Engineered-Based Machining Parameters Analysis for Aircraft Structural Parts

2014 ◽  
Vol 721 ◽  
pp. 109-112
Author(s):  
Fang Zhu ◽  
Xiong Fei Huang ◽  
Li Jun Meng
Keyword(s):  
Computer Experiment ◽  
Machining Process ◽  
Aviation Industry ◽  
Machining Parameters ◽  
Structural Part ◽  
Parameter Evaluation ◽  
Aircraft Structural Part ◽  
Parameters Analysis ◽  
Structural Parts ◽  
Machining Parameter

Modern aircraft structures need to be further integrated and parts to be enlarged, the structural parts which require high accuracy of size, shape, and good deformed surface finish, so digital machining process has become key technology in aviation industry. Machining parameters selection is an important phase of machining process, it has very important influence on the dimensional quality of an aircraft structural part. This paper develops a methodology to get an optimal machining parameters in an aircraft structural part machining process, which can satisfies the quality specifications and minimizes the expected value of the sum of machining costs at the same time. An engineered-based model is adopted to describe the machining process of aircraft structural part firstly and then a quantitative machining parameter evaluation methods driven by cost constraint is proposed, an optimal machining parameter is obtained through a computer experiment model which can explore a large number of machining process parameter alternatives. At last, we illustrate the validity and the significance of the engineered-based machining parameters analysis method for aircraft structural parts by a case study. The results have shown that the proposed method could significantly increase the efficiency of aircraft structural parts machining process.

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%.

Research on Anti-Burnt Method during Machining Complex Titanium Alloy Aircraft Structural Parts

2013 ◽  
Vol 589-590 ◽  
pp. 299-303
Author(s):  
Shao Chun Sui ◽  
Cheng Li Du ◽  
Li Min Tang
Keyword(s):  
Titanium Alloy ◽  
Machining Process ◽  
Machining Parameters ◽  
Structural Parts

With the development of modern aircraft, complex titanium alloy aircraft structural parts are widely used. As a kind of difficult machining material, it’s easy to burn the tools and the parts during machining the titanium alloy structural parts because of the gathering heat. The article studies the technology to prevent burning the titanium structural parts during the machining process. The burnt problem can be effectively solved by using proper tools, proper machining parameters and fully cooling environment.

Kerf Width Optimization in Wire-Cut Electrical Discharge Machine by using Taguchi Method

2012 ◽  
Vol 59 (2) ◽  
Author(s):  
Rizauddin Ramli ◽  
Roazam Ahmad ◽  
Jaharah Abd Ghani
Keyword(s):  
Taguchi Method ◽  
Electrical Discharge ◽  
Electrical Discharge Machine ◽  
Kerf Width ◽  
Machining Process ◽  
Machining Parameters ◽  
Diverse Range ◽  
Machine Manufacturer ◽  
Discharge Machine ◽  
Machining Parameter

Today, in most of wire cut electrical discharge machine (WEDM), the suitable machining parameters are relied based on the data from machine manufacturer and the experience of operators. The situation became more difficult when involving numerous and diverse range of parameters and the lack of experience. Normally, the parameter tables given by the machine manufacturer is only for basic machining operation but not for optimum machining condition. The optimal machining parameters only can be achieved by determining significant parameters that are affecting the machining performance. In this paper, a Taguchi Method quality design and analysis of variance (ANOVA) is used to determine the optimal kerf width for material tool steel grade DF-2 for machining process with WEDM. The optimum machining parameter is obtained by using a analysis of signal-to-noise (S/N) ratio. After several experiments, it can be ascertained the significant parameter contributed to the kerf witdh are the open circuit voltage(47%), pulse duration(20%) and wire speed(15%). The average of kerf width from the three experiments was 0.255mm and from that, the error margin is only 1.53%, which satisfied our data .

