Multi-Objective Optimization for the Micro-Milling Process With Adaptive Data Modeling

2011 ◽  
Author(s):  
Xinyu Liu ◽  
Weihang Zhu ◽  
Victor Zaloom
Keyword(s):  
Surrogate Model ◽  
Data Modeling ◽  
Milling Process ◽  
Machining Process ◽  
Micro Milling ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Study ◽  
Machining Parameter

This paper presents a multi-objective optimization study for the micro-milling process with adaptive data modeling based on the process simulation. A micro-milling machining process model was developed and verified through our previous study. Based on the model, a set of simulation data was generated from a factorial design. The data was converted into a surrogate model with adaptive data modeling method. The model has three input variables: axial depth of cut, feed rate and spindle speed. It has two conflictive objectives: minimization of surface location error (which affects surface accuracy) and minimization of total tooling cost. The surrogate model is used in a multi-objective optimization study to obtain the Pareto optimal sets of machining parameters. The visual display of the non-dominated solution frontier allows an engineer to select a preferred machining parameter in order to get a lowest cost solution given the requirement from tolerance and accuracy. The contribution of this study is to provide a streamlined methodology to identify the preferred best machining parameters for micro-milling.

scholarly journals OPTIMASI PARAMETER PERMESINAN TERHADAP WAKTU PROSES PADA PEMROGRAMAN CNC MILLING DENGAN BERBASIS CAD/CAM

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
I G.N.K. Yudhyadi ◽  
Tri Rachmanto ◽  
Adnan Dedy Ramadan
Keyword(s):  
Cutting Speed ◽  
Operation Time ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Milling Machine ◽  
Cnc Milling ◽  
Cnc Milling Machine ◽  
Machining Parameter

Milling process is one of many machining processes for manufacturing component. The length of time in the process of milling machining is influenced by selection and design of machining parameters including cutting speed, feedrate and depth of cut. The purpose of this study to know the influence of cutting speed, feedrate and depth of cut as independent variables versus operation time at CNC milling process as dependent variables. Each independent variable consists of three level of factors; low, medium and high.Time machining process is measured from operation time simulation program, feed cut length and rapid traverse length. The results of statistically from software simulation MasterCam X Milling, then do comparison to CNC Milling machine.  The data from experiments was statistical analyzed by Anova and Regression methods by software minitab 16.Results show that the greater feedrate and depth of cut shorten the operation time of machinery, whereas cutting speed is not significant influence. Depth of cut has the most highly contribution with the value of 49.56%, followed by feedrate 43% and cutting speed 0.92%. Optimal time of machining process total is 71.92 minutes, with machining parameter on the condition cutting speed is 75360 mm/minutes, feedrate is 800 mm/minutes and depth of cut = 1 mm. Results of comparison time machining process in software Mastercam X milling with CNC Milling machine indicates there is difference not significant with the value of 0,35%.

Cutting forces analysis of micro-milling process on Inconel 718 – a finite element approach

2020 ◽  
Vol 11 (02) ◽  
pp. 2050011
Author(s):  
Padmaja Tripathy ◽  
Kalipada Maity
Keyword(s):  
Finite Element ◽  
Cutting Forces ◽  
Inconel 718 ◽  
Cutting Speed ◽  
Fem Analysis ◽  
Milling Process ◽  
Machining Process ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents a modeling and simulation of micro-milling process with finite element modeling (FEM) analysis to predict cutting forces. The micro-milling of Inconel 718 is conducted using high-speed steel (HSS) micro-end mill cutter of 1mm diameter. The machining parameters considered for simulation are feed rate, cutting speed and depth of cut which are varied at three levels. The FEM analysis of machining process is divided into three parts, i.e., pre-processer, simulation and post-processor. In pre-processor, the input data are provided for simulation. The machining process is further simulated with the pre-processor data. For data extraction and viewing the simulated results, post-processor is used. A set of experiments are conducted for validation of simulated process. The simulated and experimental results are compared and the results are found to be having a good agreement.

Machining Parameters Mapping’s of Inconel 718 and Aluminum Alloy 1100 in Micro-Milling Process

2020 ◽  
Vol 846 ◽  
pp. 99-104
Author(s):  
Gandjar Kiswanto ◽  
Maulana Azmi ◽  
Adrian Mandala ◽  
Dede Lia Zariatin ◽  
Tae Jo Ko
Keyword(s):  
Aluminum Alloy ◽  
Inconel 718 ◽  
Cutting Tool ◽  
Milling Process ◽  
Machining Process ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Manufacturing Cost ◽  
Machining Parameters ◽  
Workpiece Material

The development of micro-products in industry, like aviation, medical equipment, electronics, etc, has been increasing lately. The need for scaling down of product has been increasing to make the product simpler and complex. Micro-milling has capabilities in producing complex parts. In this study, mapping and comparing the result of the machining process of Inconel 718 and Aluminum Alloy 1100 was employed. In this experiment, Inconel 718 was used as workpiece material and the result of Aluminum Alloy taken from recent studies. Then, A cutting tool with a diameter 1 mm carbide coating TiAlN was used in this experiment. The machining process was performed with three varieties of spindle speed and feed rate with a constant depth of cut. After the machining is done, the mapping of the result surface roughness of Inconel 718 and AA1100 performed. It was found that Inconel 718 has poor machinability compared with AA 1100. Inconel 718 also has a high manufacturing cost compared to AA 1100 because the cutting tool was easy to wear.

