Table 2 Comparison of different modeling methods
From: Development of Fixture Layout Optimization for Thin-Walled Parts: A Review
| Â | Methods | Scope of application | Advantages | Limitations | References | ||
|---|---|---|---|---|---|---|---|
Mechanism-based modeling methods | Jacobian matrix method | Single-station process Robust design Rigid assumption | Simple calculation Direct reflect the influence of fixture deviations on part variations | Applicable to rigid assumptions Limited for complex problems | Cai et al. [25], Cai [5], Xing et al. [26] and Lu et al. [27] | ||
State space method | Multi-station process Variation propagation Considering source variations | Suitable for multi-station assembly Considering various source variations | Cannot be used for complex problems Complicated derivation and calculation | Masoumi et al. [10], Kim and Ding [11], Tian et al. [13], Huang et al. [14], Xie et al. [15], Jin and Shi [30], Ding et al. [31], Zhang and Shi [32, 33], Chaipradabgiat et al. [34], Tyagi et al. [35] | |||
FEM | Considering deformations caused by force Compliant assumption | Intuitively reflect the deformations at nodes Easy to understand its principle | Large amount of calculation Calculation accuracy depends on FEA software | Du et al. [23], Haynes and Lee [36], DeVries and Menassa [37], Zhong and Hu [38], Chen et al. [39], Liao et al. [40], Vishnupriyan et al. [41], Kumar and Paulraj [42], Wu et al. [43], Hajimiri et al. [44], Xiong et al. [45], Yang et al. [46], Dou et al. [47], Wen et al. [48], Wu et al. [49], Liu and Hu [50], Aderiani et al. [51], Sayeed et al. [52, 53] | |||
Data-based modeling methods | Regression modeling methods | RSM | Large amount of calculation Multiple input variables | Simple calculation | Reduce the amount of calculation Require a small amount of data | Model accuracy depends on data and parameters Requirements for experimental design and data sampling Time for parameter optimization | Sundararaman et al. [54, 57], Li et al. [55, 56], Xia et al. [58], Yu et al. [59] |
SVR | Good generalization ability | ||||||
Kriging | Stable accuracy | ||||||
PLSR | Suitable for multiple outputs | Bi et al. [19] | |||||
Grey model | Low requirements for data | Yang et al. [69] | |||||
ANN methods | BPNN | Large amount of calculation Multiple input variables | Relatively simple structure | Reflecting the complex relationship between inputs and outputs More accurate | Need a large amount of data Not interpretable Long time to obtain the model | Selvakumar et al. [70], Rex and Ravindran [71], Qin et al. [72, 73], Ramachandran et al. [74] | |
RBFNN | Fast convergence Global approximation | ||||||