Model-Based Analysis of the Surface Generation in Microendmilling—Part II: Experimental Validation and Analysis

2006 ◽  
Vol 129 (3) ◽  
pp. 461-469 ◽  
Author(s):  
Xinyu Liu ◽  
Richard E. DeVor ◽  
Shiv G. Kapoor
Keyword(s):  
Surface Roughness ◽  
Stochastic Models ◽  
Feed Rate ◽  
Deterministic Model ◽  
Surface Generation ◽  
Tool Geometry ◽  
Process Conditions ◽  
Deterministic Models ◽  
Floor Surface ◽  
Wide Range

The surface-generation models for the microendmilling process developed in Part I (Liu, DeVor, and Kapoor, 2007, J. Manuf. Sci. Eng., 129(3), pp. 453–460) are experimentally calibrated and validated. Partial immersion peripheral downmilling and full-immersion slotting tests are performed over a wide range of feed rates (0.25–12μm∕flute) using two tools with different edge radii (3μm and 2μm) and runout levels (2μm and 3μm) for the investigation of sidewall and floor surface generation, respectively. The deterministic models are validated using large feed-rate tests with errors within 18% for both sidewall and floor surfaces. For low feed-rate tests, the stochastic portion of the surface roughness data are determined from the observed roughness data and the validated deterministic model. The stochastic models are then calibrated and validated using independent data sets. The combination of the deterministic and stochastic models predicts the total surface roughness within 15% for both the sidewall and floor surface over a range of feed rates. The models are then used to simulate micromachined surfaces under a variety of conditions to gain a deeper understanding of the effects of tool geometry (edge radius and edge serration), process conditions, tool tip runout, process kinematics and dynamics on the machined surface roughness.

Surface Roughness Analysis of Carbon/Glass Hybrid Polymer Composites in Drilling Process Based on Taguchi and Response Surface Methodology

2015 ◽  
Vol 1119 ◽  
pp. 622-627 ◽  
Author(s):  
Chye Lih Tan ◽  
Azwan Iskandar Azmi ◽  
Noorhafiza Muhammad
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Regression Model ◽  
Response Surface ◽  
Feed Rate ◽  
Hybrid Composite ◽  
Spindle Speed ◽  
Tool Geometry ◽  
Secondary Process ◽  
Drilling Process

Drilling is an essential secondary process for near net-shape of hybrid composite as to achieve the required dimensional tolerances prior to final application. Dimensional tolerance is often influenced by the surface integrity or surface roughness of the workpart. Thus, this paper aims to employ the Taguchi and response surface methodologies in minimizing the surface roughness of drilled carbon-glass hybrid fibre reinforced polymer (CGCG) using tungsten carbide, K20 drill bits. The effects of spindle speed, feed rate and tool geometry on surface roughness were evaluated and optimum cutting conditions for minimizing the aforementioned response was determined. Subsequently, response surface methodology (RSM) was utilised in finding the empirical relationships between experimental parameters and surface roughness based on the Taguchi results. The experimental analyses reveal that surface roughness is greatly influenced by feed rate and tool geometry rather than the spindle speed. This is due to the increment of feed that attributed to the increased strain rate and hence, deteriorated the surface roughness of the hybrid composite. The predicted results (via regression model) and theoretical results (via additivity law) were in good agreement with experiment results. This indicates that the regression model from response surface methodology (RSM) can be used to predict the surface roughness in machining of CGCG hybrid composite.

Study of Machining Parameter Optimization in Drilling of Magnesium Based Hybrid Composites Reinforced with SiC/CNT

2020 ◽  
Vol 846 ◽  
pp. 42-46 ◽  
Author(s):  
J.S.Suresh Babu ◽  
Min Heo ◽  
Chung Gil Kang
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Hybrid Composites ◽  
Spindle Speed ◽  
Influential Factor ◽  
Burr Formation ◽  
Machining Parameters ◽  
Matrix Composites ◽  
Wide Range ◽  
Machining Parameter

