Force Based Model for Automated Surface Roughness Prediction in Slot Milling

2001 ◽  
Author(s):  
A. Sarhan ◽  
A. A. Nasr ◽  
R. M. El-Zahry
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Force ◽  
Control Surface ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Monitoring And Control ◽  
Slot Milling ◽  
On Line ◽  
And Control

Abstract Study was carried out to analyze the dynamic cutting signals of slot-milling process, in order to design automated on-line tool and surface roughness monitoring strategies, based on indices extracted from these signals, to automatically monitor and control surface roughness in slot milling. Especially designed and manufactured sensitive strain gage dynamometer was used to measure slot-milling radial and tangential forces during milling cycle. The dynamometer was calibrated in static and dynamic ranges. The effect of flank wear width on the magnitude of the cutting force harmonics was constructed as function of axial depth of cut, feed rate per tooth, specific cutting pressure of work material and instantaneous angle of rotation. The results were plotted at various cutting conditions in time and frequency domains. The tool wear was measured in an off-line manner using the tool maker’s microscope and interrelationships of cutting force harmonics and tool wear magnitude were constructed and were used in the computer simulation. Surface roughness was measured using surface meter (Surtronic 3+) with a portable printer. The cutting force signal harmonics were used to establish the proposed force based model to predict the surface roughness of the workpiece machined in slot-milling and examining this system by another experimental tests to define the reliability of the system and to define the percentage error of the system model. Hence, an index named as surface index (S.I) is extracted from ratio between first force amplitude at first significant frequency and first surface amplitude at the same frequency, to predict the surface roughness of the workpiece machined in slot-milling. This is to be employed in automated on-line quality management (monitoring and control) strategy.

An Experimental Study on Finish Dry Milling of AISI 321 Stainless Steel

2016 ◽  
Vol 861 ◽  
pp. 26-31 ◽  
Author(s):  
Peng Guo ◽  
Chuan Zhen Huang ◽  
Bin Zou ◽  
Jun Wang ◽  
Han Lian Liu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Tool Wear ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Dry Milling ◽  
Aisi 321 ◽  
Coated Cemented Carbide

The milling of AISI 321 stainless steel which has wide engineering applications particularly in automobile, aerospace and medicine is of great importance especially in the conditions where high surface quality is required. In this paper, L16 orthogonal array design of experiments was adopted to evaluate the machinability of AISI 321 stainless steel with coated cemented carbide tools under finish dry milling conditions, and the influence of cutting speed ( V ), feed rate ( f ) and depth of cut ( ap ) on cutting force, surface roughness and tool wear was analysed. The experimental results revealed that the cutting force decreased with an increase in the cutting speed and increased with an increase in the feed rate or the depth of cut. The tool wear was affected significantly by the cutting speed and the depth of cut, while the effect of the feed rate on the tool wear was insignificant. With the cutting speed increased up to 160 m/min, a decreasing tendency in the surface roughness was observed, but when the cutting speed was further increased, the surface roughness increased. The effect of the feed rate and the depth of cut on the surface roughness was slight.

Finish Cutting of Carbide Mold with Thermally Affected Layer by Diamond Tool

2012 ◽  
Vol 565 ◽  
pp. 382-387
Author(s):  
Kazuki Imazato ◽  
Koichi Okuda ◽  
Hiroo Shizuka ◽  
Masayuki Nunobiki
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Force ◽  
Diamond Tool ◽  
Depth Of Cut ◽  
Good Surface ◽  
Finish Cutting ◽  
Ultra Precision ◽  
Cutting Experiments ◽  
Edm Process

This paper deals with finish cutting of thermally affected layer on cemented carbide by a diamond tool in order to machine efficiently the carbide mold with high accuracy and good surface without a polishing. The microstructure of thermally affected layer left by EDM process was observed and analyzed by EPMA. Its hardness and thickness were measured. Subsequently, the cutting experiments were carried out by using a PCD tool and an ultra-precision cutting machine. The effects of the thermally affected layer on the surface roughness, the cutting force and the tool wear were investigated. As a result, it was confirmed that the cutting force decreased with an increase in the depth of cut. Furthermore, it was found that the tool wear and the surface roughness obtained by cutting the thermally affected layer were greater than those of the original workpiece.

