Fuzzy modelling of machine-tool cutting process (original) (raw)
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Fuzzy logic for modeling machining process: a review
Artificial Intelligence Review, 2013
The application of artificial intelligence (AI) techniques in modeling of machining process has been investigated by many researchers. Fuzzy logic (FL) as a well-known AI technique is effectively used in modeling of machining processes such as to predict the surface roughness and to control the cutting force in various machining processes. This paper is started with the introduction to definition of FL and machining process, and their relation. This paper then presents five types of analysis conducted on FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process. Literature showed that milling contributed the highest number of machining operation that was modeled using FL. In terms of machining performance, surface roughness was mostly studied with FL model. In terms of fuzzy components, center of gravity method was mostly used to perform defuzzification, and triangular was mostly considered to perform membership function. The reviews extend the analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors. The analysis leads the author to conclude that FL is the most popular AI techniques used in modeling of machining process.
Toward intelligent machining: hierarchical fuzzy control for the end milling process
IEEE Transactions on Control Systems Technology, 1998
The difficulties in implementing adaptive and other advanced control schemes in industrial machining processes have encouraged researchers to combine the utilization of one hierarchical level, a fuzzy control algorithm, and robust sensing systems. The main idea of this paper deals with self-regulating controllers (SRC's). The control signal's scaling factor (output scaling factor) is self-regulated during the control process, and it can assure the optimum gain setting for the hierarchical fuzzy controller. An important role in this strategy is performed by a robust sensing system based on current sensors. For comparison, the CNC-PLC's own control loops, a hierarchical fuzzy controller based on look-up tables, and the hierarchical fuzzy controller with a self-regulating output scaling factor GC are studied. The performances of these controllers are compared. The results indicate that the hierarchical fuzzy controller with a self-regulating output scaling factor yields the best performances among them. The index known as the metal removal rate is increased, and the in-process time is reduced by 50%. Thus, higher production rates are obtained. The hierarchical fuzzy controller is equipped with three basic requirements: flexibility, low cost, and compatibility with any CNC manufacturer.
Consideration of Fuzzy Components for Prediction of Machining Performance: A Review
Procedia Engineering, 2011
This paper presents the application of artificial intelligence techniques especially fuzzy logic (FL) in predicting machining performance. FL is chosen because it is widely used to predict the machining performances such as surface roughness, cutting force and material removal rate. Previous works on FL focusing on fuzzy components has been presented. The FL components are fuzzification, fuzzy rule, inference engine and defuzzification. The review shows that the FL components for fuzzification, which is logical operator, membership function (MF) and IF-THEN rule, is the necessary facts that must be considered before applying FL in prediction. Fuzzy rule that is derived from fuzzification process is important in the development of inference engine. Therefore, the defuzzification of the inference engine will give desired fuzzy system. The review also revealed that there are several types of defuzzification which include centroid, bisector, smallest of maximum, mean of maximum and largest of maximum. There are important facts that must be considered in FL development. To conclude, this paper revealed that MF and defuzzification is important in predicting machining performance. It shows that for MF and defuzzification, triangular and centroid are respectively mostly used in the prediction process.
Fuzzy Logic Modeling For Peripheral End Milling Process
IOP Conference Series: …, 2011
Fuzzy logic has been deployed in this study to predict cutting speed and feed rate ofperipheral end milling process at given hardness of material, radial depth of cut and cutterdiameter. There were two types of fuzzy models had been designed and developed throughout in this study. The first developed fuzzy model (Model A) was two inputs with two outputs while the second developed model (Model B) was three inputs with two outputs. Hardness of material and radial depth of cut had been chosen as the inputs for the Model A. Cutter diameter then had been introduced as the third input besides material hardness and radial depth of cut for Model B. Two types of fuzzy model were designed to evaluate the effectiveness and efficiency of introducing cutter diameter as another input into the system. Both types of fuzzy model had been tested and validated with the recommended data obtained from Machining Data Handbook (MDH). The results showed a very good correlation between predicted data and the data from MDH. Model B had been chosen as the best fuzzy model to represent peripheral end milling process although Model A had performed better; 3.78% and 2.06% (Model A) compared to 3.81% and 2.27% (Model B) for cutting speed and feed percentage errors respectively. This is due to less development time and the ability to predict cutting speed and feed at any given cutting tool diameter.
