Effect of tool wear on the surface integrity of Inconel 718 in face milling with cemented carbide tools

Oliveira, A.R.F.; Silva, L.R.R. da; Baldin, Vitor; Fonseca, Maria Cindra; Silva, R.B.; Machado, Álisson Rocha

doi:10.1016/j.wear.2021.203752

Cited by 40 publications

(15 citation statements)

References 10 publications

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“…At tc of 1.1471 mins, the notch wear appears at the depth of cut (Fig. 10a) occurs due to machining of the deformed hardened surface resulting from the previous machining [32]. On futher machining, extensive abrasive wear along with adhesive wear resulted in ank wear (Fig.…”

Section: Tool Wear Analysis Under Dry Environmentmentioning

confidence: 99%

“…Concerning environmental awareness Minimum Quantity Lubrication (MQL) was used as an alternative to conventional cutting uids [30]. The use of lubrication minimizes the thermal softening [31], but the cutting tool is prone to plastic deformation which induces compressive residual stresses on the workpiece [32]. On subsequent machining during tool life, progressive deformation of the cutting edge in uences the surface integrity (esp.…”

Section: Introductionmentioning

confidence: 99%

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Tool Life and Surface Integrity Characteristics of SLM and C&W Inconel 718 in Dry and MQL Condition

Periane

Duchosal

Vaudreuil

et al. 2022

Preprint

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In this paper, a tool life test was performed using PVD coated insert on Inconel 718 (IN718) samples. The tool life was tested on IN718 workpiece fabricated through conventional (Cast & Wrought) and additive (Selective Laser Melting) process routes. The as-built SLM samples were subjected to two heat treatments such as Hot Isostatic Pressing (HIP) and Aeronautic Heat Treatment (AHT). This study aimed to evaluate the machining behavior of C&W and SLM IN718 from the machining point of view such as tool wear, chip appearance, surface roughness, and residual stresses. The tool life tests were performed under a dry and near dry environment like using Minimum Quantity Lubrication (MQL). Meanwhile, the tool wear propagation under different cutting conditions is also explored. Under both the cutting conditions flank wear and fracture of the cutting edge are the most predominant failure modes minimizing the tool life. The microhardness, surface roughness, and residual stress measurements were analyzed. The result indicates that the microstructural difference between the C&W and SLM has more influence on the tool life compared to the machining environment. On machining, the SLM sample has 80% and 43% more tool life than the C&W in dry and MQL machining. Comparing the dry and MQL machining of SLM, on using MQL the tool life is 30% less compared to the dry machining.

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Section: Tool Wear Analysis Under Dry Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tool Life and Surface Integrity Characteristics of SLM and C&W Inconel 718 in Dry and MQL Condition

Periane

Duchosal

Vaudreuil

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The common tangent plane could be solved by separately obtaining the partial derivatives of the two contact surfaces and then simultaneously solving the equations, as shown in equation (9). The unit direction vector projected by the movement speed on the common tangent plane can be expressed as equation (10) in the workpiece coordinate.…”

Section: Determination Of Friction Speedmentioning

confidence: 99%

“…Wang et al 9 examined the microstructure and compositional characterization of worn tool inserts using SEM and EDS analysis. Oliveira et al 10 tested different tools wear under finishing, roughing, and moderate machining conditions. Wu et al 11 studied the effect of aspect ratio on the machining process during milling of titanium alloys.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous friction energy consumption and its evolution of high energy efficiency milling cutter

Zhao

Zhang

Jiang

et al. 2022

Advances in Mechanical Engineering

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In the process of high-efficiency milling of titanium alloys, cutting vibration causes the frequent changes in the contact relationship between the flank face of the cutter tooth and the workpiece. The instantaneous friction speed, friction force and energy consumption of the milling cutter thus change continuously, resulting in the difficulty to precisely control the wear and life of milling cutter. This has become a bottleneck of restricting the further improvement of the cutting performance of high energy efficiency milling cutter. In this work, the model of instantaneous friction velocity and friction energy consumption of the flank face of the cutter tooth under vibration are developed. The distribution and evolution of instantaneous friction speed and friction energy consumption are studied. The proposed models are verified by high-efficiency milling experiments. The results show that the average relative error of the proposed model is 10.05%, by using this model, the influences of the cutting vibration on instantaneous friction energy consumption and friction wear boundaries could be effectively unveiled. The proposed model also could accurately describe the distribution and evolution of the above-mentioned parameters.

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“…Other preliminary works report the impact of ultrasonic assistance on SLP in milling [28,29]. Furthermore, it was shown that tool wear state has a significant effect on SLP [3,[30][31][32][33]. Other works showed the consequences of tool substrate [3,31], tool coating [34], cutting edge chamfer [3,35,36] and rake angle variation [37].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning of Surface Layer Property Prediction for Milling Operations

Uhlmann

Holznagel

Schehl

et al. 2021

JMMP

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Tool wear and cutting parameters have a significant effect on the surface layer properties in milling. Since the relation between tool wear, cutting parameters, and surface layer properties is mostly unknown, the latter cannot be controlled during production and may vary from part to part as tool wear progresses. To account for this uncertainty and to prevent premature failure, components often need to be oversized or surface layer properties need to be adjusted in subsequent manufacturing processes. Several approaches have been made to obtain models that predict the surface layer properties induced by manufacturing processes. However, those approaches need to be calibrated with a considerable number of experimental trials. As trials are time-consuming and surface layer measurements are laborious, no industrial applications have been realized. Complex models have one major drawback. They have to be re-parameterized as soon as process characteristics change. Therefore, manual experimental parameterization does not appear to be a feasible approach for industrial application. A highly automated approach for the machine learning of the relation between tool wear, cutting parameters and surface layer properties is presented in this paper. The amount of obtained measurement data allows a fundamental analysis of the approach, which paves the way for further developments.

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Effect of tool wear on the surface integrity of Inconel 718 in face milling with cemented carbide tools

Cited by 40 publications

References 10 publications

Tool Life and Surface Integrity Characteristics of SLM and C&W Inconel 718 in Dry and MQL Condition

Tool Life and Surface Integrity Characteristics of SLM and C&W Inconel 718 in Dry and MQL Condition

Instantaneous friction energy consumption and its evolution of high energy efficiency milling cutter

Machine Learning of Surface Layer Property Prediction for Milling Operations

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Effect of tool wear on the surface integrity of Inconel 718 in face milling with cemented carbide tools

Cited by 40 publications

References 10 publications

Tool Life and Surface Integrity Characteristics of SLM and C&amp;W Inconel 718 in Dry and MQL Condition

Tool Life and Surface Integrity Characteristics of SLM and C&amp;W Inconel 718 in Dry and MQL Condition

Instantaneous friction energy consumption and its evolution of high energy efficiency milling cutter

Machine Learning of Surface Layer Property Prediction for Milling Operations

Contact Info

Product

Resources

About

Tool Life and Surface Integrity Characteristics of SLM and C&W Inconel 718 in Dry and MQL Condition

Tool Life and Surface Integrity Characteristics of SLM and C&W Inconel 718 in Dry and MQL Condition