Recently, researchers and engineers have been interested in the development of hybrid metal matrix composites (HMMCs) for the applications of automotive and aerospace industries owing to their superior properties due to the usage a wide range of material combinations in its manufacturing. The present study focuses on the machining of magnesium based hybrid composites reinforced with CNT (1vol.%) and SiC (2vol.%).The influence of machining parameters such as spindle speed, feed rate, drill diameter and point angle on burr formation and surface roughness on drilling the composites were investigated using Taguchi method. The drilling parameters were optimized by using ANOVA experimental design and also find out the percentage of contribution of each factor. Based on the results, the most influential factor for the burr thickness was spindle speed and point angle. While spindle speed and feed rate were the influencing factors for surface roughness. The analysis revealed that burr height, burr thickness, and surface roughness decreases significantly with an increase in spindle speed.

scholarly journals On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 678 ◽  
Author(s):  
Michail Vlysidis ◽  
Yiannis Kaznessis
Keyword(s):  
Stochastic Models ◽  
Deterministic Model ◽  
Kinetic Constant ◽  
Stable Solution ◽  
System Size ◽  
Stationary Probability ◽  
Stationary Probability Distribution ◽  
Deterministic Models ◽  
Chemical Reaction Kinetics ◽  
Schlögl Model

Deterministic and stochastic models of chemical reaction kinetics can give starkly different results when the deterministic model exhibits more than one stable solution. For example, in the stochastic Schlögl model, the bimodal stationary probability distribution collapses to a unimodal distribution when the system size increases, even for kinetic constant values that result in two distinct stable solutions in the deterministic Schlögl model. Using zero-information (ZI) closure scheme, an algorithm for solving chemical master equations, we compute stationary probability distributions for varying system sizes of the Schlögl model. With ZI-closure, system sizes can be studied that have been previously unattainable by stochastic simulation algorithms. We observe and quantify paradoxical discrepancies between stochastic and deterministic models and explain this behavior by postulating that the entropy of non-equilibrium steady states (NESS) is maximum.

Field Observations of Tracer Dispersion Under Natural Flow Conditions in an Unconfined Sandy Aquifer

1979 ◽  
Vol 14 (1) ◽  
pp. 1-18 ◽  
Author(s):  
E.A. Sudicky ◽  
J.A. Cherry
Keyword(s):  
Analytical Solution ◽  
Groundwater Flow ◽  
Solute Transport ◽  
Deterministic Model ◽  
Test Site ◽  
Chloride Salt ◽  
Transport Parameters ◽  
Deterministic Models ◽  
Wide Range ◽  
Sandy Aquifer

Abstract ABSTRACT An exceptionally detailed field determination of the solute transport parameters was performed in an unconfined sandy aquifer near an abandoned landfill at the Canadian Forces Base at Borden, Ontario. The test site is located above the contaminant plume originating from the landfill. The aquifer consists of slightly stratified sands with minor laminations. A chloride salt solution was injected into a two m3 volume of aquifer about one meter below the water-table and then migration of the tracer occurred under the natural hydraulic gradient. The migration of the chloride pulse was monitored in detail using a three-dimensional array of bundle-type multilevel samplers. Hydraulic head measurements in the zone of transport were obtained from a network of miniature piezometers. The test results demonstrated the influence of zones of local aquifer heterogeneity on solute migration rates and the ability of a porous medium to disperse solutes in these zones. Different rates of groundwater flow between a fast and slow transport zone caused the pulse to split into two halves. Each half was found to be Gaussian in shape in accord with the classical theory of solute transport. The measured chloride distributions closely fit an analytical solution of the advection-dispersion equation. Dispersivity values for chloride obtained from the analytical solution increased with mean travel distance in the groundwater flow domain, which suggests that calibration of a deterministic model at one spatial scale may lead to erroneous predictions when applied to a different scale. From this it is concluded that, if deterministic models are to yield useful predictions of contaminant migration, it will be necessary to establish scaling functions from studies of the variability of transport parameters in a wide range of hydrogeological settings.

scholarly journals Characterization and Modeling of Surface Roughness and Burr Formation in Slot Milling of Polycarbonate

2020 ◽  
Vol 4 (2) ◽  
pp. 59 ◽  
Author(s):  
David Adeniji ◽  
Julius Schoop ◽  
Shehan Gunawardena ◽  
Craig Hanson ◽  
Muhammad Jahan
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
Surface Finish ◽  
Feed Rate ◽  
Micro Milling ◽  
Burr Formation ◽  
Great Promise ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Thermoplastic Material