Generalized fuzzy model for metal cutting data selection
Journal of Materials Processing Technology, 1999
Metal cutting data selection is complex and cannot be easily formulated to meet design specification by any mathematical model. Optimized Machinability data is obtained from a skilled machine tool operator's experience and intuition. A rule-based expert system and materials database have been incorporated into many CAD/CAM systems in order to obtain optimal machining parameters. Fuzzy logic is a better tool to describe the strategy and action of the skilled operator when selecting the metal cutting data. A first prototype of such a system was developed by the present authors. This paper further describes development of fuzzy models and their feasibility. Development of several models for different cutting tools is presented and discussed. The models are validated with the Machining Data Handbook. The feasibility of a generalized fuzzy model for all the cutting tools is also presented. (M.A. El Baradie) 0924-0136/99/$ -see front matter © 1999 Elsevier Science S.A. All rights reserved. PII: S 0 9 2 4 -0 1 3 6 ( 9 9 ) 0 0 1 2 7 -2
Fuzzy Logic Models for Selection of Machining Parameters in CAPP Systems
Fuzzy logic is a mathematical theory of inexact reasoning that allows us to model the reasoning process of humans in linguistic terms. It is very suitable in defining the relationship between the system inputs and the desired system outputs. This paper presents fuzzy logic models to select machining parameters (cutting speed and feed rate) in automated process planning (CAPP) systems. Each model utilizes two-input and two-output variables which are partitioned into several fuzzy sets according to their minimum and maximum values allowed to control the model. A set of fuzzy rules have been constructed for each model, based on the knowledge extracted from machining data handbooks. Once the rules are evaluated the variables are defuzzified and converted into the corresponding output variables (cutting speed and feed rate). An example is given to demonstrate and verify the application of the developed fuzzy models. The results obtained are compared with the corresponding ones obtained from machining data handbook and shown good fit.
THE IRAQI JOURNAL FOR MECHANICAL AND MATERIALS ENGINEERING
In turning operation, numerous parameters are utilized to analyze machinability. Parameters,for instant, tool wear, tool life, cutting temperature, machining force components, powerconsumption, surface roughness, and chip thickness ratio are frequently utilized. The goal ofthis work is to model the effect of cutting parameters (cutting speed, depth of cut and feedrate) on the machining force and chip thickness ratio during turning ductile aluminum 1350-O. Four fuzzy logic models were built to model the relationship between cutting parametersand the three force components of machining force and the chip thickness ration. The inputsto all fuzzy logic models are cutting speed, depth of cut and feed rate. Whereas, the outputfor first, second, third and fourth models are cutting force, passive force, feed force and chipthickness ratio, respectively. All fuzzy models showed good match to the experimental dataand the computed correlation coefficients were larger than or equal 0.9998. Those...
An Approach on Fuzzy and Regression Modeling for Hard Milling Process
This paper proposes the prediction of cutting temperature, tool wear and metal removal rate using fuzzy and regression modeling techniques for the hard milling process. The feed per tooth, radial depth of cut, axial depth of cut and cutting speed were used as process state variables.The experiements were conducted using RSM based central composite rotatable design methodology. Regression and fuzzy modeling were used to evaluate the input – output relationship in the process. It is interesting to observe that the R2 and average error values for each response are very consistent with small variations were obtained.Also, the confirmation results show that very less relative error varitions. Thus, the developed fuzzy models directly integrated in manufacturing systems to reduce the more computational complexity in the process planning activities.
2010
Problem statement: Most Nickel based Hastelloy C-276 is a difficult-to-machine material because of its low thermal diffusive property and high strength at high temperature. Machinability consideration of nickel based Hastelloy C-276 in turning operations has been carried out using ceramic inserts under dry conditions. Approach: This study described a modification approach applied to a fuzzy logic based model for predicting cutting force where the machining parameters for cutting speed ranges, feed rate, depth of cut and approach angle are not overlapping. For this study, data were selected depending on the design of experiments. Response surface methodology was applied to predict the cutting force and to examine the fuzzy logic based model. Results: The modification approach fuzzy logic based model produced the cutting force data providing good correlation with response surface data. In this situation the cutting force data were superimposed and results were adjusted according to their own ranges. Conclusion: A review of literatures on optimization techniques revealed that there were, in particular, successful industrial applications of design of experiment-based approaches for optimal settings of process variables.