On machining hardened steel AISI 4140 with self-propelled rotary tools: experimental investigation and analysis

Ahmed, Waleed; Hegab, Hussien; Mohany, Atef; Kishawy, Hossam A.

doi:10.1007/s00170-021-06827-8

Cited by 11 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Exceptional wear tolerance and extended tool life have also been reported in earlier investigations, thus emphasizing rotary tools for hard machining. Ahmed, et al [33] studied and evaluated the SPRT's functionality in the dry machining of hardened steel alloy. The results confirmed that the SPRT provided smaller cutting forces, fairly low flank tool wear and extremely low machining temperature than conventional tools.…”

Section: Literature Surveymentioning

confidence: 99%

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

et al. 2022

Self Cite

View full text Add to dashboard Cite

The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54–56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…RSM analyzes the influence of independent variables (cutting parameters) on a specific quality characteristic called response (R a ) [19]. The relationship between the measured responses and cutting settings is provided by Equation (1).…”

Section: Regression Modellingmentioning

confidence: 99%

“…High-thermal strength, wear resistance, and chemical stability are characteristics of hard-to-cut materials. These materials are challenging to a machine due to extreme wear and high heat concentrations created during the process [1] [2]. Excessive cutting tool wear is a major problem in conventional turning using a singlepoint cutting tool when machining difficult-to-cut materials.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Central Composite Design and Genetic Algorithm to Optimize Turning Parameters for Surface Roughness in Self-propelled Rotary Tools

Kouta

Elsanabary

2022

Port-Said Engineering Research Journal

View full text Add to dashboard Cite

This paper investigates experimentally the efficiency of a self-propelled rotary tool (SPRT) using carbide inserts during turning of K110 alloy steel. The cutting conditions, namely, cutting velocity v c (m/min), feed rate f (mm/rev), and depth of cut a p (mm) were interacted at a constant tilting angle of 20°, while considering the surface roughness (R a ) as performance criteria. The exploratory strategy is based on the central composite design (CCD), which was used to investigate at the influence of cutting boundaries on a superficial harshness level to observe the optimal cutting conditions. A second-order regression model was created. The performance parameters of the turning operation were studied using analysis of variance. A genetic algorithm (GA) was applied to optimize the SPRT cutting condition. The surface roughness and corresponding cutting conditions were optimized by creating a hybrid CCD-GA. The results showed that vc has the most impact on R a , followed by f and a p . The minimum R a value was 0.58 µm obtained at 140 m/min of v c , 0.04 mm/rev off, and 0.3 mm of a p . Finally, GA and hybrid CCD-GA optimization techniques have optimal surface roughness and compared with experimental results.

show abstract

“…A good agreement between the predictive and experimental results indicated that the developed model was reliable and useful. Ahmed et al emphasized that the rotary turning process provided lower cutting forces, flank wear, and cutting temperature, as compared to the fixed turning, while a better Ra could be obtained using the conventional one [17]. Umer et al revealed that lower values of the D and A could be applied to decrease the turning temperature, force, and stress of the tool [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Rotary Turning of Hardened Mold Steel for Energy Saving and Surface Roughness Improvements

Doan,

Nguyen,

Van

2023

Journal of Machine Engineering

View full text Add to dashboard Cite

In this investigation, the specific cutting energy (SCE) and average surface roughness (Ra) were decreased using the hard-rotary turning (HRT) factors, including the inclined angle (I), depth of cut (D), feed rate (f), and spindle speed (S). The Bayesian regularized feed-forward neural network was applied to develop the SCE and Ra models. The entropy method and vibration and communication particle swarm optimization (VCPSO) algorithm were employed to compute the weights and determine optimal factors. The optimizing outcomes presented that the optimal I, D, f, and S were 35 deg., 0.45 mm, 0.50 mm/rev., and 1200 rpm, respectively, while the SCE and Ra were decreased by 37.4% and 6.6%, respectively. The total turning cost was saved by 7.5% at the selected solution. The valuable outcomes could be applied to the practical HRT process to decrease performance measures, while the developed HRT operation could be utilized for machining difficult-to-cut materials.

show abstract

On machining hardened steel AISI 4140 with self-propelled rotary tools: experimental investigation and analysis

Cited by 11 publications

References 21 publications

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

Hybrid Central Composite Design and Genetic Algorithm to Optimize Turning Parameters for Surface Roughness in Self-propelled Rotary Tools

Multi-Objective Optimization of the Rotary Turning of Hardened Mold Steel for Energy Saving and Surface Roughness Improvements

Contact Info

Product

Resources

About