Surface Roughness In Drilling Research Articles

Statistical modelling and assessment of surface roughness in drilling of hybrid fiber composite

In this article, the effects of conventional drilling parameters on the surface roughness of holes in hybrid fibre composites were investigated and quantified. A sample of a hybrid fibre composite with E-glass, hemp, and flax fibre reinforcements was fabricated by the hand lay-up method and subjected to drilling tests under different operating conditions by varying the drilling process parameters (drill diameter, spindle speed, and feed rate). The average surface roughness (Ra in µm) of the drilled hole was measured for each set of conditions. The results were subjected to statistical analysis (ANOVA) to determine the effects of process parameters on the measured variable. The calculations show that a combination of drill diameter and spindle speed, as well as drill diameter and feed rate, are the most important determinants of variation in bore surface roughness. A simple regression equation with linear terms was then established to model the observed interactions between the input and output variables. The equation was able to accurately model the behaviour of surface roughness, showing that this methodology can be extrapolated for use with different machining processes and/or materials. The 3-factor analysis ANOVA, performed with a confidence level of 95% and a statistical significance of a p-value less than 0.05, showed that the drill diameter ranked first and made the largest contribution (82.394% contribution), followed by the feed rate (16.719% contribution) and the spindle speed (0.6199% contribution). Regression modelling using linear regression yielded an R2 value of 0.8015 and using the power-law equation yielded an R2 value of 0.8796.

EXPERIMENTAL EVALUATION OF PARAMETERS AFFECTING DELAMINATION FACTOR, TENSILE STRENGTH, THRUST FORCE AND SURFACE ROUGHNESS IN DRILLING OF GFRP

The drilling of glass fiber-reinforced plastic (GFRP) composites gained importance since they are used as structural components in many industries such as automotive, aerospace, and aviation. A large number of holes are needed in the industry to join these composite parts. However, some failures occur in drilling GFRP composites, such as delamination, matrix cracking, and fiber breakage. These failures not only reduce the strength of the composite, but also reduce its service life. Drilling parameters, drill bits, and woven types have a great influence on the occurrence of these failures by greatly influencing the thrust force, surface quality, and cutting temperature. In this study, the effects of drilling parameters and woven types of GFRP composites on thrust force, surface roughness, delamination factor, and cutting temperature were examined in the drilling of uni-directional (UD), [Formula: see text] and 0[Formula: see text] GFRP woven composites. The effects of drilling parameters and the delamination factor on the tensile strength of the drilled specimen were also investigated. The result of this study indicated that thrust force, delamination factor, and surface roughness increased with increasing cutting speed and feed rate. An increase in feed rate decreased the cutting temperature, while an increase in cutting speed increased the cutting temperature. Also, it was found that the delamination factor had a critical influence on the tensile strength of the GFRP composites.

Modelling and multiresponse optimization for minimizing burr height, thrust force and surface roughness in drilling of ferritic stainless steel

Although there have been many studies on the drillability of various grades of stainless steel, there is no scientific research on the drilling of ferritic stainless steel. Also, the burr at hole exit means the need for secondary machining operation and indirectly increases the production cost. Thus, this study focused on the modeling and minimizing burr height (Bh), thrust force (Fz) and surface roughness (Ra) during drilling of AISI 430 ferritic stainless steel with uncoated carbide drill under dry condition. Bh, Fz and Ra based on different cutting speed and feed rates were measured during drilling tests, and then cutting parameters were optimized by applying Taguchi based grey relational analysis. Moreover, the mathematical models were created by employing the response surface method to predict the machining outputs. The thrust force and the surface roughness decreased while the burr height increased with the increase in cutting speed. Uniform burr formation with drill cap was observed for all machining parameters under dry environment. The effect levels of feed rate and cutting speed on burr height were determined as 54.82% and 44.67%, respectively. These result shows that cutting speed is as important as the feed rate during the drilling of the ferritic stainless steel. In the current study, the best suitable levels of feed rate and cutting speed were detected as 0.12 mm/rev and 45 m/min for minimizing Bh, Fz and Ra. The coefficients of determination obtained by RSM indicated a relationship in high level between the cutting parameters and machining outputs.

