Barrel finishing process is a universal method to improve the surface quality of parts. It is widely used in high-performance parts of high-end equipment. As a necessary tool consumable for barrel finishing process, the characteristic parameters of the abrasive blocks affect the processing quality and production efficiency. However, the current method for selecting the abrasive blocks requires large number of experiments based on the operator’s extensive experience, which does not meet the rapid development needs of the barrel finishing process. Therefore, this paper proposes a case-based reasoning model with variable weights to achieve the intelligent optimization of the abrasive blocks. Based on the in-depth analysis of the characteristics of the barrel finishing process, a reasonable case base is established firstly, which is to determine the comprehensive case features and the solution of the case. AHP (analytic hierarchy process) is proposed to determine the weight of case features and to dynamically adjust the weight of case features according to the characteristics of the parts to be processed and users’ processing requirements. The results show that the proposed case-based reasoning model with variable weights can quickly, accurately, and reasonably select the abrasive blocks during the process of making processing technique of the barrel finishing, which will lay a necessary foundation for the effective implementation of the barrel finishing process and contribute significantly to the improvement of its efficiency.
Barrel finishing technology is a basic manufacturing technology in the field of machining. This technology aims to improve the surface quality and integrity of parts. It belongs to the category of precision and ultraprecision machining [
Connotation of the barrel finishing.
With the continuous expansion of the barrel finishing process market, the demand for rapid response to the R&D and promotion of professional enterprises engaged in barrel finishing technology is increasing [
In the application of barrel finishing process, the abrasive blocks are the key factor affecting the processing ability, processing effect, and processing efficiency [
In 1982, Roger C. Schank of Yale University put forward the “dynamic memory” theory based on “memory organization packets” in the book Dynamic Memory [
CBR has attracted the attention of many scholars due to its advantages of solving problems simply, quickly, and efficiently. Through its development of more than 30 years, the application of CBR has continuously evolved from a single field to multiple fields. Ahn et al. [
This paper proposes a case-based reasoning technology to reasonably select the abrasive blocks during the barrel finishing process, instead of relying on expert knowledge.
Because each case feature has different importance, different case features have different weights. According to the different processing requirements, a weighted case-based reasoning technique based on analytic hierarchy process (AHP) is proposed. Figure
The diagram of the abrasive blocks optimization based on the weighted case-based reasoning.
As shown in Figure
Case construction is the first step to the problem using CBR. Appropriate case representation can reflect the essential characteristics of the solved problems and make the case retrieval system quickly retrieve the desired cases in the case base, thereby improving the efficiency of case retrieval [
Case representation should have a good organizational structure to facilitate querying and storage while improving the query speed and accuracy. Generally, a typical case includes the feature representation of the case and the result of the case [
E-R diagram of the abrasive blocks optimization.
According to the experimental reports and actual factory production data, the characteristics of the required parts are determined, including the type of parts, the material of the parts, the size of the parts, and the characteristics before processing (roughness, brightness, burr, hardness, etc.). The processing requirements are set for the specific indicators (roughness, brightness, burr, hardness, residual stress, etc.,) of the parts after processed. The main indexes of the abrasive blocks are the material, shape, and size. Therefore, the case of optimum selection of abrasive blocks includes the case features (i.e., characteristics of the parts to be processed and the processing requirements) and the case solution (i.e., the characteristics of abrasive blocks). Because the dimensional parameters of different types of parts are different, the case base is built according to the type of parts in case construction. The optimum case structure of the abrasive blocks is shown in Figure
Case structure of the abrasive blocks optimization.
Feature hierarchy model of the gear parts.
In case-based reasoning, different factors have different effects on the final results. These factors are important features in the process of case retrieval. It is necessary to set different weight values for different features so that the features with larger impacts have a larger weight in order to reduce the impact of secondary features and improve the accuracy of retrieval matching [
The AHP is a combination of qualitative, quantitative, systematic, and hierarchical analysis method proposed by Professor Saaty. The steps to determine the weight of case features using the AHP are as follows: First, there are many factors that affect each other of the decision-making problems in a large system. To hierarchize these problems, a multilayer analytical structure model is formed. Through the analysis of the case features of barrel finishing, after the material and type of parts are determined by classification search, a three-layer structure model can be established. Taking gear parts as an example, the case feature hierarchy model is shown in Figure The weights of each index in the comprehensive evaluation are different at the criterion layer and the scheme layer. Based on the 1–9 scale method and expert knowledge, a two-to-two comparison judgment matrix is constructed. Obtain the comparative judgment matrix The scale definition of the comparative judgment matrix is shown in Table Generally, the comparative judgment matrix is a positive and reciprocal matrix, which has the following properties: Through expert opinions and the analysis of actual processing data of the barrel finishing process, the relative importance of the case features is obtained. Then, the comparative evaluation of The maximum eigenvalues and the corresponding eigenvectors of the comparison judgment matrices are calculated. The formula is as follows: where
The scale definition of the comparative judgment matrix.