Experimental Investigation of the Tool Wear and Tool Life in Micro Hard Milling

2013 ◽  
Author(s):  
Shreyas Shashidhara ◽  
Xinyu Liu ◽  
Weihang Zhu ◽  
James Curry ◽  
Victor Zaloom
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Abrasive Wear ◽  
Cutting Force ◽  
Tool Life ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Dry Machining ◽  
Edge Chipping ◽  
Edge Radius

The objective of this project is to experimentally investigate the influence of Minimum Quantity Lubrication (MQL) on tool wear and tool life in micro hardmilling. The experiments were performed on stainless steel using uncoated WC micro-mill with the nominal diameter of 508 microns. The tool wear is characterized by the volume of the material loss at the tool tip. In order to reveal the progression of the tool wear, the worn tool was examined periodically under SEM after a fixed amount of workpiece material removal (1.25 mm3 or 5 slots in this study). The tool life was characterized as the amount of material removed, instead of the conventional cutting times. The feedrate and the spindle speed were fixed, and two levels of axial depth of cut (50 and 75 microns) were compared. The higher depth of cut leads to longer tool life. The machining performance under MQL is superior to the dry machining for both process conditions in terms of the tool life. The cutting forces in feed direction and the surface roughness at the bottom of the slots were also examined during the experiments. The magnitude of the machining forces showed cyclic pattern for both MQL and dry machining. The SEM images and the cutting force signals suggested that the dominant mode of the tool wear in micro-milling is edge chipping and abrasive wear at the tool tip. The loss of the micro-grain of WC at the cutting edge leads to edge chipping, which reduces the effective cutting diameter; the abrasive wear enlarge the edge radius, causing the cutting force increase. As the cutting edge radius reaches a certain dimension, the whole edge was stripped off, a new edge formed with a smaller edge radius, and the cycle restarts. Under MQL cutting conditions, three cycles were observed before tool failure, while under dry machining conditions, the tool only experienced two cycles before tool breakage. The surface roughness at the bottom of the slots improved significantly with the application of MQL for all levels of the tool wear. The surface roughness did not increase drastically as the tool wear increased. It reached a plateau after the tool wear went into gradual wear state. Further experiments and theoretical analysis will be pursued in the future to gain a deeper understanding of tool wear mechanism in micro-milling.

Multi-Objective Optimization of Surface Roughness and Tool Wear in Turning Inconel 718: A Desirability Analysis, Genetic Algorithm and Firefly Algorithm 

2014 ◽  
Vol 592-594 ◽  
pp. 545-549 ◽  
Author(s):  
Shiv Kumar Sharma ◽  
M. Chandrasekaran ◽  
R. Thirumalai
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Tool Wear ◽  
Process Parameters ◽  
Inconel 718 ◽  
Firefly Algorithm ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Desirability Analysis

In this work, a parameter optimization in turning Inconel 718 for multiple performance characteristics has been attempted. The process parameters viz., cutting speed (v), feed (f) and depth of cut (d) is optimized that minimizes surface roughness (Ra) and tool wear (VB). Response surface methodology (RSM) employing CCD experimental design was used to develop predictive model for Ra and VB. The predictive capability of the model provides the average percentage error as 3.87 % and 5.10% for Ra and VB respectively with maximum percentage error limited to 14.67 %. The data are analysed to study the main effect and interaction effects of machining parameters through surface plot. Feed remains dominating factor. Process parameters are optimized for single and multiple objectives using three different techniques viz., statistical and mathematical approach based desirability analysis (DA) and soft computing based genetic algorithm (GA) and firefly algorithm (FA). The results are compared.

The Cutting Behavior of Diamond Coated Tool in Machining Al-20wt%Si Alloy

2010 ◽  
Vol 431-432 ◽  
pp. 365-368
Author(s):  
Wen Zhuang Lu ◽  
Dun Wen Zuo ◽  
B. Yang ◽  
Feng Xu ◽  
M. Wang
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Tool ◽  
Cvd Diamond ◽  
Cemented Carbide ◽  
Depth Of Cut ◽  
Coated Tool ◽  
Coated Cemented Carbide

The performance of CVD diamond coated cemented carbide cutting tool in comparison with K10 uncoated cemented carbide tool in the dry turning of Al-20wt%Si aluminum-silicon hypereutectic alloy was investigated. The obtained results showed a better cutting performance for CVD diamond coated tool in machining Al-20wt%Si, particularly in terms of cutting force, tool wear, surface roughness, when compared with K10. The cutting forces are lower with CVD diamond coated tool and the depth of cut promotes a great increment of the cutting force. The tool wear processes taking place in the tool tips in all cutting conditions. The tool life of CVD diamond coated tool is longer than that of the uncoated K10. The surface roughness Ra increases obviously with the increase of feed rate using a CVD diamond coated cutting tool. A higher feed rate produces surface rougher. The chip morphology in machining of Al-20wt%Si alloy by CVD diamond coated tool is continuous. The tests showed that the CVD diamond coated tool can be applied on the K10 tool at low feed rate to produce high quality surfaces.

