Tool Deflection Error of Three-Axis Computer Numerical Control Milling Machines, Monitoring and Minimizing by a Virtual Machining System

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Mohsen Soori ◽  
Behrooz Arezoo ◽  
Mohsen Habibi
Keyword(s):  
Manufacturing Systems ◽  
Optimization Technique ◽  
Dimensional Accuracy ◽  
Virtual Manufacturing ◽  
Numerical Control ◽  
Free Form ◽  
Machining Parameters ◽  
Tool Deflection ◽  
Virtual Machining ◽  
Machining System

Virtual manufacturing systems carry out the simulation of manufacturing processes in digital environment in order to increase accuracy as well as productivity in part production. There are different error sources in machine tools, such as tool deflection, geometrical deviations of moving axis, and thermal distortions of machine tool structures. The errors due to tool deflection are caused by cutting forces and have direct effects on dimensional accuracy, surface roughness of the parts, and efficient life of the cutting tool, holder, and spindle. This paper presents an application of virtual machining systems in order to improve the accuracy and productivity of part manufacturing by monitoring and minimizing the tool deflection error. The tool deflection error along machining paths is monitored to present a useful methodology in controlling the produced parts with regard to desired tolerances. Suitable tool and spindle can also be selected due to the ability of error monitoring. In order to minimize the error, optimization technique based on genetic algorithms is used to determine optimized machining parameters. Free-form profile of virtual and real machined parts with tool deflection error is compared in order to validate reliability as well as accuracy of the software.

Designing and implementation of a novel online adaptive control with optimization technique in hard turning

2020 ◽  
pp. 095965182095219
Author(s):  
Vahid Pourmostaghimi ◽  
Mohammad Zadshakoyan
Keyword(s):  
Adaptive Control ◽  
Tool Wear ◽  
Hard Turning ◽  
Cutting Tool ◽  
Optimization Technique ◽  
Cutting Parameters ◽  
Numerical Control ◽  
Machining Process ◽  
Machining Parameters ◽  
Optimization Methodology

Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.

scholarly journals Virtual Minimization of Residual Stress and Deflection Error in the Five-Axis Milling of Turbine Blades

2021 ◽  
Vol 67 (5) ◽  
pp. 235-244
Author(s):  
Mohsen Soori ◽  
Mohammed Asmael
Keyword(s):  
Residual Stress ◽  
Finite Element ◽  
Virtual Environments ◽  
Turbine Blades ◽  
Research Work ◽  
Machining Parameters ◽  
Virtual Machining ◽  
Machined Parts ◽  
Machining System ◽  
Five Axis

To simulate and analyse the real machined parts in virtual environments, virtual machining systems are applied to the production processes. Due to friction, chip forming, and the heat produced in the cutting zone, parts produced using machining operation have residual stress effects. The machining force and machining temperature can cause the deflection error in the machined turbine blades, which should be minimized to increase the accuracy of machined blades. To minimize the residual stress and deflection error of machined parts, optimized machining parameters can be obtained. In the present research work, the application of a virtual machining system is presented to predict and minimize the residual stress and deflection error in a five-axis milling operations of turbine blades. In order to predict the residual stress and deflection error in machined turbine blades, finite element analysis is implemented. Moreover, to minimize the residual stress and deflection error in machined turbine blades, optimized parameters of machining operations are obtained by using a genetic algorithm. To validate the research work, experimentally determining residual stress by using a X-ray diffraction method from the machined turbine blades is compared with the finite element results obtained from the virtual machining system. Also, in order to obtain the deflection error, the machined blades are measured by using the CMM machines. Thus, the accuracy and reliability of machined turbine blades can be increased by analysing and minimizing the residual stress and deflection error in virtual environments.

scholarly journals Surface Roughness Prediction and Minimization in 5-Axis Milling Operations of Gas Turbine Blades

2021 ◽  
Author(s):  
Arameh Eyvazian ◽  
Farayi Musharavati ◽  
Afrasyab Khan ◽  
Mohsen Soori ◽  
Tamer A. Sebaey ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Gas Turbine ◽  
Turbine Blades ◽  
Machining Process ◽  
Machining Parameters ◽  
Gas Turbine Blades ◽  
Virtual Machining ◽  
Machined Parts ◽  
Machining System

