Abstract
The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09% by the new algorithm and the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.
Highlights
The diaphragm spring clutch is widely used in automation because of good nonlinear characteristics [1]
The method is used in the parallel method is used in the parallel optimization design of the friction type diaphragm spring clutch
The of of pull diaphragm spring clutch optimization is taken as an Thedesign designofofcertain certainnew newtype type pull diaphragm spring clutch optimization is taken as example
Summary
The diaphragm spring clutch is widely used in automation because of good nonlinear characteristics [1]. The performance of the clutch diaphragm spring directly affects the above parameters and it has important practical engineering optimal design implications. The method is used in the parallel method is used in the parallel optimization design of the friction type diaphragm spring clutch. The optimization design of the friction type diaphragm spring clutch. The authorsmodel of [9] set a new multi-objective optimization multi‐objective optimization design, taking the minimum of average compressing force of the minimum of average compressing force of spring within the scope of the friction slice spring wear within scopeminimum of the friction slice wear the driver’s minimum manipulatingobjectives. We aim to study the separation stroke average of the minimum separation of and the pressing force change minimum as two multi-objective optimization models. Thethe pressing force of diaphragm variedcoefficient slightly during abrasionempirical range of design the friction film, and manipulation the optimized diaphragm spring varied slightly to during the abrasion rangebyofsimulations.
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