Pre-evaluation on surface profile in turning process based on cutting parameters

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile preevaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.

Tối ưu hóa độ nhám bề mặt và tốc độ loại bỏ vật liệu trong tiện thép không gỉ AISI 420 sử dụng ANN và NSGA-II

“…Ngược lại, Bozid và cộng sự [3] nói rằng tốc độ tiến dao, độ sâu cắt và tốc độ cắt tương ứng ảnh hưởng đến Ra. Những khác biệt này có thể là do các yếu tố khác nhau, chẳng hạn như các phạm vi thông số cắt khác nhau, các đặc tính của phôi và dụng cụ riêng biệt, thiết bị gia công cụ thể được sử dụng và điều kiện môi trường trong các kỹ thuật gia công hiện đại [10]. Những biến thể này nhấn mạnh sự phức tạp của quy trình gia công và tầm quan trọng của việc xem xét nhiều yếu tố khi diễn giải ảnh hưởng của các tham số cắt đối với độ nhám bề mặt.…”

Simulation Approach for Surface Roughness Interval Prediction in Finish Turning

“…The techniques employed include response surface methodology (RSM) combined with factorial design approach [11], RSM combined with data extracted from workpiece surface profile simulated from images of tool inserts [12], statistical models based on experimental data [13] and artificial neural network-based methodologies [14][15][16]. Hessainia et al [17] developed a surface roughness model in hard turning by exploiting the response surface model.…”

Use of Longitudinal Roughness Measurements as Tool End-of-Life Indicator in AISI 1045 Dry Longitudinal Turning

“…The cutting parameters also have important effects on the surface roughness and tool life [11]. More advanced techniques, such as machine learning and in particular neural networks may take tool wear as a variable for roughness prediction, mainly for inducing uncertainty [12]. It has also been suggested to predict both the tool wear and the surface roughness from the cutting parameters and learning data [13].…”