An intelligent method to generate liaison graphs for truss structures

Abstract

Liaison graph is a necessary prerequisite of assembly sequence planning for mechanical products. Traditionally, it is generated via shape matching of joints among parts, but this strategy is invalid to truss structures because they lack patterns for shape matching. In this context, this article presents an intelligent method based on support vector machine to obtain liaison graphs of truss products automatically. This method defined three kinds of oriented bounding boxes to embody the relationships of the joints in truss structures. Based on them, a series of factors are deduced as training data for support vector machine. Furthermore, two algorithms are introduced to calculate oriented bounding boxes to facilitate the data extraction. By these processes, this method established the knowledge of joints and realized the intelligent construction of liaison graph without shape matching reasoning. To verify the effect of the method, an experimental implementation is presented. The results suggest that the proposed method could recognize most joint types and construct liaison graph automatically with sufficient sample training. The correct recognition rate is more than 85%. Comparing with back-propagation neural network, support vector machine is more accurate and stable in this case. As an alternative method, it could help the engineers to arrange the assembly plan for truss structures and other similar assemblies.

Prediction of tool wear width size and optimization of cutting parameters in milling process using novel ANFIS-PSO method

“…Generally, the hybrid learning algorithm is used to optimize the parameters of ANFIS model. 11 However, it often relies on MATLAB toolbox to achieve predictive ability, and its scalability is not strong. 14 In this work, we try to solve these problems by proposing the new learning algorithm which combines PSO and DE algorithms.…”

“…As can be seen from above, the performance of ANFIS model is mainly determined by optimization algorithm. 11 Hence, a better optimization algorithm is needed to enhance the performance of ANFIS model.…”

Optimal robotic assembly sequence planning with tool integrated assembly interference matrix

“…Researchers used artificial intelligence (AI) techniques for their simplicity to generate optimal assembly sequences for various objective functions with a high convergence rate (Deepak et al , 2019; Su et al , 2021). AI methods like breakout local search (Ghandi and Masehian, 2015 a ), firefly algorithm (Zhang et al , 2016), advanced immune system (Bahubalendruni et al , 2016), machine learning (Cao et al , 2018), particle swarm optimization (Wang and Liu, 2010; Ab Rashid et al , 2019), ant colony optimization (Han et al , 2021), genetic algorithm (Wu et al , 2022; Lu et al , 2006), rule-based reasoning (Kroll et al , 1989; Lin et al , 2007), neural network (Chen et al , 2010), simulated annealing (Murali et al , 2017), and psychoclonal algorithm (Tiwari et al , 2005). Sometimes, combining different methods like the advanced immune system and GA (Gunji et al , 2017) and neuro-fuzzy by Zha (2001) also provides the optimal solutions faster.…”