Modeling and Analysis of Spatial Flexible Mechanical Systems With a Spherical Clearance Joint Based on the LuGre Friction Model

A computational methodology for modeling spatial flexible mechanical systems with stick-slip friction in a spherical clearance joint is presented. A modified three-dimensional (3D) absolute nodal coordinate formulation based shear deformable beam element with two nodes is proposed and employed to discretize the flexible components. To avoid locking problems, we employed an enhanced continuum mechanics approach to evaluate the beam element elastic forces. The strain components εyz, εyy, and εzz are approximated using linear interpolation to improve the computational efficiency while the loss of accuracy is acceptable. The contact and friction forces in a spherical clearance joint were evaluated by the hybrid contact and LuGre friction models, respectively. Three numerical examples are presented and discussed. A simple pendulum was utilized to prove the correctness of the modified beam element. A classical slider–crank mechanism was employed to validate the computational methodology. A spatial rigid–flexible slider–crank mechanism with a spherical clearance joint was used to investigate the effect of link flexibility and joint clearance on the dynamic behavior of mechanical systems. Using the LuGre friction model, we reproduced the Stribeck effect as it is expected in real world settings. The components with appropriate stiffness play the role of suspension for spatial mechanical systems with imperfect joints. The vibrations of the flexible components play an active role of intensifying the collision in kinematic joint with clearance.

A Framework for Software Component Reusability Analysis using Flexible Software Components Extraction

With the adaptation of DevOps and agile methodologies for software developments, the demand from the consumers of the software applications are increasing to fulfil short time development phases. The application development community is facing the everlasting challenge of managing the bottleneck of timely delivery and quality of the software. The software quality being one of the most important factors to maintain the market reputation and the customer satisfaction, cannot be compromised. The software development companies have also tried adopting the strategies of deploying highly skilled development team in order to reduce the development life cycle span. Thus, several research attempts are made to identify the correct software reusable components for any projects. Nonetheless, the recent research outcomes are challenged by the development community with the arguments of reliability as different applications from different domain demands unique specifications and standards to be matched. Also, the components, which are to be reused for other specifications, must match the compliances of that specification. Considerably, identification of the reusable components is always a challenge and it is observed that the identification of the flexible components, for which matching the domain specification is easy, is also high complex process. Finally, finding the ideal coupling point of the identified modules are also a significant challenge. Thus, this research proposes a novel framework for identification of the flexible components for reuse in software development life cycle and identifies the unique coupling points of these components. Firstly, this work analyses the software components for features to be identified as flexible components using the deep extraction algorithm. The deep extraction algorithm demonstrates a novel characteristic for extracting feature based on the external and internal feature dependencies. The extracted features are also ranked based on the degree of independency and finally listed based on given ranks for reusability. Secondly, the extracted features are tested for outcome-based equivalence to ensure best reusability from the component library. Due to the multi – level equivalency analysis, this proposed framework provides higher accuracy and lower time complexity using the moderate number of extracted features for component analysis.