Thermoplastic materials hold great promise for next-generation engineered and sustainable plastics and composites. However, due to their thermoplastic nature and viscoplastic material response, it is difficult to predict the properties of surfaces generated by machining. This is especially problematic in micro-channel machining, where burr formation and excessive surface roughness lead to poor component-surface integrity. This study attempts to model the influence of size effects, which occur due to the finite sharpness of any cutting tool, on surface finish and burr formation during micro-milling of an important thermoplastic material, polycarbonate. Experimental results show that the depth of cut does not affect either surface finish or burr formation. A proposed new sideflow model shows the dominant effect of cutting-edge radius and feed rate on surface finish, while tool edge roughness, coating and feed rate have the most pronounced influence on burr formation. Overall, a good agreement between the experimental data and the proposed size effect model for the machining of thermoplastic material was found. Based on these results, tool geometry and process parameters may be optimized for improved surface integrity of machined thermoplastic components.

scholarly journals FEM-Based Study of Precision Hard Turning of Stainless Steel 316L

Materials ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 2522 ◽  
Author(s):  
Ahmed Elkaseer ◽  
Ali Abdelaziz ◽  
Mohammed Saber ◽  
Ahmed Nassef
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Process Parameters ◽  
Chip Formation ◽  
Feed Rate ◽  
Cutting Conditions ◽  
Cutting Process ◽  
Surface Generation ◽  
Stainless Steel 316L ◽  
Edge Radius

This study aims to investigate chip formation and surface generation during the precision turning of stainless steel 316L samples. A Finite Element Method (FEM) was used to simulate the chipping process of the stainless steel but with only a restricted number of process parameters. A set of turning tests was carried out using tungsten carbide tools under similar cutting conditions to validate the results obtained from the FEM for the chipping process and at the same time to experimentally examine the generated surface roughness. These results helped in the analysis and understanding the chip formation process and the surface generation phenomena during the cutting process, especially on micro scale. Good agreement between experiments and FEM results was found, which confirmed that the cutting process was accurately simulated by the FEM and allowed the identification of the optimum process parameters to ensure high performance. Results obtained from the simulation revealed that, an applied feed equals to 0.75 of edge radius of new cutting tool is the optimal cutting conditions for stainless steel 316L. Moreover, the experimental results demonstrated that in contrast to conventional turning processes, a nonlinear relationship was found between the feed rate and obtainable surface roughness, with a minimum surface roughness obtained when the feed rate laid between 0.75 and 1.25 times the original cutting edge radius, for new and worn tools, respectively.

scholarly journals Study The Effect of Process Parameters of CNC Milling Surface Generation Using Al-alloy 7024

2019 ◽  
Vol 12 (3) ◽  
pp. 103-112
Author(s):  
Nareen Hafidh Obaeed
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Surface Finish ◽  
Feed Rate ◽  
Spindle Speed ◽  
Surface Generation ◽  
Depth Of Cut ◽  
Al Alloy ◽  
Cnc Milling ◽  
Tool Diameter

A wonderful unique research developments in modeling surface roughness and optimization of the predominant parameters to get a surface finish of desired level since only suitable selection of cutting parameters can get a better surface finish, so the objective of this work is to study the milling process parameters which include tool diameter, feed rate, spindle speed, and depth of cut resulting in optimal values of the surface roughness during machining AL-alloy 7024. The machining operation implemented on XK7124 3-axis CNC milling machine. The effects of the selected parameters on the chosen characteristics have been accomplished using Taguchi’s parameter design approach. The parameters considered are – depth of cut with two levels (0.2, 0.5 mm), tool diameter with two levels (6, 8 mm), spindle speed with two levels (1000, 2500 rpm), and finally feed rate with two levels (200, 500 mm/min). Analysis of the results showed that the optimal settings for low values of surface roughness are large tool diameter (8 mm), high spindle speed (2500 r.p.m), low feed rate (200 mm/min) and high depth of cut (0.5 mm). Response Table for mean of surface roughness showed that tool diameter has the most effected factors (rank one) followed by feed rate (rank two) then depth of cut which is the third effected factors and finally spindle speed with the less effected factors of surface roughness (rank four).

scholarly journals Drilling Process on CFRP: Multi-Criteria Decision-Making with Entropy Weight Using Grey-TOPSIS Method

