Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network

Leo Gertrude David; Raj Kumar Patra; Przemysław Falkowski-Gilski; Parameshachari Bidare Divakarachari; Lourdusamy Jegan Antony Marcilin

doi:10.3390/app12168130

Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network

Abstrakt

In recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep learning models are extensively used in the existing studies. In this manuscript, a new model is proposed for the effective classification of both serviceable and worn cutting edges. Initially, a dataset is chosen for experimental analysis that includes 254 images of edge profile cutting heads; then, circular Hough transform, canny edge detector, and standard Hough transform are used to segment 577 cutting edge images, where 276 images are disposable and 301 are functional. Furthermore, feature extraction is carried out on the segmented images utilizing Local Binary Pattern (LBPs) and Speeded up Robust Features (SURF), Harris Corner Detection (HCD), Histogram of Oriented Gradients (HOG), and Grey-Level Co-occurrence Matrix (GLCM) feature descriptors for extracting the texture feature vectors. Next, the dimension of the extracted features is reduced by an Improved Dragonfly Optimization Algorithm (IDOA) that lowers the computational complexity and running time of the Deep Belief Network (DBN), while classifying the serviceable and worn cutting edges. The experimental evaluations showed that the IDOA-DBN model attained 98.83% accuracy on the patch configuration of full edge division, which is superior to the existing deep learning models.

Cytowania

1 1

CrossRef
0

Web of Science
1 1

Scopus

Autorzy (5)

Leo Gertrude David
Raj Kumar Patra
Przemysław Falkowski-Gilski dr inż.
Parameshachari Bidare Divakarachari
Lourdusamy Jegan Antony Marcilin

Cytuj jako

Pełna treść

pobierz publikację

pobrano 37 razy

Wersja publikacji: Accepted albo Published Version
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/app12168130
Licencja: otwiera się w nowej karcie

pełna treść artykułu zobacz w serwisie zewnętrznym otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach

Opublikowano w:

Applied Sciences-Basel nr 12,
ISSN: 2076-3417

Język:

angielski

Rok wydania:

2022

Opis bibliograficzny:

Gertrude David L., Kumar Patra R., Falkowski-Gilski P., Bidare Divakarachari P., Antony Marcilin L. J.: Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network// Applied Sciences-Basel -Vol. 12,iss. 16 (2022), s.8130-

DOI: