دانلود مقاله انگلیسی رایگان:یادگیری عمیق انتخاب ابزار برش را برای ویژگی های ماشینکاری خاص محصولات پیچیده امکان پذیر می کند - 2019
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  • Deep learning enabled cutting tool selection for special-shaped machining features of complex products Deep learning enabled cutting tool selection for special-shaped machining features of complex products
    Deep learning enabled cutting tool selection for special-shaped machining features of complex products

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Deep learning enabled cutting tool selection for special-shaped machining features of complex products


    ترجمه فارسی عنوان مقاله:

    یادگیری عمیق انتخاب ابزار برش را برای ویژگی های ماشینکاری خاص محصولات پیچیده امکان پذیر می کند


    منبع:

    Sciencedirect - Elsevier - Advances in Engineering Software, 133 (2019) 1-11: doi:10:1016/j:advengsoft:2019:04:007


    نویسنده:

    Guanghui Zhoua,b,⁎, Xiongjun Yangb, Chao Zhangb, Zhi Lib, Zhongdong Xiaoc


    چکیده انگلیسی:

    Each complex product contains many special-shaped machining features required to be machined by the specific customized cutting tools. In this context, we propose a deep learning based cutting tool selection approach, which contributes to make it effective and efficiency for and also improves the intelligence of the process of cutting tool selection for special-shaped machining features of complex products. In this approach, one-to-one correspondence between each special-shaped machining feature and each cutting tool is first analyzed and established. Then, the problem of cutting tool selection could be transformed into a feature recognition problem. To this end, each special-shaped machining feature is represented by its multiple drawing views that contain rich information for differentiating each of these features. With numbers of these views as training set, a deep residual network (ResNet) is trained successfully for feature recognition, where the recognized features cutting tool could also be automatically selected based on the one-to-one correspondence. With the learned ResNet, engineers could use an engineering drawing to select cutting tools intelligently. Finally, the proposed approach is applied to the special-shaped machining features of a vortex shell workpiece to demonstrate its feasibility. The presented approach provides a valuable insight into the intelligent cutting tool selection for special-shaped machining features of complex products.
    Keywords: Cutting tool selection | Special-shaped machining features | Complex products | Residual networks | Deep learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 11
    حجم فایل: 3098 کیلوبایت

    قیمت: رایگان


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