Prediction System of Magnetic Abrasive Finishing (MAF)
On the Internal Surface of Cylindrical Tube
Ching-Lien Hung1, Lieh-Dai Yang2+, Wei-Liang Ku3, Han-Ming Chow4
Submitted to
March, 2010
This paper has not been published elsewhere nor has it been submitted for publication elsewhere.
1+, 2Department of Industrial Engineering and Management, Nan Kai Universtiy of Technology, Nan Tou, Taiwan, R.O.C.
3Department of Information Management, Nan Kai Universtiy of Technology, Nan Tou, Taiwan, R.O.C.
4Department of Mechanical Engineering, Nan Kai Universtiy of Technology, Nan Tou, Taiwan, R.O.C.
+Corresponding authorAbstract
This study mainly used the way of the magnetic abrasive finishing (MAF) to explore the cylindrical tube of stainless steel SUS304 related to the processing characteristic and the prediction system. The self-make adjustable electricity polishing mechanism was assembled on the magnetic abrasive machine. The magnetic abrasive which was consisted of the sintered iron and Aluminum Oxide powder filled in the cylindrical stainless steel tube. Magnetic abrasive in the electromagnetic field was absorbed on the cylindrical tube to become flexible magnetic brush. It could generate adjustable pressure on the work piece surface when the magnetic brush is grinding, it could make the workpiece face polished to the mirror surface degree.
This experiment used the non-magnetic stainless steel SUS304, following experimental design to conduct the experiments and to explore the effects of various parameters such as rotational speed, vibration frequency, current strength, abrasive, etc., to the surface finish characteristics. After statistical analysis, ANOVA was obtained, and then surface finish prediction system was constructed based on the significant parameters, and the system precision was about 97%. The system will be further to develop an adaptive control model for MAF in a real fashion.
Key words: Magnetic abrasive polishing; flexible magnetic brush, experimental design, prediction model, surface finish.
1. Introduction
The rapid development of the semiconductor, biotechnology, and optical electronic industries has increased the importance of geometrical precision and part surface quality. Finishing is regularly applied to parts to obtain precise surfaces. Hence, numerous finishing techniques have been applied for finishing parts to obtain parts with high quality. These techniques include chemical mechanical polishing (CMP), electrical polishing (EP), and many others. However, both CMP and EP suffer from the formation of pollutants during its operations, and also yield surfaces with limited quality. Consequently, researchers in the industry and academics have attempted to develop a better means of obtaining a high-precision surface, with low cost, high efficiency, easy operations and low environmental pollution.
Following recent technological developments, stainless steel materials with characteristics of anti-oxidizing, anti-corrosive, and shiny surface have been applied in electronic, biochemical and medical instrumentation equipments. The surface of stainless steel parts must be extremely smooth to prevent pollution. Optimally, the surface finish can reach a level in that it looks like a mirror. A smooth stainless steel surface not only improves the parts quality but it also prevents rusting and staining of the parts surface. Finished parts can prevent the occurrence of the following situations: powder particles remaining on the part surfaces, contact between parts and the stainless steel surface, rough surfaces residing with oil dusk or food particles, and stainless steel burr of processed parts falling off when two parts contact each other.
Stainless steel is a soft, tough, and a difficult finishing material. Thin plate stainless steel that uses traditional processes is not easy to achieve a good surface finish. Hence, manual finishing was usually applied to achieve a surface finish that looks like a mirror. However, it is very time consuming to achieve a good surface finish using manual finishing techniques for stainless container steel surfaces.
To resolve the above problems, magnetic abrasive finishing (MAF) was recently created. MAF involves using a permanent magnet or an electronic magnet to generate a magnetic field, and the magnetic abrasives are formed as a flexible magnetic brush for pressing the workpiece [1, 2]. Thus, the magnetic brush becomes a finishing tool, and the magnetic abrasives of the magnetic brush stick to the workpiece during the finishing. Moreover, the frictional force generated by the abrasive finishing can remove particles of free-form surface. The procedure is repeated until a desired surface finish is attained.
When a permanent magnet was installed on the topside of the workpiece, any uneven or concave areas on the part could be finished [3-5]. Moreover, when the magnetic pole was installed inside or outside of the part, the internal and external pipes could also be finished [6]. Therefore, MAF is a multi-function precise finishing method. Workpiece materials can be magnetic (such as steel) or non-magnetic (such as ceramic), and the material removal weight can also be adjusted based on the size of the magnetic abrasives. The finishing pressure is controlled via the magnetic field, so MAF is used for micro-pressure finishing [7, 8]. Thus, the MAF method achieved a highly efficient way of obtaining a good surface finish.
This study attempts to develop a surface finishing technique for stainless steel, with the aim of analyzing the effects of different parameters and constructing the prediction system for the development of a further adaptive control system. Secondly, this investigation seeks to enhance surface finish of parts in order to meet the customer requirements.
2. Magnetic abrasive finishing
2.1 Fundamental principle
Magnetic abrasive finishing (MAF) of free-form surfaces involves filling the gap between the circular magnetic pole and the workpiece with the magnetic abrasives. The magnetic abrasives consist of sintered pure iron powder (99.9% Fe) and Al2O3. The end face of the magnetic pole absorbs the magnetic abrasives and forms a closed-loop magnetic field with the workpiece holder. The magnetic abrasives are generated in a non-uniformly magnetic field; in which the abrasives will join each other and follow the direction of the magnetic force to form a flexible magnetic brush. Refer to Figure 2-1 to see how the magnetic brush acted on the free-form surface. The magnetic force lines generated power to apply pressure from the magnetic abrasives to the workpiece, and the magnetic brush became a tool for finishing the workpiece. Moreover, the magnetic abrasives in the magnetic brush stick to the workpiece. When the magnetic pole rotates and moves with the workpiece relatively, the frictional force generated from MAF cause the abrasives to finish the particles of uneven or free-form surfaces until it becomes smooth. Moreover, the magnetic brush continues to move on the x-y-z direction of the CNC machine, brushing the workpiece until it meets the customer’s requirements.
