Development of a Neural-Fuzzy Model for Machinability Data Selection in Turning Process
A neural-fuzzy model has been developed to represent machinability data selection in turning process. Turning process is a branch of machining process, which is used to produce cylindrical parts. Considerable efforts have been done to automate such machining process in order to increase the efficien...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English |
| Published: |
2008
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/5759/1/A__ITMA_2008_5.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A neural-fuzzy model has been developed to represent machinability data selection in turning process. Turning process is a branch of machining process, which is used to produce cylindrical parts. Considerable efforts have been done to automate such machining process in order to increase the efficiency and precision of manufacturing. One of the issues is machinability data selection, which is always referred as the proper selection of cutting tools and machining parameters. This task is a complex process; and usually depends on the experience and skill of a machinist. Although sources like machining data handbooks and tool catalogues are available for reference, the process is still very much depending on a skilled machinist.
Previously, mathematical and empirical approaches have been attempted to reduce the dependency. However, the complexity of machining makes it difficult to formulate a proper model. Applications of fuzzy logic and neural network have been considered too to solve the machining problem; and have shown good potential. But, some issues remain unaddressed. In fuzzy logic, among the issues are tedious process of rules identification and inability to self-adapt to changing machining conditions. On the other hand, neural network has the issues of black box problem and difficulty in optimal topology determination. In order to overcome these difficulties, a neural-fuzzy model is proposed to model machinist in selecting machinability data for turning process. The neural-fuzzy model combines the self-adapting and learning abilities of neural network with the human-like knowledge representation and explanation abilities of fuzzy logic into one integrated system. The characteristics of fuzzy logic would solve the shortcomings in neural network; and vice versa.
Generally, the developed neural-fuzzy model is designed to have five layers; input and output layers, and three hidden layers. Each of the layers has different classes of nodes; in which are input nodes, input term nodes, rule nodes, output term nodes and output nodes. The model is developed using Microsoft Visual C++ .NET (MSVC++ .NET). Object oriented approach is applied as the development process to enhance reusability.
The results from the model have been validated and compared against machining data of Machining Data Handbook from Metcut Research Associate. Good correlations have been shown, indicating the feasibility of representing machining data selection with neural-fuzzy model. The mean absolute percentage error for four different types of tools is below 3%, and averaging at 2.4%. Apart from that, the extracted fuzzy rules are compared with the general rules of thumbs in turning process as well as rules from other paradigm; and found to be consistent. This would simplify the task of obtaining fuzzy rules from machining data. Beside that, the model is compared with other artificial intelligence approaches, such as fuzzy logic, neural network and genetic algorithm. The neural-fuzzy model has shown good result among them. In addition, the characteristics of the model are studied and analyzed as well; in which include membership functions, shouldered membership functions and randomness.
This research has shown promising results in employing neural-fuzzy model to solve problems; in this case, machinability data selection in turning process. The developed neural-fuzzy model should be further considered in a wider range of real-world machining processes for learning and prescribing knowledge. |
|---|