https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/146 Sun, 05 Apr 2026 18:51:25 GMT 2026-04-05T18:51:25Z http://localhost:8080/jspui/retrieve/ade5dbe2-c438-452c-9eca-f477ff527a99/khoahoc.png https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/146 https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/448 Title: Dự đoán mô-men xoắn trong khoan lỗ sâu: Mạng nơ-ron nhân tạo so với mô hình hồi quy phi tuyến tính Authors: Chu, Ngoc Hung; Nguyen, Hoai Nam; Nguyen, Van Du; Nguyen, Dang Binh Abstract: One of the main challenges when drilling small and deep holes is the difficulty of chip evacuation. As the hole depth increases, chips tend to become tightly compressed, causing chip jamming. It leads to a rapid increase in cutting forces and strong random fluctuations. The discontinuous chip evacuation process makes the cutting force signal strongly nonlinear and random, making it difficult to predict accurately. In this paper, we have developed a two-layer artificial neural network (ANN) model for training using the Levenberg-Marquardt algorithm to predict torque during deep drilling. Unlike many previous studies, this model uses hole depth as an input vector element instead of hole diameter. The model has been validated through experiments drilling AISI-304 stainless steel with hole depth-todiameter ratios of 8 under continuous drilling conditions with ultrasonic-assisted vibration. The performance of the ANN model was compared with the exponential model and evaluated by the MAPE index. Results show that the ANN model has better predictive capability, the average MAPE value approximately four times smaller and higher reliability with a standard deviation approximately 3.5 times smaller than the exponential function model. This model can be further refined to predict torque for drilling deep holes for future studies. Description: Một trong những thách thức chính khi khoan các lỗ nhỏ và sâu là khó khăn trong việc thoát phoi. Khi độ sâu lỗ tăng lên, phoi có xu hướng bị nén chặt, gây kẹt phoi. Điều này dẫn đến lực cắt tăng nhanh và dao động ngẫu nhiên mạnh. Quá trình thoát phoi không liên tục làm cho tín hiệu lực cắt trở nên phi tuyến tính và ngẫu nhiên mạnh, gây khó khăn cho việc dự đoán chính xác. Trong bài báo này, chúng tôi đã phát triển một mô hình mạng nơ-ron nhân tạo (ANN) hai lớp để huấn luyện sử dụng thuật toán Levenberg-Marquardt nhằm dự đoán mô-men xoắn trong quá trình khoan sâu. Không giống như nhiều nghiên cứu trước đây, mô hình này sử dụng độ sâu lỗ làm phần tử vectơ đầu vào thay vì đường kính lỗ. Mô hình đã được kiểm chứng thông qua các thí nghiệm khoan thép không gỉ AISI-304 với tỷ lệ độ sâu lỗ trên đường kính là 8 trong điều kiện khoan liên tục với rung động siêu âm trợ giúp. Hiệu suất của mô hình ANN đã được so sánh với mô hình hàm mũ và được đánh giá bằng chỉ số MAPE. Kết quả cho thấy mô hình ANN có khả năng dự đoán tốt hơn, giá trị MAPE trung bình nhỏ hơn khoảng bốn lần và độ tin cậy cao hơn với độ lệch chuẩn nhỏ hơn khoảng 3,5 lần so với mô hình hàm mũ. Mô hình này có thể được cải tiến hơn nữa để dự đoán mô-men xoắn khi khoan lỗ sâu cho các nghiên cứu trong tương lai. Wed, 01 Jan 2025 00:00:00 GMT https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/448 2025-01-01T00:00:00Z https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/367 Title: Tư tưởng Hồ Chí Minh vè xây dựng Chủ nghĩa xã hội ở Việt Nam Authors: Huỳnh Thành Lập Tue, 01 Jan 2008 00:00:00 GMT https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/367 2008-01-01T00:00:00Z https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/232 Title: THE SHARING ECONOMY FOR SUSTAINABILITY DEVELOPMENT AND RECOMMENDATIONS FOR VIETNAM Authors: Do Anh Duc, Le Dinh Manh Abstract: The concepts of "sharing economy" or "sharing economy model" have been mentioned and discussed enthusiastically on many forums recently. This is a new form of business capable of bringing super profits for businesses in particular and benefits for socioeconomics in general in the digital economy. However, to apply the sharing economy model effectively, it requires the participation of leaders and state management at all levels. Without reasonable policies, it may cause unfair competition between traditional business enterprises and businesses operating under the sharing economy model. The article clarifies the theoretical basis, outlines the popular sharing economy models in the world, analyzes the current situation of developing the sharing economy model in Vietnam, thereby making some recommendations to develop the sharing economy. The sharing economy in Vietnam for sustainably development. Sat, 01 Jan 2022 00:00:00 GMT https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/232 2022-01-01T00:00:00Z https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/231 Title: Ultrasonic assisted nano-fluid MQL in deep drilling of hard-to-cut materials Authors: Tien-Dat Hoang, Quoc-Huy Ngo; Ngoc-Hung Chu, Thu-Ha Mai; Truong Nguyen, Ky-Thanh Ho; Du Nguyen Abstract: This paper presents a new method of combining Minimum Quantity Lubricant (MQL) with ultrasonic assistance in deep hole drilling processes for difficult-to-cut materials, using internal coolant tools. A small amount of lubricant as 90 mL/h was provided directly into the cutting zone during deep drilling under ultrasonic vibrations exerted on the tool. A comparative experimental study was implemented for two types of drilling processes: conventional drilling (CD) and ultrasonic-assisted drilling (UAD). For each set, three different methods of lubricant feeding methods were applied, including flooding, MQL, and MQL with graphene nanoparticles. A continuous drilling process using a through-hole drill bit (D = 5 mm, L = 8D) for AISI SUS 304 stainless steel was implemented to validate the effectiveness of the proposed approach. The results show that the approach proposed can overcome the bottleneck in deep drilling using MQL, providing a higher production rate, longer tool life, and better machining performance in terms of cutting forces and processing ability. Using the newly proposed method, continuous drilling can be done in critical machining conditions, which are typically recommended by the tool manufacturers. The results are promising to expand to deep hole drilling of other hard-to-cut materials. Fri, 01 Jan 2021 00:00:00 GMT https://thuvienso.tuetech.edu.vn:8080/jspui/handle/123456789/231 2021-01-01T00:00:00Z