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
Sciencedirect - Elsevier - Advances in Engineering Software, 133 (2019) 1-11: doi:10:1016/j:advengsoft:2019:04:007
Guanghui Zhoua,b,⁎, Xiongjun Yangb, Chao Zhangb, Zhi Lib, Zhongdong Xiaoc
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