Deep Learning For Commercial Robotics

On Jul 13, 2019, my associate Lee and I attended a temporary program at Tsinghua College to learn the application of deep learning methods for commercial robotics. We are designers at PIX Moving and we aim to use Machine Learning how to improve Wire-Arc additive manufacturing (WAAM) process. This post documents our learning.

Thinking
In its basic form, WAAM is application of robotic welding technology to metal 3D publishing. Because of fundamental restrictions of welding technology, such as the dimension of weld bead, WAAM isn't a net-shape process (post-machining is needed) and cannot suit the intricacy of forms accomplished by powder-based technologies. The last publish quality, when measured by surface waviness and dimensional precision, depends on many specifications such as cable feed speed, present, robotic travel speed, tool-path plan and work range. The communication in between these specifications and the last accomplished form is complex and requires comprehensive testing to be comprehended. Our company believe that a support learning — centered model can learn how to understand this connection and lead to better publish quality.  Tips Untuk Pemula Judi Bola Online
The problem involved educating a KUKA KR6 900 commercial robotic to publish planar plastic cable hairs of preferred form in between 2 finish factors. Just the preferred form needs to be fed as input and the robotic must determine the required input specifications needed by itself. So, the robotic must learn to find the connection in between input specifications and the last obtained form.
The educating information is gathered through pictures. The robotic is trained on 50 experiments and in every experiment, it prints a planar contour. The input specifications for each published contour are arbitrarily varied using a customized software device developed on rhino-grasshopper, thereby prominent to varied contour designs for each experiment. The effect of just 2 input specifications were considered, extrusion speed and pause time.
To refix this problem, a deep learning model was developed based upon CNN (Convolutional Neural Network) and LSTM (Lengthy Short-Term Memory). The neural networks were chosen because they have been proven to work well on image-based problems. The 2D nature of the problem allows us to gather information through pictures and use it to educate the deep learning formula.
Location: Indonesia

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