A study on knowledge mining and reuse for non-standard tool design based on deep belief network

doi:10.11996/JG.j.2095-302X.2026020411

Abstract

Abstract:

In the design of non-standard tools, a strongly coupled correlation between tool features and part-machining features is identified as a typical type of implicit design knowledge. It exhibits data multimodality and multi-dimensional uncertainty, leading to difficulties in capture and reuse. Therefore, a method for tool design knowledge mining and reuse based on a Deep Belief Network (DBN) was proposed. First, targeting the two-modal data of 2D images and attribute texts associated with machining features and tool features, a dual-channel DBN was designed to perform feature extraction and fusion. Second, a DBN oriented to correlation mining was designed to obtain implicit relationships between machining features and tool features. Finally, existing tool cases were evaluated and reused through association-rule reasoning and an improved Rake algorithm. Taking the design process of a non-standard special inner-hole-groove tool as an example, the effectiveness of the method was verified by comparing the reuse results with the actual results in terms of structural and attribute information.

Key words: non-standard tool, deep belief network, implicit knowledge mining, multimodal fusion, cross-modal correlation

CLC Number:

TG71
TP183

WANG Mingwei, ZHAO Jianhua, SUN Zhihong, SUI Peng, LU Xiaojun. A study on knowledge mining and reuse for non-standard tool design based on deep belief network[J]. Journal of Graphics, 2026, 47(2): 411-422.

Figures/Tables 17

Fig. 1 Non-standard tool design knowledge mining and reuse process based on deep learning

Fig. 2 The structure of tool feature extraction and fusion network

Fig. 3 The structure of processing feature extraction and fusion network

Fig. 4 DBN model for tool feature-machining feature association mining

Fig. 5 Tool design knowledge reuse process

Fig. 6 Part of the tool structure feature pixel map

Fig. 7 Attribute information corresponding to some tools

Fig. 8 Pixel map of partial processing features

Fig. 9 Attribute information corresponding to some processing features

Fig. 10 Extraction of machining features and reconstruction error of fusion network ((a) Structural feature extraction network; (b) Attribute feature extraction network; (c) Feature fusion network)

Fig. 11 Reconstruction error curves of the last layer RBM in local feature fusion networks of the cutting tool ((a) Feature fusion network of tool insert connection part; (b) Feature fusion network of tool insert and tip part; (c) Feature fusion network of tool head structure part; (d) Feature fusion network of tool rake face structure part; (e) Feature fusion network of tool shank cross-section part)

Fig. 12 Association relationship mining network reconstruction error curve diagram

Fig. 13 Machining characteristics of inner hole groove

Table 1 The similarity value of each local feature similar case

案例名	案例编号	刀片连接	刀片与刀尖	刀头构造	前刀面结构	刀杆截面	刀具整体
x-T0855	1	0.743	0.664	0.765	─	0.679	2.084 2
x-T1012	2	0.717	─	─	─	0.701	1.059 1
x-ZD0700	3	─	─	─	0.726	─	0.538 3
x-T0857	4	0.682	─	0.692	─	0.668	1.517 2
x-VL8010	5	─	0.823	0.728	0.735	─	1.631 3
x-S2269	6	─	0.759	─	─	─	0.528 8
x-T0762	7	─	─	─	0.641	─	0.484 2

Fig. 14 Blade connection reuse results ((a) Actual design results; (b) Recommended examples)

Fig. 15 The reuse results of blade and tool tip structure ((a) Actual design results; (b) Recommended examples)

Table 2 Physical parameters of recommended cases

实例	材料	加工精度	是否涂层	前角/后角/主偏角/刃倾角	刀片连接		刀片与刀尖
实例	材料	加工精度	是否涂层	前角/后角/主偏角/刃倾角	宽	刀片宽	刀尖半径	刀尖角度
0	42CrMo	IT8	是	7/7/20.5/3.0	18.0	9.50	1.75	90
1	42CrMo	IT8	是	7/7/45.0/3.0	17.1	7.98	1.50	90
2	42CrMo	IT7	是	─	12.0	─	─	─
3	42CrMo	IT8	否	─	─
4	42CrMo	IT8	是	10/10/30.0/3.5	20.8
5	42CrMo	IT7	是	7/7/20.0/3.0	─	9.00	1.75	90
6	42CrMo	IT8	是	─	─	9.60	0.50	90
7	42CrMo	IT8	否	─	─	─	─	─

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