Experimental investigation and optimization of machining performance characteristics during WEDM of inconel 718: On evaluation of wire electrodes and advanced parameter techniques

2021 ◽  
pp. 095440622110231
Author(s):  
Anshuman Kumar ◽  
Chandramani Upadhyay
Keyword(s):  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Performance Characteristics ◽  
Machining Process ◽  
Computational Time ◽  
Machining Parameters ◽  
Optimization Strategy ◽  
Machining Performance ◽  
Teaching Learning ◽  
Machining Parameter

Wire Electrical-Discharge-Machining (WEDM) is a well-known unconventional machining process to produce intricate shapes. However, obtaining a satisfactory WEDM cutting performance is indeed a challenging task during precision cutting. Hence, this investigation aims to attempt a favorable machining parameter setting in order to corner-cutting during WEDM for In-718. Here, machining performance characteristics have been considered based on corner deviation (CD) along with Material Removal Rate (MRR) and surface roughness (SR). Taguchi’s experiment design technique (L16) has been considered to run the experiments. The controllable process parameters are considered as Spark-on-time (Son), flushing-pressure (Fp), wire-tension (Tw), and discharge-current (Id). The aforesaid machining performance characteristics have been achieved through the two most popular wire electrodes, i.e., Zinc-coated brass electrode (Zn-BE) and Brass Wire Electrode (BWE), and compared the results. The comparison of performances by the wire electrodes on CD, MRR, and SR varied from 0.0286 mm to 0.0844 mm, 0.0045 g/min to 0.0214 g/min and 3.12 µm to 4.80 µm for BWE and 0.0218 mm to 0.0783 mm, 0.0090 g/min to 0.0342 g/min and 2.58 µm to 4.40 µm for Zn-BE respectively. However, machining with Zn-WE yields reduced CD, SR, and increased MRR value and shows less defect on the WEDMed surfaces than its counterpart. The present study developed the mathematical model based on non-linear regression for correlating the machining parameters with the machining responses. The next step of this study is that a unique optimization strategy, namely grey relation analysis (GRA) integrated with Teaching Learning-Based Optimization (TLBO), has been implemented for achieving optimal parametric setting. The satisfactory machining setting obtained from GRA-TLBO has been compared with GRA-JAYA and GRA-genetic algorithm (GA). The proposed methodology appears more fruitful in terms of computational time and effort.

scholarly journals A Data-driven Minimum Stiffness Prediction Method for Machining Regions of Aircraft Structural Parts

2021 ◽  
Author(s):  
Jiarui Chen ◽  
Yingguang Li ◽  
Xu Liu ◽  
Tianchi Deng
Keyword(s):  
Prediction Method ◽  
Cutting Parameters ◽  
Data Driven ◽  
Calculation Methods ◽  
Structural Part ◽  
Average Percentage ◽  
Thin Walled ◽  
Structural Parts ◽  
Local Stiffness ◽  
Machining Parameter

Abstract Large thin-walled structural parts have been widely used in aircrafts for the purpose of weight reduction. These parts usually contain various thin-walled complex structures with weak local stiffness, which are easy to deform during machining if improper cutting parameters are selected. Thus, local stiffness has to be seriously considered during the machining parameter planning. Existing stiffness calculation methods mainly include mechanics calculation methods, empirical formula methods, finite element methods, and surrogate-based methods. However, due to the structural complexity, these methods are either inaccurate or time consuming. To address this issue, this paper proposes a data-driven method for stiffness prediction of aircraft structural parts. First, machining regions of aircraft structural part finishing are classified into bottom, sidewall, rib and corner to further define the minimum stiffness of machining regions. Then, by representing the part geometry with attribute graph as the input feature, while computing the minimum stiffness using FEM as the output label, stiffness prediction is turned to a graph learning task. Thus, a graph neural network (GNN) is designed and trained to map the attribute graph of a machining region to its minimum stiffness. In the case study, a dataset of aircraft structural parts is used to train four GNN models to predict the minimum stiffness of the defined four types of machining regions. Compared with FEM results, the average percentage errors on the test set are 6.717%, 7.367%, 7.432% and 5.962% respectively. In addition, the data driven model once trained, can greatly reduce the time in predicting the stiffness of a new part compared with FEM, which indicates that the proposed method can meet the engineering requirements in both accuracy and computational efficiency.