Scale | Meaning |
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1 | Indicates that index |
3 | Indicates that index |
5 | Indicates that index |
7 | Indicates that index |
9 | Indicates that index |
2, 4, 6, 8 | Represents the median value of the above adjacent evaluations |
Criteria layer judgment matrix.
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1 | 1/3 | 1/2 |
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3 | 1 | 2 |
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2 | 1/2 | 1 |
Gear type parts size judgment matrix.
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1 | 1/2 | 1/3 |
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2 | 1 | 1/2 |
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3 | 2 | 1 |
The characteristics of the parts judgment matrix.
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1 | 2 | 3 | 4 |
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1/2 | 1 | 3 | 3 |
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1/3 | 1/3 | 1 | 2 |
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1/4 | 1/3 | 1/2 | 1 |
Processing requirement judgment matrix.
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1 | 2 | 3 | 4 | 5 |
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1/2 | 1 | 3 | 3 | 4 |
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1/3 | 1/3 | 1 | 2 | 3 |
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1/4 | 1/3 | 1/2 | 1 | 2 |
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1/5 | 1/4 | 1/3 | 1/2 | 1 |
The steps of using the geometric averaging method to calculate Let the vector where ( To make the calculation results consistent with the actual situation, it is necessary to check the consistency of where If The consistency ratio CR indicates the degree of consistency of the comparison judgment matrix. When CR < 0.1, it is considered that For matrix For matrix For matrix For matrix To obtain the total weight of each case feature to the target layer, it is necessary to calculate the weight of each layer index to obtain the total objective of the system and check the consistency. The calculation method is as follows:
Average random consistency index.
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
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RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 |
From Figure
If the consistency index for criterion layer
According to the CI and RI of the judgment matrix of each layer calculated above, the total consistency ratio CR is calculated as 0.0267 by formula (
Case matching is used to retrieve and select potentially available cases from the source case base using retrieval information and to make a reasonable evaluation of the similarity between new cases and stored cases so that the retrieved cases can be used to solve new problems. The similarity measurement is a key cornerstone of CBR [
According to the feature matching of parts processing cases, the steps of case retrieval are as follows: where SIM (
According to the case description information in the barrel finishing test reports, the data types of the case features can be divided into three classes: numerical type, fuzzy logic type, and switch type. The similarity calculation methods of different data types are as follows: The formula for calculating the similarity of numerical case features [ where The size, original roughness, original burr, original hardness before machining, roughness after machining, and residual stress all are the numerical data. The similarity is calculated by using formulas ( The formula for calculating the similarity of case features of fuzzy logic type is [ where According to the national standard of brightness, the brightness attributes have four grades: distinguishable processing trace direction, no brightness, low and no abrasion, and very high brightness. They are assigned to the corresponding values of 1, 2, 3, and 4. The similarity of luminance can be calculated by formula ( The formula for calculating the similarity of switch-type case features [
The “burr” parameter in some “processing requirement” is usually described by “yes” or “no.” In this case, formula (
Case processing includes case revision, case application, and case preservation. According to formula ( If the similar case is unique, the abrasive blocks of the case will be directly applied If there are many similar cases, the experts will perform an analysis and make a decision regarding the abrasive blocks
If the similar case cannot be retrieved, case revision is needed. According to the importance of case features, this paper chooses case features with weights greater than
Through case matching and case revision, the abrasive blocks for new parts are selected to process the new parts. After processing, the case needs to be evaluated to determine whether the case can be stored in the case base. This evaluation adopts the method of “postevaluation of the processing effect.” The optimized abrasive blocks are applied to machine the new parts. If the processing effect of the new parts meets the processing requirements, the case will be retained to the case base; otherwise, it will be retained to the abandoned case base.
A friendly man-machine interface is aimed to be designed for easy operation and real-time display of the process parameters of the abrasive blocks optimization, which adopts the weighted case-based reasoning. This paper designs the optimization interface of the abrasive blocks with the procedure shown in Figure
The interface of the abrasive blocks optimization.
As shown in Figure
Many simulation researches are carried out using the actual data of the gear parts, shaft parts, and blade parts of a factory. In the similarity calculation formula, the parameter
For ease of analysis, the case information is represented by the following symbols: case number (No.), material number (
Through a large number of simulations, it is determined that the change in the
When the threshold
The existing cases in the case base are selected for testing. For example, No. 16 of gear parts is selected as the test case, which is an existing case in the case base. The simulation results are shown in Table
Simulation results of the existing case of gear parts.
No. |
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Ra1 ( |
B1 (mm) | Br1 | H1 (HV) | Ra2 ( |
Rs (MPa) | Br2 | H2 (HV) | B2 | Similarity | Abrasive blocks |
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16 | Alloy steel | 83 | 206 | 100 | 0.618 | 1.9 | 1 | 50 | 0.515 | −250 | 4 | 55 | No | No. 3 rough blocks | |
16 | Alloy steel | 83 | 206 | 100 | 0.618 | 1.9 | 1 | 50 | 0.515 | −250 | 4 | 55 | No | 1 | No. 3 rough blocks |
18 | Alloy steel | 64 | 200 | 80 | 0.594 | 1.873 | 1 | 48 | 0.487 | −245 | 4 | 52 | No | 0.762 | No. 3 rough blocks |
As seen from Table
Select the case base similar to certain cases to test. For example, No. 13 of gear parts is selected as the test case which is similar to certain cases in the case base. The simulation results are shown in Table
Simulation result of similar case of gears.
