GPR and RVM-Based Predictions of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel

2015 ◽  
pp. 736-762
Author(s):  
Pijush Samui ◽  
H. Yildirim Dalkilic
Keyword(s):  
Surface Roughness ◽  
Tool Steel ◽  
Basis Function ◽  
Feed Rate ◽  
Cold Work ◽  
Cutting Speed ◽  
Hole Quality ◽  
Roundness Error ◽  
Aisi D2 ◽  
Cold Work Tool Steel

This chapter examines the capability of Gaussian Process Regression (GPR) and Relevance Vector Machine (RVM) for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel. This chapter uses GPR and RVM as regression techniques. The database contains information about cutting tool, feed rate, cutting speed, surface roughness, and roundness error. Cutting tool, feed rate, and cutting speed are considered inputs of GPR and RVM. The outputs of GPR and RVM are surface roughness and roundness error. In RVM, radial basis function is adopted as kernel function. GPR uses radial basis function as covariance function. The obtained variance can be used to determine uncertainty. A sensitivity analysis is also carried out. This chapter gives robust models based on RVM and GPR for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel.

Determination of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel Using MPMR, MARS and LSSVM

2014 ◽  
Vol 13 (04) ◽  
pp. 237-246 ◽  
Author(s):  
Pijush Samui
Keyword(s):  
Tool Steel ◽  
Cold Work ◽  
Cutting Speed ◽  
Least Square ◽  
Support Vector ◽  
Hole Quality ◽  
Aisi D2 ◽  
Cemented Carbide Drills ◽  
Coated Cemented Carbide ◽  
Cold Work Tool Steel

This paper adopts Minimax Probability Machine Regression (MPMR), Multivariate Adaptive Regression Spline (MARS), and Least Square Support Vector Machine (LSSVM) for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer- and TiAlN/TiN multilayer-coated-cemented carbide drills. MPMR is a probabilistic model. MARS is a nonparametric regression technique. LSSVM is developed based on statistical learning algorithm. Cutting tool (t), Feed rate (fr)(mm/rev), and Cutting speed (v)(m/min) have been adopted as inputs of MPMR, MARS, and LSSVM. The output of MPMR, MARS, and LSSVM is Surface roughness (rs) (μm) and Roundness error (re) (μm). A comparative study has been presented between the developed models. The results show that the developed model gives excellent performance.

ANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steel

2013 ◽  
Vol 68 (1-4) ◽  
pp. 197-207 ◽  
Author(s):  
Sıtkı Akıncıoğlu ◽  
Faruk Mendi ◽  
Adem Çiçek ◽  
Gülşah Akıncıoğlu
Keyword(s):  
Tool Steel ◽  
Cold Work ◽  
Hole Quality ◽  
Aisi D2 ◽  
Cold Work Tool Steel

scholarly journals Parametric Optimization of Trochoidal Step on Surface Roughness and Dish Angle in End Milling of AISID3 Steel Using Precise Measurements

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1335 ◽  
Author(s):  
Santhakumar J ◽  
Mohammed Iqbal U
Keyword(s):  
Surface Roughness ◽  
Tool Steel ◽  
Process Parameters ◽  
Feed Rate ◽  
End Milling ◽  
Cold Work ◽  
Practical Importance ◽  
Research Work ◽  
Cutting Speed ◽  
Optimum Process

Tool steel play a vital role in modern manufacturing industries due to its excellent properties. AISI D3 is a cold work tool steel which possess high strength, more hardenability and good wear resistance properties. It has a wide variety of applications in automobile and tool and die making industries such as blanking and forming tools, high stressed cutting, deep drawing and press tools. The novel ways of machining these steels and finding out the optimum process parameters to yield good output is of practical importance in the field of research. This research work explores an attempt to identify the optimized process parameter combinations in end milling of AISI D3 steel to yield low surface roughness and maximum dish angle using trochoidal milling tool path, which is considered as a novelty in this study. 20 experimental trials based on face centered central composite design (CCD) of response surface methodology (RSM) were executed by varying the input process factors such as cutting speed, feed rate and trochoidal step. Analysis of variance (ANOVA) was adopted to study the significance of selected process parameters and its relative interactions on the performance measures. Desirability-based multiple objective optimization was performed and the mathematical models were developed for prediction purposes. The developed mathematical model was statistically significant with optimum conditions of cutting speed of 41m/min, feed rate of 120 mm/min and trochoidal step of 0.9 mm. It was also found that the deviation between the experimental and predicted values is 6.10% for surface roughness and 1.33% for dish angle, respectively.

The effectiveness of coatings when turning hardened cold work tool steel AISI D2 (60 HRC)

2008 ◽  
Vol 4 (1) ◽  
pp. 63
Author(s):  
Ahmad Kamely Mohamad ◽  
Noordin Mohd Yusof ◽  
Ali Ourdjini ◽  
Vellore Chelvaraj Venkatesh
Keyword(s):  
Tool Steel ◽  
Cold Work ◽  
Aisi D2 ◽  
Steel Aisi ◽  
Cold Work Tool Steel

Determining Ms temperature on a AISI D2 cold work tool steel using magnetic Barkhausen noise

2013 ◽  
Vol 577 ◽  
pp. S726-S730 ◽  
Author(s):  
Edgar Apaza Huallpa ◽  
J. Capó Sánchez ◽  
L.R. Padovese ◽  
Hélio Goldenstein
Keyword(s):  
Tool Steel ◽  
Cold Work ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Aisi D2 ◽  
Cold Work Tool Steel

Acoustic Emission and Fracture Characteristics of AISI D2 Tool Steel Tempered at Different Temperatures

