ỨNG DỤNG MẠNG NƠRON NHÂN TẠO VÀ GIẢI THUẬT PHỎNG TỰ NHIÊN TRONG TỐI ƯU HÓA KẾT CẤU KHUNG MÁY IN 3D BÊ TÔNG KÍCH THƯỚC LỚN DẠNG CỔNG TRỤC=APPLICATION OF ANN AND NATURE-INSPIRED ALGORITHM IN DESIGN OPTIMIZATION OF THE FRAME OF A LARGE-SCALE 3D CONCRETE PRINTER

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Tác giả: Đình Tùng Phạm, Văn Bình Phùng, Đức Hải Tạ

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Journal of Science and Technique, 2024

Mô tả vật lý: tr.21

Bộ sưu tập: Metadata

ID: 479810

Thêm vào giỏ

Bài báo trình bày việc ứng mạng nơron nhân tạo (ANN) trong tính toán và tối ưu hóa kết cấu khung máy in 3D bê tông. Mạng ANN theo cấu trúc 6-30-4 được xây dựng để dự đoán các giá trị chuyển vị uX, uY, uZ và tần số dao động riêng thứ nhất f1 của cụm trục di động theo 6 tham biến kích thước đầu vào. Khảo sát cho thấy mạng có hiệu năng dự đoán tốt, với sai số dự đoán uX, uY nhỏ hơn 3%, và sai số của uZ, f1 nhỏ hơn 5%. Mô hình ANN này đã được ứng dụng trong quá trình tối ưu hóa kết cấu khung máy in sử dụng giải thuật di truyền (GA). Kết quả so sánh cho thấy, việc sử dụng GA trực tiếp tương tác với chương trình tự động tính toán Ansys APDL-Matlab có thời gian tìm kiếm khoảng 240 ÷ 300 phút, trong khi đó việc kết hợp ANN-GA đã giúp giảm thời gian tìm kiếm phương án tối ưu còn 77 giây. Đặc biệt, việc tối ưu hóa đã được thực hiện trên miền không gian rời rạc theo kích thước của thép hộp tiêu chuẩn, thỏa mãn yêu cầu công nghệ chế tạo nên nó có thể sử dụng trực tiếp trong việc hoàn thiện thiết kế cơ khí máy in 3D bê tông.This article presents the application of artificial neural networks (ANNs) in computing and optimizing frame structure in a 3D concrete printer. An ANN (with 6 inputs, 30 neurons in hidden layer and 4 outputs) was developed to predict the system natural frequency f1 and displacement values uX, uY, uZ of nozzle according to 6 input parameters. The survey showed that the ANN has good predictive performance with the prediction errors uX, uY, prestige less than 3% and the errors of uZ, f1 less than 5%. This ANN model was embedded into the genetic algorithm (GA) to optimize the frame structure. The results of searching for an optimization solution indicate that although utilizing an ANN-GA combination took over 77 seconds, using GA directly interacting with the Ansys APDL-Matlab code took about 240 ÷ 300 minutes. Particularly, the optimization process was performed on a discrete spatial domain based on the dimensions of the standard steel box, so the optimal design solution can be used directly in completing the mechanical design of the 3D concrete printer.

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