Yazar "Li, Z." seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Measuring Surface Characteristics in Sustainable Machining of Titanium Alloys Using Deep Learning-Based Image Processing
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Ross, Nimel Sworna; Shibi, C. Sherin; Mustafa, Sithara Mohamed; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Sharma, Vishal S.; Li, Z.
    A crucial method of maintenance in the manufacturing industry is machine vision-based fault diagnostics and condition monitoring of machine tools. The friction that occurs between the tool and the workpiece has a greater influence on the surface properties of the material. Effective problem diagnosis is necessary for machine systems to continue operations safely. Data-driven approaches have recently exhibited great promise for intelligent fault diagnosis. Unfortunately, the data collected under real-world conditions may be imbalanced, making diagnosis difficult. In dry, minimum quantity lubrication (MQL), and cryogenic circumstances, the method of failure detection of the proposed design is novel. The purpose of this interrogation is to evaluate the roughness profiles obtained from the machined surfaces and class separation. Markov transition field (MTF) is adopted to encode the surface profiles. In addition to this, conditional generative adversarial network (CGAN) for augmentation and bidirectional long-short term memory (BLSTM), multilayer perceptron (MLP), and 2-D-convolutional neural network (CNN) models are used for surface profile classification and correlation with process parameters. According to the study's finding, the 2-D-CNN was significantly more accurate than the models in predicting surface profiles, with an average accuracy of above 99.6% in both training and testing. In the limelight, the suggested approach can demonstrate to be quite useful for categorizing and proposing appropriate machining circumstances, specifically in situations with minimal data.
  • Küçük Resim Yok
    Öğe
    A Short Review on Measurement Methods in Machining of Aerospace Materials
    (Institute of Electrical and Electronics Engineers Inc., 2023) Korkmaz, M.E.; Gupta, M.K.; Krolczyk, J.B.; Krolczyk, G.M.; Li, Z.; Ross, N.S.
    In-process measurements are becoming more and more popular among businesses as a result of the numerous benefits they provide, including reduced production costs, improved product quality, and real-time analysis of both production and product quality of aerospace materials. It is anticipated that the method of measuring manufactured components known as 'in-process measurement' will become the standard practice in the not too distant future. After a description of the in-process measurement methods with the developed examples, an explanation of the usage of machine tools as a measurement device will be provided in this paper, along with the needs, issues, and challenges, and recent research work. © 2023 IEEE.