Background This paper proposes a method for designing a passive suspension system that determines the optimal suspension settings while offering feasible performance near an active suspension system. A mathematical model of a nonlinear quarter car is developed and simulated for control and optimization in MATLAB/Simulink® environment. The input road condition is a Class C Road, and the vehicle moves at 80 kmph. Fuzzy logic control (FLC) action is used to accomplish active suspension system control. An approach for investigating optimal suspension settings based on the FLC control force is described here. The optimized passive suspension system is supposed to have the same suspension travel and velocity as an active suspension system. The least square technique is implemented to optimize the suspension parameters of the passive suspension system. Results The initial passive suspension system, FLC active system, and optimized suspension system are simulated in MATLAB/Simulink® environment. It is observed that RMS acceleration for the FLC system is 0.5057 m/s2, which is reduced by 46% (passive suspension system has RMS acceleration of 0.9322 m/s2, which is uncomfortable). For optimized system, RMS acceleration is 0.6990 m/s2. It is observed that the optimized passive suspension system almost mimics the initial FLC active suspension system. For the optimized system, sprung mass acceleration and VDV are improved by 30% and 27%, respectively, compared to the initial passive system. Conclusion It is observed that the optimized passive suspension system mimics the initial FLC system. Also, an optimized FLC system has improved health criterion-based results compared to other suspension systems.

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This paper presents an intelligent technique to recognize the volumetric features from CAD mesh models based on hybrid mesh segmentation. The hybrid approach is an intelligent blending of facet-based, vertex based, rulebased, and machine learning based techniques. Comparing with existing stateof-the-art approaches, the proposed approach does not depend on attributes like curvature, minimum feature dimension, number of clusters, number of cutting planes, the orientation of model and thickness of the slice to extract volumetric features. The intelligent threshold prediction makes hybrid mesh segmentation automatic. The proposed technique automatically extracts volumetric features like blends and intersecting holes along with their geometric parameters. The proposed approach has been extensively tested on various benchmark test cases. The proposed approach outperforms the existing techniques favorably and found to be robust and consistent with coverage of more than 95% in addressing volumetric features

This paper reports a unique, platform-independent approach for blend recognition from CAD mesh model using pattern matching. About 60% of the average portion of the total facets in CAD mesh model is blended features. So, it becomes essential and necessary to extract these blend features for the successful accomplishment of seamless CAD/CAM integration. The facets of the same region have similar patterns. The focus of this paper is to recognize the blends using hybrid mesh segmentation based on pattern matching. Blend recognition has been carried out in three phases viz. preprocessing, pattern matching hybrid mesh segmentation and blend feature identification. In preprocessing, the adjacency relationship is set in facets of CAD mesh model, and then Artificial Neural Networks based threshold prediction is employed for hybrid mesh segmentation. In the second phase, pattern matching hybrid mesh segmentation is used for clustering the facets into patches based on distinct geometrical properties. After segmentation, each facet group is subjected to several conformal tests to identify the type of analytical surfaces such as a cylinder, cone, sphere, or tori. In the blend feature recognition phase, the rule-based reasoning is used for blend feature extraction. The proposed method has been implemented in VC++ and extensively tested on benchmark test cases for prismatic surfaces. The proposed algorithm extracts the features with coverage of more than 95 %. The innovation lies in "Facet Area" based pattern matching hybrid mesh segmentation and blend recognition rules. The extracted feature information can be utilized for downstream applications like tool path generation, computer-aided process planning, FEA, reverse engineering, and additive manufacturing.