Prediction of Surface Roughness in Drilling of Polymers using a Geometrical Model and Artificial Neural Networks

Polymeric materials are synthetic macromolecular products, of which, by mechanical or thermal processing, objects of various shapes can be obtained, with wide uses in industry and commerce. This paper deals with the roughness of surfaces obtained during drilling of three polymeric materials: polyamide - PA6, polyacetal - POM-C and high density polyamide - HDPE 1000. In the experimental research was used a EMCO MILL 55 milling machine numerical controlled and HS steel helical drills with two straight cutting edges with the diameter of �8 mm and �10 mm, respectively. Experimental determinations consisted in drilling of the polymeric materials by modifying some parameters of the cutting regime, and determining the roughness of the surface of the holes machined, using the Mitutoyo Surftest SJ-210 rough meter. The purpose of the paper is to predict the roughness of the machined surfaces as one of the important surface quality indicators by using a geometrical model and an artificial neural network (ANN) methodology.

An experimental investigation of thrust force, delamination and surface roughness in drilling of jute/carbon hybrid composites

Purpose This paper aims to deal with the influence of cutting parameters on drill thrust force, delamination and surface roughness in the drilling of laminated jute/carbon hybrid composites. Design/methodology/approach The hybrid composites were fabricated with four layers of fabrics, which are arranged in different sequences using the hand-layup technique. Drilling experiments involved drilling of 6 mm diameter holes on the prepared composite plates using high-speed steel and solid carbide drill materials. Analysis of variance was used to find the influence, percentage contribution and significance of drilling parameters on drilling-induced damages. Scanning electron microscopy analysis was also conducted to understand the fracture behavior and surface morphology of the drilled holes. Findings The experimental study reveals that the most significant effect was the feed rate influenced the drill thrust force and the drill speed influenced both delamination factor and surface roughness of hybrid fiber-reinforced composites. From observations, the suggested combination for drilling jute/carbon hybrid composites is carbide drill, spindle speed of 1,750 rpm and feed of 0.03 mm/rev. Originality/value The new lightweight and low-cost hybrid composites were developed by hybridizing jute with carbon fabrics in the epoxy matrix with interplay arrangements. The influence of cutting speed and feed rate on delamination damage and surface roughness in the drilling of hybrid composites have been experimentally evaluated.

Experimental Investigation, Modeling and Optimization of Circularity, Cylindricity and Surface Roughness in Drilling of PMMA Using ANN and ANOVA

In this paper, experimental investigation, modeling and optimization of the drilling of PMMA are performed using the Taguchi Design of Experiments (DOE), analysis of variance (ANOVA) and artificial neural networks (ANN) methods. Drilling experiments were conducted on PMMA to assess the impact of process parameters (drill diameter, spindle speed, and feed rate) on the hole-quality characteristics (surface roughness, circularity error, and cylindricity error). ANOVA was performed to identify the drilling parameters that have a statistically significant influence on the hole-quality characteristics. A predictive model for the hole-quality characteristics was derived using a four-layer ANN with a backpropagation algorithm and a sigmoidal transfer function at the hidden layers. The ANN model was able to accurately predict the hole-quality parameters with the absolute mean relative errors of the testing data in the limits of 3 to 7%. Based on the experimental results and analytical modeling, it was found that drilling of PMMA requires lower spindle speed and high feed rate when the integrity of the drill hole is the main quality criterion.

Investigation of the effect of process parameters on surface roughness in drilling of particleboard composite panels using adaptive neuro fuzzy inference system

ABSTRACTParticleboard wood composites are immensely used for many general and manufacturing applications. In this study, an analysis of various machining conditions has been performed to obtain good surface quality in the hole making of particleboard by varying the input parameters. The surface roughness (Ra) values obtained are ranging from 6.03 to 28.32 µm, and the minimum value is achieved at a higher speed, lower feed, and smaller point angle combinations. From ANOVA analysis, it has been observed that the model developed is adequate, and the influence on surface roughness is strong for feed (56.68%) followed by a point angle (28.42%) and then speed (9.37%). Mathematical models have been developed using two different criteria such as response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS) and compared for their effectiveness. The coefficient of determination (R2(R-Sq)) values of 98.5% (RSM) and 99.9% (ANFIS) indicates that the models are useful to predict Ra of particleboard. The average checking error percentage (0.20098) has been obtained for the ANFIS model trained using ‘gaussmf’ membership function with 100 epochs.

Effect of Cutting Parameters on Surface Roughness in Drilling of Al-SiC-Gr Composites Using RSM and Fuzzy Logic Modeling Method

Main aim of this paper is to analyze the machining a parameter a way to influence on surface roughness throughout drilling of composite material. During this test nearby the impact of speed and feed on drilling the composite. The drilling experiments were progressing to by CNC machine with solid carbide drill at a variety of spindle speeds and feed rates. Surface roughness of the drilled hole was investigated in Al-SiC-Graphite composite, the take a look at outcomes exhibit that the feed rate has more control on surface roughness. Surface roughness is considered as investigation result, and it is anticipated utilizing fuzzy logic. The predictable surface roughness values are compared with test outcome and response surface model. The outcomes decide that anticipated surface roughness values esteems ably by fuzzy modeling technique.