Multi-Objective Optimization of CNC Turning Machining Parameters

2014 ◽  
Vol 1016 ◽  
pp. 172-176 ◽  
Author(s):  
Sharad Kumar Pradhan ◽  
Surendra Kumar Saini
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Cnc Turning ◽  
Multi Objective ◽  
Machining Parameter

An experimental investigation into CNC turning operation on Brass C36000 alloy as work piece material which is widely used for various industrial applications is performed. Multi objective optimization is carried out to find out the influencing machining parameters among spindle speed (rpm), feed (mm per revolution) and depth of cut (mm) for CNC turning of Brass C36000 alloy with surface finish and Material Removal Rate as performance parameters using Taguchi method. Taguchi orthogonal array [L27(33)] is used for the experimental design. All experiments are conducted using EMCO Concept Turn 250 machine tool with carbide insert cutting tool. The optimization result shows that feed is the most significant turning machining parameter for surface roughness while depth of cut has high influence on material removal rate followed by spindle speed during CNC turning of Brass C36000 alloy. Above results is further validated using ANOVA approach.

Investigation of Micro-Milling Force Based on Miniature Machine Tool

2010 ◽  
Vol 29-32 ◽  
pp. 1074-1078 ◽  
Author(s):  
Zi Yang Cao ◽  
Hua Li
Keyword(s):  
Machine Tool ◽  
Cutting Force ◽  
Milling Process ◽  
Machining Process ◽  
Micro Milling ◽  
Machining Parameters ◽  
Milling Force ◽  
Processing Efficiency ◽  
Side Milling ◽  
The Impact

Miniaturized components are increasingly in demand for various industries. Micro milling operations can fabricate miniaturized components with high relative accuracy. In micro milling process, the analysis of cutting force plays an important role in characterizing the machining process, as the tool wear and surface texture depending on the cutting force. In this paper, the orthogonal experiments under slot-milling and side-milling with typical micro three dimensional parts are done by the developed three-axis micro milling machine tool, and the micro milling force is measured and analyzed. In order to improve the processing efficiency and processing accuracy of micro milling process, the impact law of the spindle speed, axial cutting depth, feed per tooth and other parameters on cutting force is deeply studied, and the machining parameters is also optimized.

scholarly journals Comparison of NSGA-II, MOALO and MODA for Multi-Objective Optimization of Micro-Machining Processes

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5109
Author(s):  
Milan Joshi ◽  
Ranjan Kumar Ghadai ◽  
S. Madhu ◽  
Kanak Kalita ◽  
Xiao-Zhi Gao
Keyword(s):  
Manufacturing Industry ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Machining Process ◽  
Micro Milling ◽  
Micro Machining ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Micro Turning

The popularity of micro-machining is rapidly increasing due to the growing demands for miniature products. Among different micro-machining approaches, micro-turning and micro-milling are widely used in the manufacturing industry. The various cutting parameters of micro-turning and micro-milling has a significant effect on the machining performance. Thus, it is essential that the cutting parameters are optimized to obtain the most from the machining process. However, it is often seen that many machining objectives have conflicting parameter settings. For example, generally, a high material removal rate (MRR) is accompanied by high surface roughness (SR). In this paper, metaheuristic multi-objective optimization algorithms are utilized to generate Pareto optimal solutions for micro-turning and micro-milling applications. A comparative study is carried out to assess the performance of non-dominated sorting genetic algorithm II (NSGA-II), multi-objective ant lion optimization (MOALO) and multi-objective dragonfly optimization (MODA) in micro-machining applications. The complex proportional assessment (COPRAS) method is used to compare the NSGA-II, MOALO and MODA generated Pareto solutions.

scholarly journals Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition

2020 ◽  
pp. 002029402091945 ◽  
Author(s):  
Ngoc-Chien Vu ◽  
Xuan-Phuong Dang ◽  
Shyh-Chour Huang
Keyword(s):  
Milling Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Hard Milling ◽  
Worst Case ◽  
Multi Objective ◽  
Cutting Energy ◽  
Aisi H13 ◽  
Machining Productivity

This paper presents the multi-objective optimization of the hard milling process of AISI H13 steel under minimum quality lubricant with graphite nanoparticle. The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency.

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