scholarly journals Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Materials ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 4338 ◽  
Author(s):  
Małgorzata Kowalczyk ◽  
Krzysztof Tomczyk
Keyword(s):  
Surface Roughness ◽  
Monte Carlo ◽  
Point Of View ◽  
Machining Process ◽  
Machining Parameters ◽  
Optimal Selection ◽  
Niti Alloys ◽  
The Monte Carlo Method ◽  
Selection Of

The paper presents a procedure for the determination of uncertainties in the modeling of surface roughness in the turning of NiTi alloys. The presented procedure is applicable both to the analysis of the measurement values of the two main roughness factors, as well as to research related to the prediction and optimization of the machining process. Type A and B, total, and expanded uncertainties were considered herein, and the obtained uncertainty values were assessed. A procedure for optimizing machining by applying the Monte Carlo (MC) method is also presented. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the machining process. The considered procedure for determining and assessing uncertainty can be useful for the optimal selection of both machining parameters and measuring tools.

scholarly journals The Use of Statistical Functions for the Selection of Laser Texturing Parameters

2020 ◽  
Vol 10 (1) ◽  
pp. 454-461
Author(s):  
Piotr Sęk
Keyword(s):  
Surface Texturing ◽  
Point Of View ◽  
Laser Surface Texturing ◽  
Machining Parameters ◽  
Textured Surfaces ◽  
Surface Layers ◽  
Adhesion Properties ◽  
Machine Elements ◽  
Selection Of ◽  
Statistical Functions

AbstractLaser surface texturing is currently the most developed technique for producing fully reproducible microcavities on the surfaces of machine elements. From the point of view of texture technology, an important aspect is the proper selection of process parameters to obtain texture elements with desirable and repetitive geometries and physicochemical properties. Surface texturing improves mottling and fretting resistance and is also used wherever the adhesion properties of surface layers (printing techniques, bonding materials, biological and chemical activity, coatings, etc.) are important. The article shows the possibility of applying statistical functions to the selection of appropriate machining parameters to obtain microgeometry useful in the application of textured surfaces [1].

scholarly journals Study of wire-electrical discharge machining parameters of titanium alloy by using taguchi method

2018 ◽  
Vol 7 (2.8) ◽  
pp. 10
Author(s):  
A VS Ram Prasad ◽  
Koona Ramji ◽  
B Raghu Kumar
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Process Parameters ◽  
Optimization Technique ◽  
Machining Process ◽  
Machining Parameters ◽  
Wire Edm ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Edm Process

Machining of Titanium alloys is difficult due to their chemical and physical properties namely excellent strength, chemical reactivity and low thermal conductivity. Traditional machining of such materials leads to formation of continuous chips and tool bits are subjected to chatter which leads to formation of poor surface on machined surface. In this study, Wire-EDM one of the most popular unconventional machining process which was used to machine such difficult-to-cut materials. Effect of Wire-EDM process parameters namely peak current, pulse-on- time, pulse-off-time, servo voltage on MRRand SR was investigated by Taguchi method. 0.25 mm brass wire was used in this process as electrode material. A surface roughness tester (Surftest 301) was used to measure surface roughness value of the machined work surface. A multi-response optimization technique was then utilized to optimize Wire-EDM process parameters for achieving maximum MRR and minimum SR simultaneously.

Application of Taguchi Technique to Surface-Grinding of Mold Steel

2016 ◽  
Vol 689 ◽  
pp. 7-11 ◽  
Author(s):  
Y. Şahin ◽  
Senai Yalcinkaya
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Quality Characteristics ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Grinding Process ◽  
Machining Parameters ◽  
Taguchi Technique ◽  
Fluid Environment ◽  
Selection Of

The selection of optimum machining parameters plays a significant role for the quality characteristics of products and its costs for grinding. This study describes the optimization of the grinding process for an optimal parametric combination to yield a surface roughness using the Taguchi method. An orthogonal array and analysis of variance are employed to investigate the effects of cutting environment (A), depth of cut (B) and feed rate (C) on the surface roughness characteristics of mold steels. Confirmation experiments were conducted to verify the optimal testing parameters. The experimental results indicated that the surface finish decreased with cutting-fluid and depth of cut, but decreased with increasing feed rate. It is revealed that the cutting fluid environment had highest physical as well as statistical influence on the surface roughness (71.38%), followed by depth of cut (25.54%), but the least effect was exhibited by feed rate (1.62%).