Application of Genetic Algorithm Technique for Machining Parameters Optimization in Drilling of Stainless Steel

Abstract This work is aimed at developing relations between the pertinent variables that affect drilling process of stainless steel using artificial neural network. The experiments were conducted on vertical CNC machining centre. The parameters used were spindle speed and feed rate. The effect of machining parameters on entry burr height, exit burr height and surface roughness was experimentally evaluated for different spindle speeds and feed rates. A model was established between the drilling parameters and experimentally obtained data using ANN. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the burr height and surface roughness in drilling of stainless steel. Genetic algorithm (GA) technique was used in this work to identify the optimized drilling parameters. Confirmation test was conducted with the optimized parameters and it was found that confirmation test results were similar to that of GA-predicted output values.

Study of influence of process parameters in drilling of glass fibre reinforced polymer composite with clam shell filler

Abstract This study focuses on assessing the effects of drill diameter, spindle speed and feed rate on delamination factor (both peel up and push out) and surface roughness in drilling of three different composite plates with 0, 10 and 20 wt% of clam shell powder filler in glass fibre reinforced composite using twist drill bit. Taguchi’s experimental design approach is used to develop a parametric analysis of drilling process. Drill diameter is the most effective parameter for delamination of composite followed by feed rate and spindle speed. The filler content has influence in determining the surface roughness.

Multi-response optimization of the thrust force, torque and surface roughness in drilling of glass fiber reinforced polyester composite using GRA-RSM

Glass fiber reinforced polymer (GFRP) composites are used in various applications across several indus�trial sectors. Drilling operation is an unavoidable machining process in the assembly zone dealing with GFRP composites as the composite structures are joined mainly through the fastening processes. GFRP drilling poses several issues compared to conventional metallic materials, which decreases the drilled hole quality. In this article, central composite design is used to provide the framework for the experimen�tal runs on glass fiber reinforced isophthalic polyester composite slabs. Grey relation analysis is used to optimize the drilling parameters: speed, feed and material thickness based on three output performance characteristics: thrust force, torque and drilled hole surface roughness. Analysis of variance (ANOVA) is used to find the percentage contribution of the drilling parameters. The experimental results indicate feed to be the most influential factor in the drilling of glass fiber reinforced isophthalic polyester compos�ites contributing 44.16% to the variation in the overall performance index, represented by the grey relation grade (GRG) for the given experimental range of control parameters. � 2019 Elsevier Ltd. All rights reserved

Multi response optimization of drilling performance of MWCNT filled banana-glass fibre reinforced composite

Natural fibres reinforced composites (NFRC) with their extended account of portion mankind are extremely significant in a broad variety of applications, and they battle and co-exist in the twenty-first century with man-made fibres, particularly as far as excellence, sustainability and cost-cutting measure of production are troubled. Numerous researchers have synthesized NFRCs using various natural fibres as well as matrix materials. Though, real-world applications of NFRCs involve some machining operations in order to complete the assembly of the parts. In this work, a technique based on the factorial design-based desirability analysis is used to determine the desired optimum machining parameters for minimum delamination, thrust force and surface roughness in drilling of MWCNT filled banana-glass natural fibres reinforced composites. The speed, federate, wt% MWCNT and point angle of drill are the most important parameters that characterize the machining operation and have been selected for investigation. Results indicated that point angle is playing major role on the responses followed by feed rate.

Experimental Investigation on Influence of Functionalized Multi-Walled Carbon Nanotubes on Surface Roughness in Drilling of CFRP Composites

This paper presents the effect of carboxyl functionalised multi-walled carbon nanotubes on the surface roughness of drilled holes in carbon fibre reinforced plastic (CFRP) laminates.

Measurement and analysis of temperature, thrust force and surface roughness in drilling of AA (6351)-B 4 C composite

In the present study, the drilling is performed on AA (6351)–B4C composite to analyze the influence of cutting parameters on temperature, thrust force and surface roughness. The TiN coated carbide drill tools with varying point angle such as 90°, 118° and 135° are used to conduct the experiments. Initially, the temperature rise during drilling and the effect of temperature on the chip formation are investigated by contact and non-contact measurement techniques. The reduced temperature is achieved at lower spindle speed and feed rate conditions. However, the improper selection of point angle leads to tool wear and produces poor surface finish. In addition, the thrust force generation and the delamination of the drill holes are assessed by varying the input process parameters. The thrust force is low while drilling with 135° point angle tool, but produced higher delamination. Finally, the surface roughness is examined and the Scanning Electron Microscopic (SEM) images are utilized to observe the drill hole surface morphology of the composite.

