CAD-MBSE Interoperability for the Checking of Design Requirements Based on Assemblability Indicators

A mechanical product is the result of collaboration between different domains. In this paper we focus on the collaborative work that brings together the system engineer and the designer in the realization process of a mechanical system. A design solution, conceived in the CAD environment, is considered valid if all the specification requirements, defined in the MBSE (Model Based System Engineering) domain, are met. Thus, the preliminary study of these requirements has a major influence on the choice and validation of the design solution. In this paper a methodology for handling the requirements has been detailed. These requirements are classified into two main categories. The first one deals with the product performance and the second addresses the process performance in order to generate the best assembly sequence. Depending on the response of the designed solution to these requirements, it becomes easy to decide not only whether the design solution can be validated or not but also to choose the most optimal assembly sequence that ensures the best operation quality. A validation example of a speed reducer is used to demonstrate the added value of the proposed approach.

APPLICATION OF THE METAHEURISTIC TUNICATE SWARM ALGORITHM IN SOLVING APPLIED MECHANICS PROBLEMS

In this paper, the principles of a metaheuristic algorithm based on tunicate swarm behavior are shown. The Tunicate Swarm Algorithm (TSA for short) was used for solving problems in applied mechanics (speed reducer, cantilever beam and three-dimensional beam optimization). In the end, a comparison of results obtained by TSA and results obtained by other methods is given.

A Novel Crow Search Algorithm Based on Improved Flower Pollination

Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows’ intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem.

Simulation and experimental study on lubrication of high-speed reducer of electric vehicle

Purpose The lubrication of the high-speed reducer of an electric vehicle is investigated. The specific contents include visualization of the flow field inside reducer, lubrication evaluation of bearings and efficiency experiment. Design/methodology/approach The flow field inside reducer at five working conditions: straight, uphill, downhill, left lean and right lean is simulated by smoothed particle hydrodynamics (SPH). According to the instantaneous number of particles through bearings, the lubrication states of bearings are evaluated. The test platform is set up to measure the efficiency of the reducer. Findings The flow field inside the reducer is obtained, the lubrication of bearings needs to be improved, the efficiency of the electric vehicle reducer meets the requirement. Originality/value The SPH method is used to simulate lubrication instead of using the traditional grid-based finite volume method. A novel method to evaluate the lubrication of bearings is proposed. The method and conclusions can guide electric vehicle reducer design.

On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection

Butterfly Optimization Algorithm (BOA) is a recent metaheuristics algorithm that mimics the behavior of butterflies in mating and foraging. In this paper, three improved versions of BOA have been developed to prevent the original algorithm from getting trapped in local optima and have a good balance between exploration and exploitation abilities. In the first version, Opposition-Based Strategy has been embedded in BOA while in the second Chaotic Local Search has been embedded. Both strategies: Opposition-based & Chaotic Local Search have been integrated to get the most optimal/near-optimal results. The proposed versions are compared against original Butterfly Optimization Algorithm (BOA), Grey Wolf Optimizer (GWO), Moth-flame Optimization (MFO), Particle warm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA) using CEC 2014 benchmark functions and 4 different real-world engineering problems namely: welded beam engineering design, tension/compression spring, pressure vessel design, and Speed reducer design problem. Furthermore, the proposed approches have been applied to feature selection problem using 5 UCI datasets. The results show the superiority of the third version (CLSOBBOA) in achieving the best results in terms of speed and accuracy.

Development and validation of a dynamic model for a two-stage speed-reducer

This paper presents the development and validation of a six degree of freedom (DOF) dynamic model of a two-stage parallel shaft gearbox, without flaws, which is able to determine the reaction of gearbox components to varying torque inputs and loads. The model utilises flexible shafts and gears rather than using a rigid assumption, to further understand the effect of varying mesh stiffness. The paper replicates the results presented by Diehl and Tang, and improves the number of frequencies that can be analysed. The gear meshing frequencies were expected to dominate the result, however due to the use of a sinusoidal approximation of the varying tooth mesh frequency, the presented model shows the additional gear generated frequencies are present and analysable in the data.