Multi response optimization of CNC end milling of AISI H11 alloy steel for rough and finish machining using TGRA

Singh, Premlata; Saini, Pardeep; Kumar, Deepak

doi:10.1016/j.matpr.2020.02.544

Cited by 17 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Higher the feed rate, more amount of force is required to remove the material, which in turn increases the TW. As the FR and TW is more, the SR is also found to be higher among the selected input conditions [22]. Hence, the FR is found to be the most influencing factor in deciding the machined sample quality.…”

Section: A) B)mentioning

confidence: 85%

Multi Objective Study on Machining Characteristics of AISI H-11 Tool Steel Prepared by Different Processing Techniques

Margabandu¹,

Ramanujam²

2021

JESA

View full text Add to dashboard Cite

In the current study, hard turning of AISI H11 tool steel is done using TiAlN coated cutting tool. The workpiece is prepared by three different processing conditions (Solution treated, Heat treated, Cryogenic treated). The machining performance are studied by the input parameters; cutting speed (60, 90, 120 m/min) and feed rate (0.06, 0.12, 0.18 mm/rev) with a constant depth of cut of 0.5mm using Taguchi’s L9 design. The responses like, surface quality, wear on the tool, and forces generated for all three specimens are studied. The hardness of all three components is measured using Vicker’s micro-hardness tester. The experimental results proved that feed rate is the most influential parameter in deciding, surface roughness, cutting force and tool wear. The utility concept approach is applied and found, solution treated sample with 120 m/min of cutting speed and 0.06 mm/rev of feed rate produces optimal results in all three response criterion.

show abstract

Section: A) B)mentioning

confidence: 85%

Multi Objective Study on Machining Characteristics of AISI H-11 Tool Steel Prepared by Different Processing Techniques

Margabandu¹,

Ramanujam²

2021

JESA

View full text Add to dashboard Cite

show abstract

“…Minitab is effective to plan the experiments and to generate the analysis of variance (ANOVA) of outcomes. Mahajan [25], Basavarajappa et al [26], Mitra et al [27], Montgomery [28] & Singh et al [29] are the useful texts and references for details on application of Taguchi's methodology.…”

Section: Wear Characteristicsmentioning

confidence: 99%

Sliding wear characteristics of Al-4032/SiC/GMP hybrid composites using L-16 orthogonal array

Kumar

Singh

2022

Eng. Res. Express

Self Cite

View full text Add to dashboard Cite

The wear characteristics of the aluminium alloy (Al-4032) matrix-based hybrid composites (AMHCs) has been investigated through the DUCOM pin-on-disc tribometer. The AMHCs have been fabricated through the bottom pouring vacuum stir casting set up, using the mixture of silicon carbide (SiC) and granite marble powder ceramic particles in equal proportion, as the hybrid reinforcement at 0, 3, 6, 9 weight%. The mass loss has been considered as the response parameter and ‘Smaller-the-Better’ criteria has been adopted as the objective model in the study. The optimization of the control parameters i.e. composition, normal load, sliding time and sliding speed has been produced using main effect of means and Analysis of Variance (ANOVA) methods. It is observed that all four control parameters have considerable influence on the wear characteristics of the AMHCs. The wear resistance of the AMHCs increases with increase in the amount of reinforcement up to 6%, followed by a decreasing trend beyond this value. This may be due to agglomeration of the reinforced particles at higher weight fraction. The percentage contribution of the control parameters such as composition, normal load, sliding time and sliding speed is 39.78%, 20.91%, 17.39% and 20.02% respectively.

show abstract

“…Singh P K, et al [6] evaluated machining parameter behaviour while performing milling operation on AISI H11 steel. The study involved responses as R a and M rr .…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-response optimization of AISI H11 using Taguchi and Grey relational analysis

R¹,

Kumar

Rajkumar

2022

Mater. Res. Express

View full text Add to dashboard Cite

Tool & Die is one of the important department in the manufacturing industries that takes care of proper designing and fabrication of tools and dies required for the production. Mostly used alloying elements are chromium, tungsten, molybdenum and vanadium and are heat-treated. The objective of the work involves machinability study of AISI H11 chromium hot-worked steel extensively used for tool & die making. It deals with the analysis of machining parameters and its influences on the responses considered. Here the controlling parameters considered are cutting speed (Cs), feed rate (Fr) and depth of cut (Dc) and responses as surface roughness (Ra) and material removal rate (Mrr). Each controlling parameters are assigned with 3 levels and experimental runs were executed as per taguchi robust design. To determine multi-objective optimal solution, grey relational analysis (GRA) is employed. GRA is used as it provides a feasible platform for converting a multi-objective function into single-objective function. The experimental runs were performed as per L27 orthogonal array sequence in CNC end-milling. The responses recorded are then analyzed using analysis of variance (ANOVA) and optimal solutions are validated through confirmatory runs. The entire machinability study of AISI H11 is performed in two conditions involving rough machining and finish machining. This has been addressed based on the machining scenario followed in industries taking up job orders. The confirmatory results for rough machining recorded was found to be 0.7871 microns against predicted value of 0.7654 microns resulting with a deviation error of 2.88%. Similarly, for finish machining, confirmatory runs recorded 0.8579 microns against predicted value of 0.8357 microns resulting with a deviation error of 2.66%. The deviation level indicated above between predicted and observed values are minimum, which shows the reliability of the optimal solutions arrived in.

show abstract

Multi response optimization of CNC end milling of AISI H11 alloy steel for rough and finish machining using TGRA

Cited by 17 publications

References 25 publications

Multi Objective Study on Machining Characteristics of AISI H-11 Tool Steel Prepared by Different Processing Techniques

Multi Objective Study on Machining Characteristics of AISI H-11 Tool Steel Prepared by Different Processing Techniques

Sliding wear characteristics of Al-4032/SiC/GMP hybrid composites using L-16 orthogonal array

Multi-response optimization of AISI H11 using Taguchi and Grey relational analysis

Contact Info

Product

Resources

About