Nature instigated Grey wolf algorithm for parametric optimization during machining (Milling) of polymer nanocomposites

2021 ◽  
pp. 089270572199320
Author(s):  
Prakhar Kumar Kharwar ◽  
Rajesh Kumar Verma
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Polymer Nanocomposites ◽  
Feed Rate ◽  
Fitness Function ◽  
Depth Of Cut ◽  
Multiwall Carbon Nanotube ◽  
Grey Wolf ◽  
Milling Characteristics ◽  
The Impact

The new era of engineering society focuses on the utilization of the potential advantage of carbon nanomaterials. The machinability facets of nanocarbon materials are passing through an initial stage. This article emphasizes the machinability evaluation and optimization of Milling performances, namely Surface roughness (Ra), Cutting force (Fc), and Material removal rate (MRR) using a recently developed Grey wolf optimization algorithm (GWOA). The Taguchi theory-based L27 orthogonal array (OA) was employed for the Machining (Milling) of polymer nanocomposites reinforced by Multiwall carbon nanotube (MWCNT). The second-order polynomial equation was intended for the analysis of the model. These mathematical models were used as a fitness function in the GWOA to predict machining performances. The ANOVA outcomes efficiently explore the impact of machine parameters on Milling characteristics. The optimal combination for lower surface roughness value is 1.5 MWCNT wt.%, 1500 rpm of spindle speed, 50 mm/min of feed rate, and 3 mm depth of cut. For lower cutting force, 1.0 wt.%, 1500 rpm, 90 mm/min feed rate and 1 mm depth of cut and the maximize MRR was acquired at 0.5 wt.%, 500 rpm, 150 mm/min feed rate and 3 mm depth of cut. The deviation of the predicted value from the experimental value of Ra, Fc, and MRR are found as 2.5, 6.5 and 5.9%, respectively. The convergence plot of all Milling characteristics suggests the application potential of the GWO algorithm for quality improvement in a manufacturing environment.

scholarly journals Fuzzy x- and s Control Charts: A Data-Adaptability and Human-Acceptance Approach

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Ming-Hung Shu ◽  
Dinh-Chien Dang ◽  
Thanh-Lam Nguyen ◽  
Bi-Min Hsu ◽  
Ngoc-Son Phan
Keyword(s):  
Surface Roughness ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Control Surface ◽  
Process Conditions ◽  
Fuzzy Data ◽  
Size Condition ◽  
Resolution Identity ◽  
And Control ◽  
Control Limits

For sequentially monitoring and controlling average and variability of an online manufacturing process, x¯ and s control charts are widely utilized tools, whose constructions require the data to be real (precise) numbers. However, many quality characteristics in practice, such as surface roughness of optical lenses, have been long recorded as fuzzy data, in which the traditional x¯ and s charts have manifested some inaccessibility. Therefore, for well accommodating this fuzzy-data domain, this paper integrates fuzzy set theories to establish the fuzzy charts under a general variable-sample-size condition. First, the resolution-identity principle is exerted to erect the sample-statistics’ and control-limits’ fuzzy numbers (SSFNs and CLFNs), where the sample fuzzy data are unified and aggregated through statistical and nonlinear-programming manipulations. Then, the fuzzy-number ranking approach based on left and right integral index is brought to differentiate magnitude of fuzzy numbers and compare SSFNs and CLFNs pairwise. Thirdly, the fuzzy-logic alike reasoning is enacted to categorize process conditions with intermittent classifications between in control and out of control. Finally, a realistic example to control surface roughness on the turning process in producing optical lenses is illustrated to demonstrate their data-adaptability and human-acceptance of those integrated methodologies under fuzzy-data environments.

OPTIMIZATION OF PROCESS PARAMETERS AFFECTING CUTTING FORCE, POWER CONSUMPTION AND SURFACE ROUGHNESS USING TAGUCHI-BASED GRAY RELATIONAL ANALYSIS IN TURNING AISI 1040 STEEL

2021 ◽  
Author(s):  
MAHIR AKGÜN
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Cutting Speed ◽  
System Optimization ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Significance Level ◽  
Relational Analysis

This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

Statistical Study and Multi-Response Optimization of Cutting Parameters for Dry Turning Stainless Steel AISI 316L Using Cermet Tool

2020 ◽  
Vol 36 ◽  
pp. 28-46
Author(s):  
Youssef Touggui ◽  
Salim Belhadi ◽  
Salah Eddine Mechraoui ◽  
Mohamed Athmane Yallese ◽  
Mustapha Temmar
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 316L ◽  
Cutting Power ◽  
Dry Turning ◽  
Optimization Of Cutting Parameters

Stainless steels have gained much attention to be an alternative solution for many manufacturing industries due to their high mechanical properties and corrosion resistance. However, owing to their high ductility, their low thermal conductivity and high tendency to work hardening, these materials are classed as materials difficult to machine. Therefore, the main aim of the study was to examine the effect of cutting parameters such as cutting speed, feed rate and depth of cut on the response parameters including surface roughness (Ra), tangential cutting force (Fz) and cutting power (Pc) during dry turning of AISI 316L using TiCN-TiN PVD cermet tool. As a methodology, the Taguchi L27 orthogonal array parameter design and response surface methodology (RSM)) have been used. Statistical analysis revealed feed rate affected for surface roughness (79.61%) and depth of cut impacted for tangential cutting force and cutting power (62.12% and 35.68%), respectively. According to optimization analysis based on desirability function (DF), cutting speed of 212.837 m/min, 0.08 mm/rev feed rate and 0.1 mm depth of cut were determined to acquire high machined part quality

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