Reliability of Monitoring Signals for Estimation of Surface Roughness in Metallic Turned Parts

2012 ◽  
Vol 498 ◽  
pp. 213-218
Author(s):  
P. Morala-Argüello ◽  
J. Barreiro ◽  
E. Alegre ◽  
M. García-Ordás ◽  
O. García-Olalla ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Signal Analysis ◽  
Cutting Parameters ◽  
Absolute Error ◽  
Machining Process ◽  
Monitoring Systems ◽  
Mean Square ◽  
Machining Processes ◽  
Current Trends ◽  
Turned Parts

Current trends in machining processes are focused in three goals: to increase the productivity and the reliability and to minimize costs. In this context, the development of signal monitoring systems is of vital importance for surface roughness inspection. One of the research lines associated to this context is oriented to predict surface roughness using indirect signal analysis, such as cutting forces or vibrations in the machining process. This paper analyzes the results obtained when comparing different nature signals combined with cutting parameters. The final goal is to quantify the deviations obtained with different monitoring signals for establishing the best ones to use as roughness evaluators. The best predictions were obtained when force and cutting conditions were combined together. The absolute error values remains always below 1.28 and 1.11 µm when using the median and root mean square (RMS) as descriptors, respectively.

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.

Optimisation of the Bauer Equation Using the Least Squares Method for Thermoplastics Turning

2018 ◽  
Vol 8 (1) ◽  
pp. 21-36
Author(s):  
János Farkas ◽  
Etele Csanády ◽  
Levente Csóka
Keyword(s):  
Surface Roughness ◽  
Least Squares Method ◽  
Composite Ceramic ◽  
Cutting Parameters ◽  
Machining Process ◽  
Tool Geometry ◽  
Machining Processes ◽  
Simple Formulation ◽  
Plastic Composite ◽  
Cutting Speeds

A number of equations are available for predicting the output of machining processes. These equations are most commonly used for the prediction of surface roughness after tooling. Surface roughness can be influenced by many factors, including cutting parameters, tool geometry and environmental factors such as the coolant used. It is difficult to create a universally applicable equation for all machining because of the variations in different materials' behaviours (e.g. metal, wood, plastic, composite, ceramic). There are also many differences between the various types of machining process such as the machining tools, rotational or translational movements, cutting speeds, cutting methods, etc. The large number of parameters required would make such an equation unusable, and difficult to apply quickly. The goal is thus to create a simple formulation with three or four inputs to predict the final surface roughness of the machined part within adequate tolerances. The two main equations used for this purpose are the Bauer and Brammertz formulas, both of which need to be optimised for a given material. In this paper, the turning of thermoplastics was investigated, with the aim of tuning the Bauer formula for use with thermoplastics. Eleven different plastics were used to develop a material-dependent surface roughness equation. Only new tooling inserts were used to eliminate the effects of tool wear.

scholarly journals Research and Modelling of Surface Roughness, Cutting Forces and I-kaz Coefficients for S42C in Turning using Response Surface Methodology

2019 ◽  
Vol 8 (12S2) ◽  
pp. 608-620
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Statistical Analysis ◽  
Response Surface ◽  
Cutting Parameters ◽  
Machining Process ◽  
Regression Equations ◽  
Turning Process ◽  
Machining Processes ◽  
Surface Method

This paper presents the optimization in machining processes on the cutting parameters for the S45C in turning process using the response surface method (RSM). The experimental work conducted investigates the influence of cutting parameters on statistical analysis of signals and surface quality. The paper also presents a statistical analysis of signal processing. The cutting force was measured during machining using the Kistler 9129AA dynamometer to monitor the force signals and the data was analyzed using the I-kazTM method of statistical analysis. This statistical analysis was used to assess the effect of force signals during the machining process. The RSM models for Ra and Rz, and Ideveloped with ANOVA and multiple regression equations. The models also were compared and validated with the predicted and measured of Ra and Rz values, and I-kaz coefficients. The optimal configuration of cutting parameters was observed at 200 m/min, 0.1 mm/rev and 0.521 mm with desirability of 95.9%. It is observed that the models developed are suggested to be utilized for predicting surface roughness values and I-kaz coefficients for the machining of S45C steel.

scholarly journals Temperature monitoring in the subsurface during single lip deep hole drilling

2020 ◽  
Vol 87 (12) ◽  
pp. 757-767
Author(s):  
Robert Wegert ◽  
Vinzenz Guski ◽  
Hans-Christian Möhring ◽  
Siegfried Schmauder
Keyword(s):  
Cutting Parameters ◽  
Drill Hole ◽  
Machining Process ◽  
Hole Drilling ◽  
Drilling Process ◽  
Deep Hole ◽  
Machining Processes ◽  
Deep Hole Drilling ◽  
Feed Force ◽  
Cutting Zone

AbstractThe surface quality and the subsurface properties such as hardness, residual stresses and grain size of a drill hole are dependent on the cutting parameters of the single lip deep hole drilling process and therefore on the thermomechanical as-is state in the cutting zone and in the contact zone between the guide pads and the drill hole surface. In this contribution, the main objectives are the in-process measurement of the thermal as-is state in the subsurface of a drilling hole by means of thermocouples as well as the feed force and drilling torque evaluation. FE simulation results to verify the investigations and to predict the thermomechanical conditions in the cutting zone are presented as well. The work is part of an interdisciplinary research project in the framework of the priority program “Surface Conditioning in Machining Processes” (SPP 2086) of the German Research Foundation (DFG).This contribution provides an overview of the effects of cutting parameters, cooling lubrication and including wear on the thermal conditions in the subsurface and mechanical loads during this machining process. At first, a test set up for the in-process temperature measurement will be presented with the execution as well as the analysis of the resulting temperature, feed force and drilling torque during drilling a 42CrMo4 steel. Furthermore, the results of process simulations and the validation of this applied FE approach with measured quantities are presented.