No. |
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Ra1 ( |
B1 (mm) | Br1 | H1 (HV) | Ra2 ( |
Rs (MPa) | Br2 | H2 (HV) | B2 | Similarity | Abrasive blocks |
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13 | 20CrMnTiH | 42 | 109 | 44 | 0.376 | 1.38 | 1 | 48 | 0.355 | −215 | 4 | 53 | No | Triangle 2 | |
11 | 20CrMnTiH | 42 | 116 | 44 | 0.384 | 1.380 | 1 | 48 | 0.379 | −210 | 4 | 55 | No | 0.819 | Triangle 2 |
10 | 20CrMnTiH | 66 | 128 | 46 | 0.368 | 1.397 | 1 | 48 | 0.357 | −205 | 4 | 52 | No | 0.766 | Triangle 2 |
As seen from Table
To verify the accuracy of the similarity threshold selection in this paper, some cases that are quite different from those in the case base are selected for testing. For example, No. 6 of gear parts is selected as the test case in the case base, and the simulation results are shown in Table
Simulation result of a case with substantially different gears.
No. |
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Ra1 ( |
B1 (mm) | Br1 | H1 (HV) | Ra2 ( |
Rs (MPa) | Br2 | H2 (HV) | B2 | Similarity | Abrasive blocks |
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6 | Cast steel | 48 | 135 | 54 | 0.66 | 1.9 | 1 | 55 | 0.464 | −255 | 4 | 63 | No | No. 3 rough blocks | |
2 | Cast steel | 5 | 32 | 13 | 0.565 | 1.8 | 3 | 47 | 0.351 | −240 | 4 | 51 | No | 0.675 | No. 3 rough blocks |
4 | Cast steel | 21 | 95 | 30 | 0.478 | 1.457 | 1 | 40 | 0.323 | −267 | 4 | 55 | No | 0.622 | Triangle 3 |
1 | Cast steel | 21 | 80 | 20 | 0.453 | 1.458 | 1 | 46 | 0.351 | −257 | 4 | 53 | No | 0.620 | Triangle 3l |
7 | Cast steel | 60 | 269 | 130 | 0.842 | 1.32 | 2 | 42 | 0.467 | 265 | 4 | 50 | No | 0.597 | No. 3 medium mill |
As shown in Table
Simulation results of case correction with substantially different gears.
No. |
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Ra1 ( |
B1 (mm) | Br1 | H1 (HV) | Ra2 ( |
Rs (MPa) | Br2 | H2 (HV) | B2 | Similarity | Abrasive blocks |
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6 | Cast steel | 48 | 135 | 54 | 0.66 | 1.9 | 1 | 55 | 0.464 | −255 | 4 | 63 | No | No. 3 rough blocks | |
2 | Cast steel | 5 | 32 | 13 | 0.565 | 1.8 | 3 | 47 | 0.351 | −240 | 4 | 51 | No | 0.745 | No. 3 rough blocks |
7 | Cast steel | 60 | 269 | 130 | 0.842 | 1.32 | 2 | 42 | 0.467 | 265 | 4 | 50 | No | 0.560 | No. 3 medium mill |
4 | Cast steel | 21 | 95 | 30 | 0.478 | 1.457 | 1 | 40 | 0.323 | −267 | 4 | 55 | No | 0.526 | Triangle 3 |
1 | Cast steel | 21 | 80 | 20 | 0.453 | 1.458 | 1 | 46 | 0.351 | −257 | 4 | 53 | No | 0.519 | Triangle 3 |
As shown in Table
As shown in Figure
Comparison of similarity before and after case revision.
In this paper, the optimal selection model of the abrasive blocks is established by using the weighted case-based reasoning method to realize the intelligent optimal selection of the abrasive blocks in barrel finishing. The determination of related parameters in this method is emphatically discussed. The implementation of this method is simple and easy to use. Using the actual machining parts data for verification, this method not only can meet the machining requirements but also can quickly and accurately select the abrasive blocks used to machine the new parts. Case-based reasoning is a suitable method for the optimal selection of abrasive blocks. And expert knowledge is spreading and accumulating with the continuous improvement of the case base, and the accuracy of the optimal selection of abrasive blocks is also improving, which will lay a necessary foundation for the effective implementation of the barrel finishing process and contribute significantly to the improvement of its efficiency.
However, due to the complexity of the barrel finishing process and numerous factors affecting the optimal selection of the abrasive blocks, it is necessary to optimize the parameters of abrasive blocks optimization and further improve the construction of the case base in the future work.
The gear parts data used to support the findings of this study have been deposited in the figshare repository (
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This study was supported by Shanxi Scholarship Council of China (2017–032), Key Research and Development (R&D) Projects of Shanxi Province of China (201903D121057), and Key Project of Natural Science Foundation of Shanxi Province of China (201801D111002).