A reliable process planning approach based on fuzzy comprehensive evaluation method incorporating historical machining data

2019 ◽  
Vol 234 (5) ◽  
pp. 900-909 ◽  
Author(s):  
Wenping Mou ◽  
Xin Gao
Keyword(s):  
Process Planning ◽  
Evaluation Method ◽  
Tool Path ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Machining Process ◽  
Prototype System ◽  
Machining Parameters ◽  
Entropy Weight ◽  
Structural Parts

The quality of process planning could directly affect product quality, machining efficiency and cost. In small batch production such as machining aircraft structural parts, human experience is dominant in the process planning of those parts with great variability. Inferior planning of the machining process directly leads to low efficiency and quality, which has serious impact on the lead time of aircraft structural parts. To address these problems, different from the existing process knowledge reuse method by estimating the geometric similarity, a more reliable process planning method based on fuzzy comprehensive evaluation via historical machining data is proposed in this article. As long as machining resources are determined, a feature-based historical machining data model can be built, and the similarities between new machining features and the features in the database are estimated accordingly. Machining strategy, which contains tool path strategy and machining parameters, can then be identified according to the evaluation results of the similar features based on entropy weight method. A prototype system is developed and successfully applied to the typical aircraft structural parts.

Dynamics of the macromolecular composition of the biomass of microalgae in the morning under natural light. Model

2020 ◽  
pp. 45-52
Author(s):  
Alexander S. Lelekov ◽  
Anton V. Shiryaev
Keyword(s):  
Biochemical Composition ◽  
Initial Conditions ◽  
Natural Light ◽  
Photoautotrophic Growth ◽  
Structural Part ◽  
Basic Model ◽  
Microalgae Culture ◽  
Simulation Results ◽  
Structural Parts ◽  
Macromolecular Composition

The work is devoted to modeling the growth of optically dense microalgae cultures in natural light. The basic model is based on the idea of the two-stage photoautotrophic growth of microalgae. It is shown that the increase in the intensity of sunlight in the first half of the day can be described by a linear equation. Analytical equations for the growth of biomass of microalgae and its macromolecular components are obtained. As the initial conditions, it is assumed that at the time of sunrise, the concentration of reserve biomass compounds is zero. The simulation results show that after sunrise, the growth of the microalgae culture is due only to an increase in the reserve part of the biomass, while the structural part practically does not change over six hours. Changes in the ratio of the reserve and structural parts of the biomass indicate a change in the biochemical composition of cells.

Performance Analysis on Eco-friendly Machining of Ti6Al4V using Powder Mixed with Different Dielectrics in WEDM

2020 ◽  
Vol 17 (3) ◽  
pp. 8128-8139
Author(s):  
S. Chakraborty ◽  
S. Mitra ◽  
D. Bose
Keyword(s):  
Metal Removal ◽  
Removal Rate ◽  
Machining Process ◽  
Process Efficiency ◽  
Machining Parameters ◽  
Dielectric Fluids ◽  
High Emission ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process

The recent scenario of modern manufacturing is tremendously improved in the sense of precision machining and abstaining from environmental pollution and hazard issues. In the present work, Ti6Al4V is machined through wire EDM (WEDM) process with powder mixed dielectric and analyzed the influence of input parameters and inherent hazard issues. WEDM has different parameters such as peak current, pulse on time, pulse off time, gap voltage, wire speed, wire tension and so on, as well as dielectrics with powder mixed. These are playing an essential role in WEDM performances to improve the process efficiency by developing the surface texture, microhardness, and metal removal rate. Even though the parameter’s influencing, the study of environmental effect in the WEDM process is very essential during the machining process due to the high emission of toxic vapour by the high discharge energy. In the present study, three different dielectric fluids were used, including deionised water, kerosene, and surfactant added deionised water and analysed the data by taking one factor at a time (OFAT) approach. From this study, it is established that dielectric types and powder significantly improve performances with proper set of machining parameters and find out the risk factor associated with the PMWEDM process.

scholarly journals Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path

Micromachines ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 88
Author(s):  
Yupeng Xin ◽  
Yuanheng Li ◽  
Wenhui Li ◽  
Gangfeng Wang
Keyword(s):  
High Speed ◽  
Tool Path ◽  
Milling Process ◽  
Ant Colony ◽  
Machining Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Peripheral Milling ◽  
Structural Part ◽  
Milling Method ◽  
Structural Parts

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

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