Việc sử dụng học máy trong mô hình hóa mối quan hệ quy trình–cấu trúc–tính chất cho sản xuất bổ sung theo quy trình đùn vật liệu: một bài tổng quan hiện trạng

Ziadia Abdelhamid1, Habibi Mohamed1, Sousso Kelouwani1
1Department of Mechanical Engineering, University of Quebec in Trois-Rivieres, Trois-Rivieres, Canada

Tóm tắt

Kể từ khi xuất hiện lần đầu vào những năm 1980, sản xuất bổ sung (additive manufacturing) đã trở nên ngày càng phổ biến. Các bộ phận phức tạp có thể được sản xuất với chất lượng cao, lãng phí tối thiểu và với nhiều loại vật liệu khác nhau. Tuy nhiên, việc chọn các thông số quy trình phù hợp cho in 3D vẫn là một thách thức. Trong bối cảnh này, các nhà nghiên cứu đã nghiên cứu ảnh hưởng của các thông số quy trình đến các tính chất của các bộ phận in 3D. Với sự phát triển của học máy, các nhà nghiên cứu đã áp dụng công nghệ này để tối ưu hóa mối quan hệ giữa quy trình, cấu trúc và tính chất, cũng như giám sát quy trình in theo thời gian thực. Do đó, bài báo tổng quan này giới thiệu ảnh hưởng của các thông số của quy trình đùn vật liệu đến các tính chất của các bộ phận đã in. Sau đó, tiềm năng của học máy trong việc tối ưu hóa in 3D cho quy trình đùn vật liệu được nhấn mạnh. Nhiều công nghệ và phương pháp đã được xác định. Hầu hết các nghiên cứu về giám sát trong quá trình in đều tập trung vào dữ liệu hình ảnh, xác định các khuyết tật in ấn, với Mạng Nơ-ron Tích chập (Convolutional Neural Network) là thuật toán được sử dụng phổ biến nhất, hoặc các dữ liệu cảm biến khác, trong đó cảm biến phát xạ âm thanh là phổ biến nhất được sử dụng để giám sát trạng thái của polyme và đầu đùn, nơi các thuật toán chuỗi thời gian chủ yếu được sử dụng. Để xác định mối quan hệ giữa quy trình–cấu trúc–tính chất, các chiến lược được áp dụng tập trung vào việc dự đoán tính chất của bộ phận, xác định các thông số in tối ưu, hoặc ước lượng các thông số quy trình dựa trên các tính chất của bộ phận. Tuy nhiên, vẫn còn những khoảng trống trong nghiên cứu. Nghiên cứu trong tương lai nên xem xét chi phí tính toán của dự đoán học máy, lựa chọn cẩn thận các cảm biến, và chia sẻ dữ liệu để thu được tập dữ liệu lớn hơn, vì các nhà nghiên cứu thường sử dụng các vật liệu và phương pháp mới khác nhau và có thể hưởng lợi lẫn nhau.

Từ khóa

#học máy #sản xuất bổ sung #quy trình đùn vật liệu #mô hình hóa mối quan hệ quy trình-cấu trúc-tính chất #giám sát in 3D

Tài liệu tham khảo

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