مونتاژ انتخابی روتور با استفاده از منطق فازی در تولید برق
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|46344||2015||6 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia CIRP, Volume 33, 2015, Pages 550–555
The growing sector of sustainable mobility requires an increasing number of electric drives for automobiles, thus increasing the need for their efficient mass production. In current production scenarios, End-Of-Line (EOL) inspection is applied to determine the quality of the assembled final product. The work presented in this paper is developed in the project MuProD, which aims at developing an innovative quality control system to change the current concept of EOL quality control. The case under investigation is the production of electric drives where the rotor is composed of several magnetized stacks. The magnetic properties of each stack differ due to variations of the single magnets and the magnetization process itself. In addition, handling of the magnets within the production line may cause cracks that decrease the strength of the magnetic field. This paper proposes a new solution for deviation compensation in the production of electric drives by selective assembly based on a crisp classification and a Mamdani style fuzzy inference system. The magnetic field of each stack is measured after the magnetization stage, yielding a discrete space-resolved magnetic profile. This magnetic profile is transformed into another feature space through a combination of feature selection and feature extraction to reduce the dimension. Based on these new features, the stacks can be classified into crisp sets. In the second part, an appropriate fuzzy rule base for the matching of the stacks is developed to obtain a uniform magnetic field of the rotor. By applying this assembly strategy, the rotor and consequently the final motor reaches desired quality targets although deviations in the single stacks are present. The benefits of the approach are validated within an industrial context.