2010 ◽  
Author(s):  
M. Ahmadi Najafabadi ◽  
J. Teymuri Shandi
Keyword(s):  
Heat Treatment ◽  
Acoustic Emission ◽  
Tool Steel ◽  
Cold Work ◽  
Tempering Temperature ◽  
Average Value ◽  
Different Temperatures ◽  
Aisi D2 ◽  
Cold Work Tool Steel ◽  
Ae Signals

Acoustic emission (AE) has been known as an excellent technique to monitor crack propagation and fracture mechanism. For more domination on AE behavior of materials, comprehensive knowledge on effective parameters is necessary. Heat treatment as one of the important factors on AE characteristics of a material must be considered. This investigation is primarily aimed at studying the effect of tempering heat treatment on characteristics of acoustic emission signals monitored during tension tests of a cold-work tool steel. Single edge notched samples of AISI D2 cold-work tool steel were prepared. Then, respectively annealing, austenitizing and tempering were performed. Tempering was carried out at 5 different temperatures from 0 to 575 C. Finally, samples were loaded at tension and AE signals recorded synergistically. Analyzing of the characteristics of AE signals showed that: (a) In all tempering conditions, the AECC increases slowly at the beginning and rapidly at the point of crack growth, although at higher tempering temperatures we have gradual rise in AECC plot; (b) Increasing tempering temperature, average value of AE count number, amplitude, energy and peak frequency decreases; (c) At 525 C, because of secondary hardening, average value of investigated AE parameters increase strongly and (d) analyzing the relation between fracture mode, AE characteristics and tempering temperature showed that special AE behavior of specimens tempered at 525 C is because of the transformation of retained austenite in ferritic matrix.

Microstructure and mechanical behavior of pulsed laser surface melted AISI D2 cold work tool steel

2013 ◽  
Vol 265 ◽  
pp. 653-662 ◽  
Author(s):  
N. Yasavol ◽  
A. Abdollah-zadeh ◽  
M. Ganjali ◽  
S.A. Alidokht
Keyword(s):  
Mechanical Behavior ◽  
Tool Steel ◽  
Cold Work ◽  
Pulsed Laser ◽  
Laser Surface ◽  
Aisi D2 ◽  
Cold Work Tool Steel

Micro-Milling of Hardened AISI D2 Tool Steel

2012 ◽  
Vol 445 ◽  
pp. 62-67 ◽  
Author(s):  
J.B. Saedon ◽  
S.L. Soo ◽  
D.K. Aspinwall ◽  
A. Barnacle
Keyword(s):  
Tool Wear ◽  
Tool Steel ◽  
Feed Rate ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Operating Conditions ◽  
Micro Milling ◽  
Burr Formation ◽  
Depth Of Cut ◽  
Aisi D2

The paper presents an experimental investigation into the slotting of hardened AISI D2 (~62HRC) tool steel using 0.5mm diameter coated (TiAlN) tungsten carbide (WC) end mills. SEM analysis of tool morphology and coating integrity was undertaken on all tools prior to testing. Tool wear details are given based on resulting cutter diameter and slot width reduction. In addition, cutting forces are also presented together with details of workpiece burr formation. A full factorial experimental design was used with variation of cutting speed, feed rate and depth of cut, with results evaluated using analysis of variance (ANOVA) techniques. Parameter levels were chosen based on microscale milling best practice and results from preliminary testing. Main effects plots and percentage contribution ratios (PCR) are included for the main factors. Cutting speed was shown to have the greatest effect on tool wear (33% PCR). When operating at 50m/min cutting speed with a feed rate of 8µm/rev and a depth of cut of 55µm, cutter diameter showed a reduction of up to 82µm for a 520mm cut length. SEM micrographs of tool wear highlighted chipping / fracture as the primary wear mode with adhered workpiece material causing further attritious wear when machining was continued up to 2.6m cut length. All tests produced burrs on the top edges of the slots which varied in size / width to a lesser or greater degree. Under the most severe operating conditions, burr width varied from approximately 50µm to more than 220µm over the 520mm cut length. Cutting forces in general were less than 12N up to test cessation.

DEVELOPMENT OF SURFACE ROUGHNESS PREDICTION MODEL FOR HARD TURNING ON AISI D2 STEEL USING CUBIC BORON NITRIDE INSERT

2017 ◽  
Vol 80 (1) ◽  
Author(s):  
Amrifan Saladin Mohruni ◽  
Muhammad Yanis ◽  
Edwin Kurniawan
Keyword(s):  
Surface Roughness ◽  
Boron Nitride ◽  
Feed Rate ◽  
Hard Turning ◽  
Cubic Boron Nitride ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Aisi D2 Steel ◽  
Aisi D2 ◽  
D2 Steel

Hard turning is an alternative to traditional grinding in the manufacturing industry for hardened ferrous alloy material above 45 HRC. Hard turning has advantages such as lower equipment cost, shorter setup time, fewer process steps, greater part geometry flexibility and elimination of cutting fluid. In this study, the effect of cutting speed and feed rate on surface roughness in hard turning was experimentally investigated. AISI D2 steel workpiece (62 HRC) was machined with Cubic Boron Nitride (CBN) insert under dry machining. A 2k-factorial design with 4 centre points as an initial design of experiment (DOE) and a central composite design (CCD) as augmented design were used in developing the empirical mathematical models. They were employed for analysing the significant machining parameters. The results show that the surface roughness value decreased (smoother) with increasing cutting speed. In contrary, surface roughness value increased significantly when the feed rate increased. Optimum cutting speed and feed rate condition in this experiment was 105 m/min and 0.10 mm/rev respectively with surface roughness value was 0.267 µm. Further investigation revealed that the second order model is a valid surface roughness model, while the linear model cannot be used as a predicted model due to its lack of fit significance.

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