Frontiers in Computing and Intelligent Systems 
ISSN: 2832-6024 | Vol. 8, No. 1, 2024 

 

134 

Research	on	the	Application	of	CBR	Technology	in	
Intelligent	Process	Design	System	
Junli Liu, Hui Lu *, Guanhui Cui, Xibin An 

School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, Henan, China 

* Corresponding author: Hui Lu (Email: 212105020011@home.hpu.edu.cn) 

 

Abstract: A Case-Based Reasoning (CBR) intelligent process design system is developed through Visual Studio development 
tools to improve the processing efficiency of mechanical parts and the recurrence rate of corporate knowledge. The key factor in 
improving the accuracy of case matching in the CBR system is the similarity calculation of parts. In this paper, similarity 
calculation models for different attribute types are presented by combining the nearest neighbor method. And the improved AHP 
method and matrix calculation of MATLAB are used to determine the weighting coefficient. The most similar cases are matched 
according to the overall similarity of the cases and the set threshold, and the method is applied to the intelligent process design 
of shafts. The results show that this method is conducive to shortening the development cycle and quickly responding to the 
market, which provides a reference for intelligent manufacturing of mechanical parts. 

Keywords: Intelligent Process; Case-Based Reasoning; Similarity; Improved Analytic Hierarchy Process; Weight Coefficient. 
 

1. Introduction 
In mechanical product design, process design is the bridge 

that connects designers and manufacturing floors. The design 
efficiency of the part process directly affects the production 
cycle of the product. The design efficiency of the part process 
directly affects the production cycle of the product. In the face 
of complex process systems, the reuse of similar processes 
based on product three-dimensional models and enterprise 
knowledge management has become the mainstream means 
of design of many enterprises. Case-Based Reasoning (CBR) 
is a similar problem-solving method that uses the existing 
knowledge and cases of enterprises, and is undoubtedly the 
preferred method for dealing with complex problems and 
multi-attribute decisions[1]. 

 
New issue 
description

case retrieval

Retrieve the 
workaround

Remediate the 
workaround

Study  cases 

Instance libraries

1.retrieval

2.reuse

3.modification

4.retention

 
Figure 1. Case-Based Reasoning diagram 

 
CBR technology solves current problems by comparing the 

differences between new and old problems, modifying and 
adjusting the cases and reusing the knowledge and 
information of old cases[2]. A complete CBR solution should 
include retrieval, reuse, modification, and retention of cases, 
as shown in Figure 1. Taking the belt conveyor roller shaft 
parts as an example, an intelligent process design system 

based on case reasoning was developed. According to the 
overall similarity of the cases and the set threshold, the most 
similar cases are matched, which is used to guide the design 
of new cases, and provides a fast process design method for 
mechanical parts, thereby improving the knowledge reuse rate 
and process design efficiency of mechanical parts in 
enterprises. 

2. Establishment and Solution of CBR 
Model 

In the life cycle of CBR, case retrieval is an important part 
of case reasoning, and CBR is based on the idea that "similar 
problems have similar solutions"[3]. Therefore, in CBR 
calculation, the key to case retrieval lies in the calculation of 
similarity, and the case similarity calculation process mainly 
includes two aspects: Evaluate the similarity between the 
target case and the historical case on each feature item, that is, 
the local similarity; Evaluate overall similarity on the basis 
of local similarity[4]. 

1

( , ) ( , )
n

j j j
j

Sim X C f X C W


             (1) 

In the formula, X represents the target case; C represents 
the mature case; n indicates the number of feature attributes 
contained in the case; f represents the similarity calculation 
function between case C and target case X; Wj represents the 
weight of the feature attribute J. According to Equation (1), it 
can be seen that attribute similarity calculation and weight 
assignment will affect the matching accuracy of the case and 
the reliability of the final decision[5]. Figure 2 shows the 
overall framework of the similarity calculation solution. 
Different similarity calculation models are established 
according to the accurate numerical type, text type and feature 
set type. By using Delphi method combined with the 
improved AHP method, the weight of each attribute is 
calculated. The global similarity of each case is obtained, and 
the best similar case is finally matched. 



