Tool Wear Analysis of Coated Carbide Tools on Cutting Force in Machining Process of AISI 4140 Steel

Abstract

In manufacturing industries specifically in metal machining, the use of the cutting tool is important to consider. The use of the cutting tool has not been used optimally, as well as cutting parameters. In the cutting process, the wear of the cutting tool and cutting force has a very close relationship. This research was carried out to analyze the wear of coated carbide cutting tool and cutting forces produced in turning AISI 4140 steel workpieces. This study was conducted with a variation of five cutting speed levels consisting of 180 m/min, 220 m/min, 260 m/min, 300 m/min, 340 m/min and depth of cut 0.7 mm. To observe the tool wear, every 5 minutes with a maximum limit value of VB of 0.3 mm and for the cutting force values taken every 1 minute. From the results of the study obtained: The increase in the value of the cutting force is affected by the increase in wear that occurs in the cutting tool due to a change in tool geometry due to wear on the cutting tool. The highest feeding force value is at the cutting speed (Vc) 340 m / min at 368 N and the greatest cutting force value is at the cutting speed (Vc) 340 m / min at 371 N.

Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review

“…The force signal is the preferred signal for online tool wear monitoring as it best reflects changes in tool condition. Lubis.et al [20] found that tool wear causes changes in cutting forces. The cutting force signal is more sensitive to tool wear than other signals.…”

“…Event data usually includes discrete events happening on the target machine based on manual entries, for example, manual installation, scheduled maintenance, abrupt breakdown, etc. [16] Monitoring and prediction data usually adopt signals related to tool wear collected by sensors, such as sound signal [17][18][19],cutting force signal [20] etc. Through the analysis and processing of the signal, the relevant characteristics are extracted, and then the established model [21][22][23][24] is used to identify the wear state, and the prediction results of the tool wear state and RUL are obtained, and the results are fed back to the machine tool.…”

A Novel Decision Support System Based on Fuzzy Multi Criteria Decision Making for Optimizing Machining Parameters

“…In addition, machinery, parts and apparatus, agricultural vehicles, a number of products used in the defense areas and the petroleum and derivatives industry are various area of usage of AISI 4140 steel. Machining operations are performed on the material with various methods since it has a very wide area of use (Şahinoğlu and Rafighi (2020), Gürbüz and Gönülaçar (2020), Schwalm et al (2020) and Lubis et al (2020)).…”