Abstract To enhance the quality of machined parts, virtual machining systems are presented in this study. In the turbine blades, the minimization of the surface roughness of the blades can decrease the Reynolds number to decrease the loss of energy in power generation. Due to difficulties of polishing process in minimizing the surface roughness of machined blades, the optimized machining parameters for minimizing the surface roughness is an effective solution for the problem. In this study, a virtual machining system is developed to predict and minimize the surface roughness in 5-Axis machining operations of gas turbine blades. To minimize the surface roughness, the machining parameters were optimized by the Genetic algorithm. To validate the developed system, the turbine blades were machined using a 5-Axis CNC machine tool and the machined blades were measured using the CMM machine to obtain the surface roughness of machined parts. So, a 41.29% reduction in the measured surface roughness and a 42.09% reduction in the predicted surface roughness are obtained using the optimized machining parameters. The developed virtual machining system can be applied in the machining process of turbine blades to enhance the surface quality of machined blades and thus improve the efficiency of gas turbines.

Development of a virtual machining and inspection system for ultra-precision diamond turning

2007 ◽  
Vol 221 (7) ◽  
pp. 1153-1174 ◽  
Author(s):  
W B Lee ◽  
C F Cheung ◽  
J G Li ◽  
S To ◽  
J J Du ◽  
...  
Keyword(s):  
Simulated Data ◽  
Industrial Applications ◽  
Diamond Turning ◽  
Virtual Manufacturing ◽  
Numerical Control ◽  
Inspection System ◽  
Machined Surface ◽  
Virtual Machining ◽  
Ultra Precision ◽  
Virtual Manufacturing Environment

The conventional approach to establish optimal processing conditions in ultraprecision diamond turning is empirical in nature. The achievement of a super mirror finish and submicrometre form accuracy in many current industrial applications still depends much on the experience and skills of the machine operators through trial cutting tests when new materials or new machine tools are used. In this paper, a virtual machining and inspection system (VMIS) is presented for ultra-precision diamond turning. The development of the VMIS aims at creating a virtual manufacturing environment for the optimization of process parameters and evaluating the quality of machined surface through various modelbased simulation modules. The VMIS is validated through a series of practical cutting tests conducted on a two-axis computer numerical control ultra-precision turning machine. The experimental results agree well with the simulated data.

Investigation of Cutting Parameter and Machine Tool Vibration Effects Using Regression Analysis to Enhance Part Dimensional Accuracy

2015 ◽  
Vol 761 ◽  
pp. 93-97
Author(s):  
M.A. Rahman ◽  
Nur Atiqah Md Sadan ◽  
Mohammad Minhat ◽  
Halim Isa ◽  
Abu Bakar Baharudin
Keyword(s):  
Machine Tool ◽  
Cutting Speed ◽  
Dimensional Accuracy ◽  
Numerical Control ◽  
Manufacturing Cost ◽  
Machining Parameters ◽  
Turning Process ◽  
High Quality ◽  
Tool Vibration ◽  
Machined Parts

Dimensional accuracy plays important criteria in producing high quality machined parts. This is a big challenge to manufacturers of precision components to produce good quality parts with minimum manufacturing error. The focus of this paper is to study the influence of the machine tool rigidity and cutting parameters on dimensional accuracy in turning operation. A method was prepared for identifying the factors effecting dimensional accuracy in a turning process. Experimental setup involved computerized numerical control (CNC) lathe machine, with VBMT 160404 carbide insert and mild steel, as cutting tool and workpiece respectively. The statistical analysis was used for analyzing and determining the accuracy of experimental data through Minitab statistical software. The regressions model was developed. The developed regression model could be used to predict the dimensional precision of the parts based on machine tool vibration and machining parameters during turning process. This is the aspect to be seriously considered and be applied in attaining sustainable machine tool development during design and development stage and its usage. This finding provides useful guidelines for manufacturers to produce high quality machined parts at minimum manufacturing cost. It was found that the cutting speed, feed rate, final part length, vibration x and vibration z have significant effects on dimensional accuracy of the machined parts.

scholarly journals Deflection Error Prediction and Minimization in 5-Axis Milling Operations of Thin-Walled Impeller Blades

2020 ◽  
Author(s):  
Mohsen Soori ◽  
Mohammed Asmael
Keyword(s):  
Machine Tools ◽  
Turbine Blades ◽  
Cnc Machining ◽  
Cnc Machine Tools ◽  
Machining Parameters ◽  
Cnc Machine ◽  
Virtual Machining ◽  
Machining System ◽  
Thin Walled ◽  
Machining Operations