 

135 

3. Calculation Model for Attribute 
Similarity 

In this paper, K-Nearest Neibhdor (K-NN) is used to 
describe the similarity between case attributes. It determines 
similarity by calculating the spatial distance of attribute 
features. and provide highly similar cases to designers for 
reuse[6]. In this paper, the process parameters such as the 
category, blank, material, heat treatment method, basic 
parameter set (shaft length, maximum outer diameter, 
roughness, main machining accuracy) and shape feature set 
(number of main features and number of auxiliary features) 
of shaft parts are described. Since the process attribute 
information involves types mainly include numeric, text and 
set. Therefore, their similarity calculation function f (Xj, Cj) is 
also different. The following will establish the similarity 
calculation model for each type of attribute in the shaft part. 

 

Numerical similarity 
calculation model

Text-based similarity 
calculation model

Feature set similarity 
calculation model

Delphi method 
combined with 
improved AHP 
method Weight 
coefficient 
calculation

Similarity matrix

Weight coefficient 
calculation

Overall similarity

Best Similarity Case

Figure 2. Intelligent design architecture of mechanical products

3.1. Similarity Calculation of Numeric Types 
When the attribute is a deterministic numeric type, such as 

the basic process parameters of the axis class (shaft length, 
maximum outer diameter, roughness, main machining 
accuracy), the spatial distance between the two is usually used 
as the similarity calculation method. 

1

1

( , ) 1 ( , ) 1 ( , )
n r

i i i i ij
j

Sim X C Dis X C w Dis x c


 
     

 
    (2) 

In the formula, when r = 1, Dis(X, Ci) represents the 
Hamming distance; When r = 2, Dis(X, Ci) represents the 
Euler distance. 

Where Dis(X, Ci) is expressed as: 

 min max
max min

( , ) , , ,
j ij

j ij j ij

x c
Dis x c x c x x

x x


 


       (3) 

In the formula, xmax represents the maximum value of the 
case attribute j, and xmin represents the minimum value of the 

case attribute. When Dis(X, Ci)=0, it means that the properties 
j of the two cases are the same, and the similarity is 1; 
Otherwise, the two properties are completely different and 
have a similarity of 0. 

3.2. Similarity Calculation of Text Types 
In this system, some properties of the case are described by 

text, such as part name, stock type, heat treatment method and 
material, collectively referred to as text type. For text class 
shapes, the similarity is calculated by the following formula: 

0,
( , )

1,

j ij

j ij
j ij

x c
Sim x c

x c

  
                (4) 

3.3. Similarity Calculation of Feature Set 
Types 

Due to the complex geometric features of the structure of 
shaft parts, the structure will involve shape features such as 
bosses, slots, and chamfers. Therefore, the geometric features 
are described as sets such as primary features and auxiliary 
features, as shown in Figure 3.  

Prima-
ry 

feature

Auxili-
ary 

features

Basic feature

Raised 
features

Ladder 
features

Transition 
features

Hole features

Groove 
features

sphere

cube

Cylindrical 
bulge

Rectangular 
bulge

Rectangular 
steps

Inclined steps

Chamfer

Rounded 
corners

Cylindrical 
holes

Conical holes

T-notch

Square groove

cylinder

cone

Geomet-
ric 

features

 
Figure 3. Classification of shape features 

 
The similarity of the shape feature set is judged and 

calculated by the primary and auxiliary features of the part. 
The part shape feature set similarity formula defined by this 
system is: 

0 .7 0 .3x zx fxS im S im S im            (5) 

In the formula, 0.7Simzx represents the similarity coefficient 
of the primary feature of the current part and the case part. 
0.3Simfx indicates the similarity coefficient of the auxiliary 
features. 

If the main feature set of the target part is O, the primary 
feature set of the case part is P, and O=[o1,o2,...,on], 

P=[p1,p2,...,pm], A=OP, then the primary feature similarity 

of the two parts is: 
2 ZA

zx
ZO ZP

Q
Sim

Q Q


               (6) 



 

136 

In the formula, QZO represents the number of major 

features of the target part, QZP represents the number of major 

features of the case part, and QZA is the number of 
intersections of the two. Similarly, the formula for calculating 
auxiliary features Simfx can be obtained, which will not be 
repeated here.  