3. Experimental mechanism, designs and results
3.1 MAF mechanism
This investigation involved the MAF mechanism illustrated in Figure 3-1. Using a permanent magnet generated the magnetic force; the magnetic field formed a closed loop due to the interaction of the permanent magnet, magnetic abrasives, workpiece, and workpiece holder (S10C steel). The magnetic flux density was close to 1.2 Tesla in a 1.0 mm working gap (distance between magnetic pole and workpiece holder). The S pole of the magnet was established with a shank installed in the spindle of the CNC machine. Meanwhile, the N pole of the magnet was designed to absorb the magnetic abrasives. The magnetic pole had an external diameter of 20 mm and a length of 40 mm . Furthermore, the N pole with a 10 mm radius ball shape was processed into four grooves with sizes of 1.5 mm width and 10mm depth to reduce the ball area of the magnetic pole and boost the magnetic field strength for achieving an efficient finishing.
3.2 Experimental designs
Objectives of the experimental design, was first to determine which parameters are most influential on the part surface. Then, to determine where to set the influential parameters so that part surface is almost always near the desired target value. In this study, four possible parameters which are spindle speed (S), vibration frequency (F), discharge current(C), abrasive weight ratio (A) considered and used to conduct experiments. In the experiment, four factors with each three levels were selected respectively as Table 3-1. After the experiments, the collected data (Table 3-2) were analyzed statistically and the ANOVA (Table 3-3) showed the results. After the analyzing, the significant parameters would be applied to develop a surface finish prediction system for the MAF operations using the collected data.
4. The Proposed S-FN-IPSFP System
Figure 4-1 illustrates the proposed statistical-assisted fuzzy-nets in-process surface finish prediction (S-FN-IPSFP) system. The inputs of the system were spindle speed (S); vibration frequency (F); discharge current (C); and abrasive weight ratio (A). The predicted variable of the system was the predicted surface finish (Rmax).
In order to generate a system with fuzzy-nets theory, a five-step approach of constructing the rule bank was introduced as follows.
Step 1: Divide the input space into fuzzy regions.
The input vectors are: spindle speed (S); vibration frequency (F); discharge current (C); and abrasive weight ratio (A). The ranges for each input variable were: spindle speed (S) [S+, S-] rpm, vibration frequency (F) [F+, F-] times/sec, discharge current (C) [C+, C-] amp, and abrasive weight ratio (A) [A+, A-], where S+and S- were the maximum and minimum values of the spindle speed (S) in all experimental data, respectively. The range of the output variable surface finish (Rmax), was [Rmax +, Rmax -], where Rmax + and Rmax - were the maximum and minimum values of Ra in all experimental data, respectively. Thus, the input feature vector X and “domain intervals” were given as:
The domain interval indicated which variable would most likely lie within the interval based on experience. Each interval was divided into 2K+1 regions, which were denoted by SK, S(K-1), ….MD, ….L(K-1), and LK.
The shape of each membership function was triangular and the spread width (W) of each triangular function was the same.
Step 2: Generate fuzzy rules for the given data pairs.
The fuzzy-nets training procedure was based on the input and output signals collected from the experiment. The signal obtained from the control system generated the input feature vector X (Eq.3-1). The output surface finish (Rmax) indicated the output vector of the system. The following example of fuzzy degree of the input variable (Fi) was determined in different regions. The function is given as:
where c(Fi) and W(Fi) indicate the center point and the spread width of the input linguistic variable Fi (e.g. S2, S1, MD, L1 or L2); and i is the index of regions, in which i = 2K+1.
Step 3: Avoid conflicting rules.
It was possible to have two or more conflicting rules from the experiments, i.e., rules that have the same IF part but a different THEN part. Top-down and bottom-up methodologies were proposed to resolve this conflict (Lou [11]). The top-down methodology assigned a degree to each rule. The degree of rule i {IF S is MD, F is L1, C is L1, and A is L1, THEN Rmax is MD} is defined as:
where mE is the data pair degree assigned by a human expert based on the data collection condition.
Step 4: Statistical assisted fuzzy-nets rule base.
From previous steps, the conflicting rules have been resolved, and it was very likely that the rule base had empty rules that needed to be filled. To fill the empty rules, a multiple linear regression (MLR) of the experimental data was used to assist filling the empty rules in order to build the fuzzy-nets rule base.
Step 5: Determine a mapping based on the fuzzy rule base.
After the fuzzy rules were developed, the following defuzzification strategy was used to determine the output Ra for a given input data set (S, F, C and A). The antecedents of the ith fuzzy rule used the minimum operation in order to determine the degree,
where
where c(Rmaxi) denotes the center value of region Rmaxi and m would be the number of fuzzy rules in the combined fuzzy rule base, t denotes fuzzy rule number t.
5. System verification and conclusions
Once the statistical-assisted fuzzy-nets in-process surface finish prediction (S-FN-IPSFP) system was constructed, the testing data shown on Table 5-1 was tested, and the results were summarized as followings:
1. Four possible parameters which are spindle speed (S), vibration frequency (F), discharge current(C), abrasive weight ratio (A) are all significant influences on surface finish in MAF processes.
2. This research uses 81 experimental data to establish the fuzzy-nets prediction model and 10 sets of experimental test data were conducted, the accuracy of the system was about 97%. Therefore, the proposed statistical-assisted fuzzy-nets in-process surface finish prediction (S-FN-IPSFP) system could be applied to the next step of adaptive control system development of MAF processes in a real time fashion.
Reference
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