Crater wear on multi-layered coated carbide inserts when turning three distinct cast irons

Tooptong, Sirisak; Nguyen, Dinh; Park, Kyung-Hee; Kwon, Patrick

doi:10.1016/j.wear.2021.203982

Cited by 6 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the possibility of gray cast-iron applications in challenging areas demands the characteristic morphology of the graphite phase. The matrix of gray cast iron consists of a soft ferrite phase and lamellar pearlite phase [10,11]. However, mechanical properties depend on the chemical composition, the type of matrix and form of graphite (flake, compacted, nodular), or heat and cooling cycle [12,13].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

et al. 2022

View full text Add to dashboard Cite

The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut ap and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

et al. 2022

View full text Add to dashboard Cite

show abstract

Tool life experiment of coated cermet during nodular cast iron turning

Lánc,

Fiala,

Zouhar

2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

In this study, the application of physical vapor deposition (PVD) coatings on various cermet substrates for the machining of nodular cast iron was investigated, given that cermet materials are traditionally not the primary choice for such applications. Through the evaluation of key parameters including wear on the flank face and surface roughness, and with vibration analysis serving as a supplementary measurement, it was discovered that machining stability is compromised by the inherent brittleness of cermet and the adhesion of material on the rake face. While the application of coatings was found to extend tool life, the cermet-coating combinations did not surpass the performance of other materials such as cemented carbide with multi-layer coatings. Notably, the stability of surface roughness across various stages of tool wear was observed.

show abstract

A new modeling approach for stress–strain relationship taking into account strain hardening and stored energy by compacted graphite iron evolution

Niu,

Huang,

Shi

et al. 2023

Front. Mech. Eng.

View full text Add to dashboard Cite

Crater wear on multi-layered coated carbide inserts when turning three distinct cast irons

Cited by 6 publications

References 28 publications

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning

Tool life experiment of coated cermet during nodular cast iron turning

A new modeling approach for stress–strain relationship taking into account strain hardening and stored energy by compacted graphite iron evolution

Contact Info

Product

Resources

About