4. Establishment of Attribute Weight 
Coefficient Calculation Model 

The size of the weight coefficient reflects the importance 
of each feature attribute of the case, which has a direct impact 
on the result of CBR matching. The Analytic Hierarchy 
Process (AHP) is a decision-making method that decomposes 
complex problems into multiple levels and conducts 
qualitative and quantitative analysis. This method evaluates 
the importance of attributes by constructing a judgment 
matrix, which provides a selection basis for decision-making. 
However, the calculation is more complex due to the need for 
consistency testing in traditional AHP. Therefore, it can 
improve the objectivity of judgment and omit consistency 
testing by introducing optimal transfer matrix to improve 
traditional AHP. The calculation steps to improve the AHP are 
as follows: 

4.1. Constructing the Judgment Matrix 
The judgment matrix is the core of the entire operation and 

directly affects the evaluation results[7]. The questionnaire 
was administered to P experts and the questionnaire data were 
synthesized into a judgment matrix A, by judging the 
importance of the guidelines according to the events as in 
Table 1. 

12 1

21 2

1 2 1

1

1
ij

n

n

n n

a a

a a
A

a a

 
 
 
 
 
  






   


                  (7) 

1

1
( ) p

p
r

ij ij
r

a a


                       (8) 

 
Table 1. The importance of the event judgment criterion 

The importance of attribute i 
over attribute j 

Pairwise comparison 
standard 

extremely important 9 

more important 7 

significantly important 5 

slightly more important 3 

equally important 1 

slightly less important 1/3 

significantly less important 1/5 

Strongly unimportant 1/7 

Extremely unimportant 1/9 

The importance level falls between 
the two 

8,6,4,2,1/2,1/4,1/6,1/8 

 

In the formula, ɑij indicates the results of the fusion of 
multiple expert opinions, and ɑij = 1/ɑij 
ɑij

r is the r-th expert to determine the importance value of 
the i-th attribute relative to the j-th attribute. P is the number 
of experts and n is the number of case attributes. 

4.2. Optimization Consistency Matrix 
In the traditional hierarchical analysis method, the 

consistency index CI, the average random consistency index 
RI and the consistency ratio CR must be used to test the 
consistency of the matrix, in order to satisfy the coordination 
between the elements of the judgment matrix and avoid the 
inconsistency of the importance between the elements that 
affect the decision results[8],[9]. 

However, with the optimization of the calculation method, 
the consistency check process can be replaced by constructing 
a consistency matrix. that is, the judgment matrix A belongs 
to the antisymmetric matrix, B = lgA; If matrix C is an optimal 
transfer matrix of A, then A*=10c is the quasi optimal 
consistent matrix of judgment matrix A. Due to the 
consistency of this matrix, the traditional consistency 
verification process is omitted. 

                     (9) 

1

1
( )

n

ij ik jknxn
k

C c b b
n 

                 (10) 

* c10A                     (11) 

4.3. Determination of Weighting Coefficients 
The methods to calculate the weights include sum method, 

product method and eigenvalue method, and this paper adopts 
the sum method to solve for the weight coefficients. The 
specific steps are as follows. 

Firstly, normalize each column element of the quasi-
optimal uniform matrix, denoted as: D=[dij]nxn 

*

*

1

ij

n

ij
i

a
D

a





                 (12) 

Then, sum the elements of matrix D in rows, denoted as: 
vector group:  1 2, , ...,

T

nC c c c  

1

, (1, 2, ..., )
n

i ij
j

c d i n


           (13) 

Finally, the column elements in the vector group C are each 
divided by n to obtain the weight vector Wi. 

*

*1

1

1 n
ij

i n
j

ij
i

a
W

n
a



 


                (14) 

 1 2, , ...,i nW w w w  , (1, 2, ..., )i n  , W are the weight 
coefficients of each attribute. 

4.4. Implementation of Attribute Weight 
Coefficients 

Based on the model structure of the improved analytic 
hierarchy method described above, the weight coefficient of 
case attributes is calculated by using MATLAB software. 
Taking the decision matrix given by four experts as an 
example, according to Equations (7) and (8), the calculated 
similarity matrix A is as follows. 

lg   ij ijnxn
B b A



 

137 

 

1 1 0.4518 0.4083 0.2686 0.2272

1 1 0.4083 0.4083 0.3102 0.2272

2.2134 2.4495 1 1 0.4518 0.25

2 2 1 1 0.4518 0.2887

3.7224 3.2237 2.2134 2.2134 1 0.4518

4.4006 4.4006 4 3.4641 2.2134 1

A

 
 
 
 

  
 
 
 
  

 

According to the weight calculation formulas (9) to (14) of 
the improved AHP method, combined with MATLAB 
programming, the weight coefficients of the case properties 
can be obtained: shape feature set (0.3887), basic parameter 
set (0.2316), heat treatment method (0.1287), material 
(0.1236), roughcast (0.0639) and part category (0.0635). 

 0.3887 0.2316 0.1287 0.1236 0.0639 0.0635iW   

5. Application Cases 
This system is mainly based on the secondary development 

of SolidWorks API and Visual Studio software. By using 
feature recognition technology and CBR inference technology, 
it can meet the rapid design of the process. Finally, an 
intelligent process design system based on CBR is developed, 
which realizes the information extraction of 3D process 
models and intelligent matching of process cases. The overall 
operation interface of the system is shown in Figure 4 and 5. 

Taking the design of the roller shaft as an example, the 
similarity calculation model and weight calculation method 
are applied to this design scheme. Table 2 shows the contents 
of the case library of the roller shaft, and the attribute types 
include text, numeric, and feature types, so it can be solved 
using the global similarity model. By storing mature cases in 
the database, the numbers C1-C5 are partial cases, C= {c1, 
c2,...,c5}, and the case characteristic properties are described 
by U={u1,u2,...,u10} 

 
Table 2. Historical Case Feature Attribute Values 

Cases 
Part 

category 
roughcast material 

heat 
treatment

Shaft 
length

Maximum 
outer 

diameter 
roughness

Machining 
accuracy 

Number 
of main 
features

number 
of 

auxiliary 
features

C1 shaft Bar stock 45 tempering 2350 140 0.8 7 13 11
C2 shaft Bar stock 40cr quench 100 50 0.4 6 8 6
C3 shaft Bar stock 45 tempering 2258 146 3.2 9 11 12
C4 shaft Bar stock 35 anneal 4300 450 6.3 5 18 12
C5 shaft Bar stock 45 anneal 2105 160 3.2 7 10 9

(1) Target part information extraction and output. By the 
function interfaces of Feature, GetFirstAnnotation2 and 
GetCustomProperty, the feature traversal and information 
extraction of the eight process parameters of the target part 
are carried out in the SolidWorks software. That is, click the 
"Information Extraction" button, the system extracts the 
attribute information of the category, roughcast, material, heat 
treatment, shaft length, maximum outer diameter, roughness, 
machining accuracy, number of main features, and number of 
auxiliary features of the target part. The information is 
automatically output to the system application interface, as 
shown in Figure 4, for the next intelligent process retrieval by 
the designer. 

(2) Retrieval and matching of similar process cases. Based 

on the similarity calculation model and attribute weight 
calculation method studied above, the similarity calculation 
between the extracted target case attribute information and the 
case in the database is calculated. By clicking the " Retrieval 
" button and setting a threshold, match the cases that meet the 
requirements and sort them by overall similarity, as shown in 
Figure 5. 

(3) Modification of the case and output of the process card. 
From the system interface, it can be seen that the most similar 
process case (97.18%) is reused. After modification and 
adjustment, the final output is a process card that matches the 
current target part. This enables the reuse of similar process 
cases and the rapid design of part processes. 

 

 
Figure 4. Information extraction of the target part 

 



 

138 

 
Figure 5. The cases retrieval results 

 

6. Conclusion 
This paper studies the problems of mechanical parts that 

cannot effectively use enterprise resources and insufficient 
rapid response ability to the market when designing the 
process. Taking the roller shaft of belt conveyor as an example, 
the description and expression of different attribute 
information in shaft process cases are given, and a global 
similarity calculation model is established. By combining 
CBR technology and feature recognition technology, an 
intelligent process design system was developed. The 
feasibility of applying this method to the process system is 
verified by case studies, and the retrieval and reuse of cases 
are realized. Thus, the process design efficiency of shaft parts 
is greatly improved, and it provides a reference for the 
research of intelligent process design of mechanical parts. 

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