An investigation into surface integrity of AISI P20 machined under the influence of spindle forced vibrations

Zahoor, Sadaf; Mufti, Nadeem Ahmad; Saleem, Muhammad Qaiser; Shehzad, Adeel

doi:10.1007/s00170-018-1804-5

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…It can also be noted that a p is proven statistically insignificant for R a as its pvalue is greater than the set value of , which is 0.05 or 5%. A similar behavior is reported by other studies [6,29].…”

Section: Fig 3: Main Effect Plots For Rasupporting

confidence: 92%

“…Despite the aforecited advantages, machinability of Ni-alloys is challenging for manufacturers due to their low thermal conductivity and high work-hardening characteristics [3][4]. Although, conventional machining of these advanced materials has an edge over non-conventional cutting methods in terms of low machining cost and high production rate [5][6], in the quest for achieving high productivity and quality of the machined surface, the tool life significantly suffers. The machining of these alloys involves an excessive amount of heat that cannot effectively be dissipated due to their low heat conductivity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Environmentally conscious machining of Inconel 718: surface roughness, tool wear, and material removal rate assessment

Zahoor

Ameen

Abdul‐Kader

et al. 2019

Int J Adv Manuf Technol

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Flood cooling is a typical strategy used in the machining of difficult-to-cut materials where high temperatures are produced. Several environmental and health concerns are associated with the cutting fluids employed during this technique. Vegetable oil-based fluids appear to be the best substitute to conventional mineral/synthetic oils due to their environmentally friendly, biodegradable, renewable, and less toxic properties. Therefore, this paper attempts to establish an environmentally conscious, flood-cooling alternative through replacing conventional fluids with a synthetic vegetable ester-based (Mecagreen 450) biodegradable oil to investigate the machinability aspects of Inconel 718. In addition to the cooling environment, cutting speed ( s), feed per tooth (ƒ z), and axial depth of cut (a p) have been used as control variables. A Taguchi L 9 array has been selected for the design of experiments (DOE). Parametric effects and microscopic analyses have been carried out to investigate the three response parameters, i.e., surface roughness (R a), tool wear, and material removal rate (MRR). Tool wear analysis is further supplemented with scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS).

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Section: Fig 3: Main Effect Plots For Rasupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Environmentally conscious machining of Inconel 718: surface roughness, tool wear, and material removal rate assessment

Zahoor

Ameen

Abdul‐Kader

et al. 2019

Int J Adv Manuf Technol

Self Cite

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show abstract

“…Furthermore, it is widely acknowledged that friction occurring at the tool-workpiece interface is a major factor negatively affecting the surface quality of machined parts [53]. In addition, the literature suggests that high surface roughness values are associated with small cutting speeds and high feed rates [54]. Specifically, poor surface quality at low cutting speeds is attributed to the high tensile stresses generated by friction, while the presence of uncut metal is reported to cause high surface roughness values at high feed rates due to the increased distance between two successive tool runs [55].…”

Section: Surface Roughnessmentioning

confidence: 99%

Analysis of Carbon Footprints and Surface Quality in Green Cutting Environments for the Milling of AZ31 Magnesium Alloy

et al. 2023

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This investigation delves into the effectiveness of employing vegetable-based cutting fluids and nanoparticles in milling AZ31 magnesium alloy, as part of the pursuit of ecologically sustainable manufacturing practices. The study scrutinizes three different cutting environments: (i) dry cutting; (ii) minimum quantity lubrication (MQL) with rice bran oil as the base oil and turmeric oil as an additive; and (iii) MQL with rice bran oil as the base oil, and turmeric oil and kaolinite nanoparticles as additives. Fuzzy logic was implemented to develop the design of experiments and assess the impact of these cutting environments on carbon emissions, surface quality, and microhardness. Upon conducting an analysis of variance (ANOVA), it was determined that all the three input parameters (cutting environment, cutting speed, and feed) greatly affect carbon emissions. The third cutting environment (MQL + bio-oils + kaolinite) generated the lowest carbon emissions (average of 9.21 ppm) and surface roughness value (0.3 um). Confirmatory tests validated that the output parameters predicted using the multiobjective genetic algorithm aligned well with experimental values, thus affirming the algorithm’s robustness.

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“…Different workpiece materials and initial material properties influence manufactured SLP as well [38][39][40]. Zahoor et al [41,42] report on the influence of tool vibrations, which are heavily dependent on the specific spindle and tool holder, on resulting SLP. Ulutan and Özel [43] published a comprehensive review on machining-induced SLP changes.…”

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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An investigation into surface integrity of AISI P20 machined under the influence of spindle forced vibrations

Cited by 11 publications

References 25 publications

Environmentally conscious machining of Inconel 718: surface roughness, tool wear, and material removal rate assessment

Environmentally conscious machining of Inconel 718: surface roughness, tool wear, and material removal rate assessment

Analysis of Carbon Footprints and Surface Quality in Green Cutting Environments for the Milling of AZ31 Magnesium Alloy

Machine Learning of Surface Layer Property Prediction for Milling Operations

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