2020 ◽  
Vol 10 (20) ◽  
pp. 7207
Author(s):  
Quang-Phuoc Tran ◽  
Van-Nhat Nguyen ◽  
Shyh-Chour Huang
Keyword(s):  
Decision Making ◽  
Surface Roughness ◽  
Feed Rate ◽  
Twist Angle ◽  
Tool Geometry ◽  
Main Body ◽  
Drilling Process ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Machining Parameter

Moisture strongly affects the quality and mechanical specificity of carbon fiber reinforced plastic (CFRP) when using lubrication fluids during machining, and the significant impact of the cutting tool geometry and cryogenic gas cooling on CFRP machining capabilities are observed. The main body of this paper aims at making decisions about the optimum parameter of the drilling process while machining on CFRP base on the grey relational coefficient embed to the technique for order of preference by similarity to an ideal solution (Grey-TOPSIS). The entropy method was used to determine the weight of decision-making for handling a multiple measure decision-making response. The twist angle of the tool drill, lubrication, and feed rate were used as the input variables, and were analyzed while taking into account several multi-response outputs, such as the surface roughness, uncut fiber, and delamination. The result showed that a feed rate of 228 mm/min, the high-helix twist angle, and cryogenic CO2 lubrication leads the calculated value to close the relative value, which minimizes the value of the surface roughness, the uncut fiber, and the delamination. Finally, verification of the valid effect of each parameter process was conducted using analysis of variance. The results indicated that the lubrication was the highest remarkable criterion on the uncut fiber, the delamination, and the surface roughness. By integrating the advantage of grey systems theory, and the technique for order preference by similarity to an ideal solution, to evaluate and optimize the machining parameter, the results indicate that the proposed model is useful to facilitate the multi-criteria decision-making problem under the environment of uncertainty and vagueness. This relatively advanced approach is very effectual in rejecting process variation and a great assistive strategy than other multi-criteria decision-making approaches.

Simulation of Machined Surface Formation While Honing

2004 ◽  
Author(s):  
Alexander M. Gouskov ◽  
Sergey A. Voronov ◽  
Eric A. Butcher ◽  
Subhash C. Sinha
Keyword(s):  
Dynamic Model ◽  
Graphical Representation ◽  
Axial Direction ◽  
Surface Generation ◽  
Process Conditions ◽  
Initial Surface ◽  
Machined Surface ◽  
Surface Formation ◽  
Expansion Pressure ◽  
Wide Range

The dynamics of deep hole honing is considered. The mathematical model of the process including the dynamic model of the tool and the interaction of the workpiece surface and honing sticks is analyzed. The honing tool is modeled as a continuous slender beam with a honing mandrel attached at the intermediate cross section. A single row of stones tool rotates and has reciprocational motion in the axial direction. The honing stones are expanded to the machined surface by a special rigid mechanism that provides cutting of workpiece cylindrical surface. The removal of chip and the tool vibrations cause the variation of expansion pressure and depend on the surface state formed by previous honing stone. The equations of new surface formation are separated as a specific set of the dynamic model. These equations inherently consider the regenerative effect of oscillations during machining. The numerical algorithm of machined surface generation has been developed which facilitates the 3D graphical representation and evaluation of the topography of the generated surface. The simulation model accounts for not only the nominal tool motion but also takes into account errors during machining such as tool components deformations and vibrations, tool runout, as well as initial surface distortions produced by previous operation. Based on the surface formation model software for evaluation of typical surface quality and accuracy criteria such as eccentricity, out-of-round, conicity, barrel, axial waviness, misalignment, faceting has been developed. The expansion pressure, tool stiffness, and technology conditions are considered as varying parameters since their influence on the process are different. The process productivity and accuracy can be improved by choosing rational conditions evaluated by simulation. The corresponding models and results of numerical simulation are presented. All the results are given in dimensionless form and therefore they are applicable to a wide range of real manufacturing process conditions. The model of new surface formation presented allows the simulation of the machined surface topography variation in time and to predict workpiece accuracy and possible correction of surface errors.

scholarly journals Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)

2021 ◽  
Vol 17 (2) ◽  
pp. 8-17
Author(s):  
Osamah F. Abdulateef
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Feed Rate ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Process Conditions ◽  
The Impact

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with,  an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.

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