensing technology
Sensing technology
Milling operations
AE,ultrasonic and optical ard limited by the reflection angle
In-process Decision-making Algorithms
Analyze the input information
Learning ability
Regression model
Neural network(NN)類神經網路
Neural-fuzzy modeling
INN-SRP System
Training to obtain the weights
Train-and-error
The layers and neurons of hidden level
The learning rate and momentum factor
了解要幾個隱藏層?要幾個神經節點?要去訓練他
假設在百分之九十可信度下
預測值與客戶要求標準之差異
讓類神經告訴我們參數要調少?
Training to obtain the weights
Can Fuzzy Logic Work ?
What is Fuzzy Logic
Fuzzy logic is applied for solving the imprecise, linguistic or vague message
Structure
fuzzy set
membership function
fuzzy relations
fuzzy reasoning
defuzzifucation
模糊理論最重要的是要建立一個 假如a是。。b是。。c是。。。則結果是。。。
input資料輸入試算產生一個結果
困難的地方是在rule back
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