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.

Effect of Cutting Parameters on Surface Roughness in Turning of Bone

2013 ◽  
Vol 845 ◽  
pp. 708-712 ◽  
Author(s):  
P.Y.M. Wibowo Ndaruhadi ◽  
S. Sharif ◽  
M.Y. Noordin ◽  
Denni Kurniawan
Keyword(s):  
Surface Roughness ◽  
Bone Tissue ◽  
Influencing Factor ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Machining Process ◽  
Turning Operation ◽  
Cutting Direction

Surface roughness indicates the damage of the bone tissue due to bone machining process. Aiming at inducing the least damage, this study evaluates the effect of some cutting conditions to the surface roughness of machined bone. In the turning operation performed, the variables are cutting speed (26 and 45 m/min), feed (0.05 and 0.09 mm/rev), tool type (coated and uncoated), and cutting direction (longitudinal and transversal). It was found that feed did not significantly influence surface roughness. Among the influencing factor, the rank is tool type, cutting speed, and cutting direction.

OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Prof. Hemant k. Baitule ◽  
Satish Rahangdale ◽  
Vaibhav Kamane ◽  
Saurabh Yende
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Multiple Time ◽  
Turning Process ◽  
Workpiece Material ◽  
Hot Turning

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

A novel approach to machining process fault detection using unsupervised learning

2020 ◽  
pp. 095440542093755
Author(s):  
Thomas McLeay ◽  
Michael S Turner ◽  
Keith Worden
Keyword(s):  
Fault Detection ◽  
Unsupervised Learning ◽  
Tool Path ◽  
Cutting Tool ◽  
Novelty Detection ◽  
Cutting Parameters ◽  
Machining Process ◽  
Machining Processes ◽  
Workpiece Material ◽  
Data Set

The most common machining processes of turning, drilling, milling and grinding concern the removal of material from a workpiece using a cutting tool. The performance of machining processes depends on a number of key method parameters, including cutting tool, workpiece material, machine configuration, fixturing, cutting parameters and tool path trajectory. The large number of possible configurations can make it difficult to implement fault detection systems without having to train the system to a particular method or fault type. The research of this article applies a novel method to detect the changing state of a process over time in order to detect faulty machining conditions such as worn tools and cutting depth changes. Unlike studies in the previous literature in this domain, an unsupervised learning method is used, so that the method can be applied in production to unfamiliar processes or fault conditions. In the case presented, novelty detection is applied to a multivariate sensor feature data set obtained from a milling process. Sensor modalities include acoustic emission, vibration and spindle power and time and frequency domain features are employed. The Mahalanobis squared-distance is used to measure discordancy of each new data point, and values that exceed a principled novelty threshold are categorised as fault conditions.

Comparative Analysis on Wiper and Standard Tools in Dry Finish Turning of Martensitic Stainless Steel AISI 420

2013 ◽  
Vol 845 ◽  
pp. 765-769 ◽  
Author(s):  
Guilherme Cortelini Rosa ◽  
André J. Souza ◽  
Flávio J. Lorini
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Residual Stresses ◽  
Martensitic Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Finish Turning ◽  
Aisi 420 ◽  
Steel Aisi ◽  
Stainless Steel Aisi

Machining performance consists to associate the optimal process and cutting parameters and maximum material removal rate with the most appropriate tool while controlling the machined surface state. This work verifies the influence of standard and wiper cutting tools on generated surface roughness and residual stress in dry finish turning operation of the martensitic stainless steel AISI 420 in a comparative way. Tests are conducted for different combinations of tool nose geometry, feed rate and depth of cut being analyzed through the Design of Experiments regarding to surface roughness parametersRaandRt. Moreover, the formation of residual stresses in the material (using the technique of X-Ray Diffraction) was evaluated after the machining process for these two cutting geometries and thereafter the result was compared between them. An ANOVA is performed to clarify the influence of cutting parameters on generated surface roughness, which outputs inform that cutting tool geometry is the most influent onRaandRt. It is concluded that analyzed wiper inserts present low performance for low feed rates. Regarding the analysis of the residual stresses it can be stated that for standard and wiper tools the values collected show that for finish turning the compression stresses were found. It can be observed that the greatest amount of compressive stress has been found for the standard tool.

scholarly journals Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

2019 ◽  
Vol 32 (1) ◽  
Author(s):  
Zhipeng Jiang ◽  
Dong Gao ◽  
Yong Lu ◽  
Xianli Liu
Keyword(s):  
Surface Roughness ◽  
Carbon Emissions ◽  
Processing Time ◽  
Cutting Parameters ◽  
Machining Process ◽  
Trade Off ◽  
Pareto Fronts ◽  
Optimization Of Cutting Parameters ◽  
1045 Steel ◽  
Optimal Cutting Parameters

AbstractAs the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design (CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135di lathe cutting of AISI 1045 steel, and NSGA-II was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.

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