scholarly journals Investigation of Functional Dependency between the Characteristics of the Machining Process and Flatness Error Measured on a CMM

2021 ◽  
Vol 21 (6) ◽  
pp. 158-167
Author(s):  
Branko Štrbac ◽  
Dragan Rodić ◽  
Milan Delić ◽  
Borislav Savković ◽  
Miodrag Hadžistević
Keyword(s):  
Surface Roughness ◽  
Regression Models ◽  
Machining Process ◽  
Error Assessment ◽  
Statistical Regression ◽  
Form Error ◽  
Neuro Fuzzy ◽  
Input Variables ◽  
Flatness Error ◽  
The Ideal

Abstract Numerous studies have shown that the choice of measurement strategy (number and position of measurement points) when measuring form error on a coordinate-measuring machine (CMM) depends on the characteristics of the machining process which was used to machine the examined surface. The accuracy of form error assessment is the primary goal of verification procedures and accuracy is considered perfect only in the case of the ideal verification operator. Since the ideal verification operator in the “point-by-point” measuring mode is almost never used in practice, the aim of this study was to examine a relationship which had not been examined in earlier studies, namely how the machining process, surface roughness and a reduced number of points in the measurement strategy affect the accuracy of flatness error assessment. The research included four most common cutting processes applied to flat surfaces divided into nine different classes of roughness. In order to determine functional dependency between the observed input variables and the output, statistical regression models and neuro-fuzzy logic (artificial intelligence tool) were used. The analyses confirmed the significance of all three input parameters, with surface roughness being the most significant one. Both the statistical regression models and neuro-fuzzy models proved to be adequate, matching the experimental results. The use of these models makes it possible to determine flatness error measured on a CMM if input variables considered in the paper are known.

Ultrasonic Vibration Assisted Grinding of Sintered Dental Zirconia Ceramics: An Experimental Study on Surface Roughness

2014 ◽  
Vol 1017 ◽  
pp. 800-805 ◽  
Author(s):  
Song Dong ◽  
Kan Zheng ◽  
Xing Zhi Xiao
Keyword(s):  
Surface Roughness ◽  
Ultrasonic Vibration ◽  
Spindle Speed ◽  
Machining Process ◽  
Zirconia Ceramics ◽  
Cutting Depth ◽  
Hard And Brittle Materials ◽  
Input Variables ◽  
Dental Zirconia Ceramics ◽  
Ultrasonic Vibration Assisted Grinding

Dental zirconia ceramics have been widely used in dental restorations due to their superior aesthetical and mechanical properties. Ultrasonic vibration assisted grinding (UVAG), as a novel effective machining process for hard and brittle materials, is introduced into directly machining sintered dental zirconia ceramics. This study is dedicated to investigating the influence of input variables (spindle speed, feedrate and cutting depth) on surface roughness during UVAG of sintered dental zirconia ceramics. The experiment is conducted through single-factor method, and the experimental results are statistically analyzed by One-Way ANOVA. Besides, the influence tendency of input variables on surface roughness is also obtained. The results indicate that the influence of spindle speed on surface roughness is highly significant. The value of surface roughness rises with the increase of spindle speed, feedrate, and cutting depth. Therefore, a better surface quality will be achieved with the combination of lower spindle speed, cutting depth and feedrate.

Surface Roughness Prediction in High Speed End Milling Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 1115 ◽  
pp. 122-125
Author(s):  
Muataz Hazza F. Al Hazza ◽  
Amin M.F. Seder ◽  
Erry Y.T. Adesta ◽  
Muhammad Taufik ◽  
Abdul Hadi bin Idris
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
High Speed ◽  
Fuzzy Inference ◽  
End Milling ◽  
Machining Process ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Roughness Prediction ◽  
Coated Carbide

One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the average surface roughness (Ra) in high speed end milling. An experimental work was conducted to measure the surface roughness. A set of experimental runs based on box behnken design was conducted to machine carbon steel using coated carbide inserts. Moreover, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used as one of the unconventional methods to develop a model that can predict the surface roughness. The adaptive-network-based fuzzy inference system (ANFIS) was found to be capable of high accuracy predictions for surface roughness within the range of the research boundaries.

Experimental Research on Machining Process for Convex Curved Rake Face of Pinion Cutter

2011 ◽  
Vol 80-81 ◽  
pp. 495-500 ◽  
Author(s):  
Xue Guang Li ◽  
Guo Quan Shi ◽  
Shu Ren Zhang ◽  
Lin Sen Song
Keyword(s):  
Surface Roughness ◽  
Experimental Research ◽  
Tool Path ◽  
Cnc Machining ◽  
Machining Process ◽  
Grinding Wheel ◽  
Laser Sensor ◽  
Rake Face ◽  
Initial Location ◽  
The Ideal

According to the new profiling characteristic of carbide pinion cutter with convex curved rake face, the machining process of convex curved rake face is designed, the special grinding equipment is designed and developed. Tool presetting and detecting equipment is developed by using laser sensor. It can be used to locate the initial location of grinding wheel precisely when convex curved rake face is machining. The tool path for CNC machining is solved. The grinding experiment is done for convex curved rake face of pinion cutter with 21teeth and 5modulus , different process parameters are established. The ideal surface roughness is obtained. The experiment result shows that the grinding equipment and tool presetting equipment are proper for machining requirements of convex curved rake face of pinion cutter.

Machining process influence on the chip form and surface roughness by neuro-fuzzy technique

2017 ◽  
Vol 123 (4) ◽  
Author(s):  
Obrad Anicic ◽  
Srđan Jović ◽  
Danilo Aksić ◽  
Aleksandar Skulić ◽  
Bogdan Nedić
Keyword(s):  
Surface Roughness ◽  
Machining Process ◽  
Neuro Fuzzy

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

Surface Roughness and Cutting Force Components Evaluation in Hard Turning by Differently Shaped Cutting Tools

2016 ◽  
Vol 862 ◽  
pp. 26-32 ◽  
Author(s):  
Michaela Samardžiová
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Hard Turning ◽  
Cutting Tool ◽  
Single Point ◽  
Machining Process ◽  
Tool Geometry ◽  
Machined Surface ◽  
Cutting Force Components ◽  
Force Components

There is a difference in machining by the cutting tool with defined geometry and undefined geometry. That is one of the reasons of implementation of hard turning into the machining process. In current manufacturing processes is hard turning many times used as a fine finish operation. It has many advantages – machining by single point cutting tool, high productivity, flexibility, ability to produce parts with complex shapes at one clamping. Very important is to solve machined surface quality. There is a possibility to use wiper geometry in hard turning process to achieve 3 – 4 times lower surface roughness values. Cutting parameters influence cutting process as well as cutting tool geometry. It is necessary to take into consideration cutting force components as well. Issue of the use of wiper geometry has been still insufficiently researched.

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

2011 ◽  
Vol 383-390 ◽  
pp. 1062-1070
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jalil
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Machining Process ◽  
Data Selection ◽  
Adaptive Network ◽  
Inference System ◽  
Single Input ◽  
Machinability Data

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

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