Investigation of Effect of Graphite Particles on Drillability of Metal Matrix Composite

The present article investigates the effect of graphite particles in the drilling of hybrid aluminium matrix composites (AMCs) using TiN coated carbide drills. Materials used for the present investigation are Al6063-aluminum alloy reinforced with alumina of size 20 microns and graphite of an average size 75 microns, which are produced through stir casting method. Experiments are conducted based on Taguchi’s method L27 orthogonal array on a vertical machining centre. A model is developed to correlate the drilling parameters with burr height and surface roughness using regression analysis. The results indicate that the developed model is suitable for prediction of burr height and surface roughness in drilling of hybrid AMCs.The influence of different parameters on Surface roughness and burr height of Al6063/Al2O3p/Grp composites has been analyzed through ANOVA table and contour graphs.DOI: http://dx.doi.org/10.5755/j01.ms.22.3.9724

Effect of Cutting Parameters on Thrust Force and Surface Roughness in Drilling of Al-2219/B4C/Gr Metal Matrix Composites

In aluminium matrix composites, reinforcement of hard ceramic particle present inside the matrix which causes tool wear, high cutting forces and poor surface finish during machining. This paper focuses on effect of cutting parameters on thrust force, surface roughness and burr height during drilling of MMCs. In the present work, discuss the influence of spindle speed and feed rate on drilling the pure base alloy (Al-2219), mono composite (Al- 2219+8% B4C) and hybrid composite (Al-2219+8%B4C+3%Gr). The composites were fabricated using liquid metallurgy route. The drilling experiments were conducted by CNC machine with TiN coated HSS tool, M42 (Cobalt grade) and carbide tools at various spindle speeds and feed rates. The thrust force, surface roughness and burr height of the drilled hole were investigated in mono composite and hybrid composite containing graphite particles, the experimental results show that the feed rate has more influence on thrust force and surface roughness. Lesser thrust force and discontinuous chips were produced during machining of hybrid composites when compared with mono and base alloy during drilling process. It is due to solid lubricant property of graphite which reduces the lesser thrust force, burr height and lower surface roughness. When machining with Carbide tool at low feed and high speeds good surface finish was obtained compared to other two types of cutting tool materials.

Surface Roughness in Drilling of Electrically Conductive Polyamide

In this study, drilling of unreinforced polyamide and carbon black reinforced electrically conductive polyamide was performed with drill tools having different drill point angle at different cutting speeds and feeds. The surface roughness of drilled holes was measured and the chip forms were investigated. The experimental studies were designed by utilizing Taguchi L9 orthogonal series in order to obtain optimum drilling parameters and analysis of variance (ANOVA) statistical method was used to specify the effects of the process parameters on the surface roughness.

Optimization of Surface Roughness in Drilling of GFRP Composite Using Harmony Search Algorithm

Optimization of Surface Roughness in Drilling of GFRP Composite Using Harmony Search Algorithm

Analysis of Surface Roughness in Drilling of Fly Ash Filled Carbon Fibre Reinforced Composites

Carbon fibre reinforced plastic (CFRP) composite materials has unique mechanical and functional properties due to the combination of its constituent materials that possesses desirable properties. The present paper focuses on analysis of surface roughness in drilling of fly ash filled carbon fibre reinforced composites produced through compression moulding route. Experiments are conducted on a radial drilling machine using Taguchi’s experimental design. Drilling test is carried out using coated HSS drill of 6 and 10 mm diameter. The effect of drill diameter, wt % of fly ash, spindle speed and the feed rate are investigated on surface roughness. The relative significance of the various parameters has been found using ANOVA.

Prediction of Surface Roughness in Drilling of Al 7075/10% - SiCp Composite under MQL Condition using Fuzzy Logic

The main aim of this study is to examine the influence of drilling parameters on Aluminium Metal Matrix Composite (Al7075/10% - SiCp). The Drilling experiments were conducted according to Taguchi L27 orthogonal array on Al7075/10% - SiCp composite with uncoated and coated HSS tools under MQL environment. The input parameters were used is speed, feed, tool material, point angle and cutting environment for 3 levels. This composite is fabricated using 53 μm sized SiC particles reinforced into aluminium matrix material via stir casting process. The surface roughness is considered as experimental result and it is predicted using fuzzy logic. The results specify that the predicted surface roughness values and experimental surface roughness values are close to each other and surface roughness can be predicated efficiently by fuzzy logic modeling technique.