Monte Carlo method for the reduction of measurement errors in the material parameter estimation with cavities

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronny Peter ◽  
Luca Bifano ◽  
Gerhard Fischerauer
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Errors ◽  
Input Data ◽  
Material Parameter ◽  
Cavity Resonator ◽  
Calculation Algorithm ◽  
The Monte Carlo Method ◽  
Electromagnetic Resonances

Abstract The quantitative determination of material parameter distributions in resonant cavities is a relatively new method for the real-time monitoring of chemical processes. For this purpose, electromagnetic resonances of the cavity resonator are used as input data for the reverse calculation (inversion). However, the reverse calculation algorithm is sensitive to disturbances of the input data, which produces measurement errors and tends to diverge, which leads to no measurement result at all. In this work a correction algorithm based on the Monte Carlo method is presented which ensures a convergent behavior of the reverse calculation algorithm.

scholarly journals Findings of performance evaluation of EDM for different materials of electrodes and work pieces- a review

2018 ◽  
Vol 7 (4.5) ◽  
pp. 542
Author(s):  
Harshalkumar R. Mundane ◽  
Dr. A. V. Kale ◽  
Dr. J. P. Giri
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Performance Measure ◽  
Machining Process ◽  
Machining Parameters ◽  
Spark Erosion ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Time Pulse

EDM (Spark erosion) is non-conventional machining process which uses as removing unwanted material by electrical spark erosion. EDM Machining parameters affecting to the performance and the industries goal is to produce high quality of product with less time consuming and cost. To achieve these goals, optimizing the machining parameters such as pulse on time, pulse off time, cutting speed, depth of cut, duty cycle, arc gap, voltage etc. The performance measure of EDM is calculated on the basis of Material Remove Rate(MRR), Tool Wear Rate(TWR), and Surface Roughness(SR).The main objective of present work is to investigate of the influence of input EDM (Electro Discharge Machining) parameters on machining characteristics like surface roughness and the effects of various EDM process parameters such as pulse on time, pulse off time, servo voltage, peak current, dielectric flow rate, on different process response parameters such as material removal rate (MRR), surface roughness (Ra), Kerf (width of Cut), tool wear ratio(TWR)and surface integrity factors. In this paper few selected research paper related to Die-sinker EDM with effect of MRR, TWR, surface roughness (SR) and work piece material have been discussed.   

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

scholarly journals Intelligent Selection of Machining Parameters in Multipass Turnings Using Firefly Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Abderrahim Belloufi ◽  
Mekki Assas ◽  
Imane Rezgui
Keyword(s):  
Production Cost ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Cutting Parameters ◽  
Machining Parameters ◽  
Metal Parts ◽  
Multipass Turning ◽  
Optimal Cutting Parameters ◽  
Selection Of

Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. In this paper, a new optimization technique, firefly algorithm, is used for determining the machining parameters in a multipass turning operation model. The objective considered is minimization of production cost under a set of machining constraints. The optimization is carried out using firefly algorithm. An application example is presented and solved to illustrate the effectiveness of the presented algorithm.

The Effect of Machining Toolpath on Surface Roughness and Dimensional Accuracy for High-Speed Micro Milling

2017 ◽  
Vol 261 ◽  
pp. 69-76
Author(s):  
Amin Dadgari ◽  
De Hong Huo ◽  
David Swailes
Keyword(s):  
Surface Roughness ◽  
Process Planning ◽  
Tool Path ◽  
Dimensional Accuracy ◽  
Machining Process ◽  
Micro Milling ◽  
Geometric Features ◽  
Machining Parameters ◽  
Machining Performance ◽  
Planning Stage

This paper investigates different machining toolpath strategies on machining efficiency and accuracy in the micro milling of linear and circular micro geometric features. Although micro milling includes many characteristics of the conventional machining process, detrimental size effect in downscaling of the process can lead to excessive tool wear and machining instability, which would, in turn, affects the geometrical accuracy and surface roughness. Most of the research in micro milling reported in literature focused on optimising specific machining parameters, such as feed rate and depth of cut, to achieve lower cutting force, better surface roughness, and higher material removal rate. However, there was little attention given to the suitability and effect of machining tool path strategies. In this research, a tool path optimisation method with respect to surface roughness and dimensional accuracy is proposed and tested experimentally. Various toolpath strategies, including lace(0°), lace(45°), lace(90°), concentric and waveform in producing linear and circular micro geometric features were compared and analysed. Experimental results show that the most common used strategies lace(0°) and concentric reported in the literature have provided the least satisfactory machining performance, while waveform toolpath provides the best balance of machining performance for both linear and circular geometries. Hence, at process planning stage it is critical to assign a suitable machining toolpath strategy to geometries accordingly. The paper concludes that an optimal choice of machining strategies in process planning is as important as balancing machining parameters to achieve desired machining performance.

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