Abstract To enhance accuracy as well as efficiency in process of machining operations, the virtual machining systems are developed. Free from surfaces of sophisticated parts such as turbine blades, airfoils, impellers, and aircraft components are produced by using the 5-axis CNC machine tools which can be analyzed and developed by using virtual machining systems. The machining operations of thin walled structures such as impeller blades are with deflection errors due to cutting forces and cutting temperatures. The flexibility of thin walled impeller blades can cause machining defects such as overcut or undercut. So, the desired accuracy in the machined impeller blades can be achieved by decreasing the deflection error in the machining operations. To minimize the deflection of machined impeller blades, optimized machining parameters can be obtained. An application of virtual machining system in predicting and minimizing the deflection errors of 5-Axis CNC machining operations of impeller blades is presented in the study to increase accuracy and efficiency in process of part production. The finite element analysis is applied to obtain the deflection error in machined impeller blades. In order to minimize the deflection error of impeller blades in the machining operations, the optimization methodology based on the Genetic algorithm is applied. The impeller is machined by using the 5-axis CNC machine tool in order to validate the developed virtual machining system in the study. Then, the machined impeller is measured by using the CMM machines to obtain the deflection error. As a result, the deflection error of in machining operations of impeller by using 5-Axis CNC machine tools can be decreased in order to enhance accuracy and efficiency of part manufacturing.

Modeling and Simulation of Material Removal Rates and Profile Accuracy Control in Abrasive Flow Machining of the Integrally Bladed Rotor Blade and Experimental Perspectives

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Kai Cheng ◽  
Yizhi Shao ◽  
Rodrigo Bodenhorst ◽  
Mitul Jadva
Keyword(s):  
Dimensional Accuracy ◽  
Accuracy Control ◽  
Abrasive Flow Machining ◽  
Virtual Machining ◽  
Computational Environment ◽  
Component Form ◽  
Simulation Based ◽  
Machining System ◽  
Bladed Rotor ◽  
Profile Accuracy

Abrasive flow machining (AFM) technology is getting more and more interest by the industry and research community particularly in the context of increasing demands for postprocessing of the additively manufactured and complex components. It is essentially important to develop an industrial feasible approach to controlling and improving the profile accuracy (form and dimensional) of components as well as their surface roughness. In this paper, a multiscale multiphysics simulation-based approach is presented to model and simulate the AFM process against the component form and dimensional accuracy control in particular. The simulation is developed in comsol which is a multiphysics computational environment. Well-designed AFM experiment trials are carried out on a purposely configured blade “coupon” to further evaluate and validate the simulations. The AFM machine and specific machining media for the experiments are provided by the industrial collaboration company, with their further industrial inputs. Both the simulation and experimental trial results illustrate that the approach is applicable to the blade profile prediction and accuracy control, which is used as a foundation for developing the simulation-based AFM virtual machining system.

A Virtual Machining Model for Sustainability Analysis

2010 ◽  
Author(s):  
Guodong Shao ◽  
Deogratias Kibira ◽  
Kevin Lyons
Keyword(s):  
Environmental Impact ◽  
Social Performance ◽  
Sustainable Manufacturing ◽  
Simulation Models ◽  
Numerical Control ◽  
Machining Process ◽  
Shop Floor ◽  
Sustainable Machining ◽  
Virtual Machining ◽  
Machining System

Sustainability has become a very significant research topic since it impacts many different manufacturing industries. The adoption of sustainable manufacturing practices and technologies offers industry a cost effective route to improve economic, environmental, and social performance. As a major manufacturing process, the machining system plays an important role for sustainable manufacturing on the factory floor. Therefore, technologies for monitoring, analyzing, evaluating, and optimizing the sustainability impact of machining systems are critical for decision makers. Modeling and Simulation (M&S) can be an effective tool for success of sustainable manufacturing through its ability to predict the effect of implementing a new facility, process without interrupting real production. This paper introduces a methodology that provides a traditional virtual Numerical Control (NC) machining model with a new capability — to quantitatively analyze the environmental impact of machining system based on Life Cycle Assessment (LCA). The objective of the methodology is to analyze the sustainability impacts of machining process and determine a better plan for improving the sustainable performance of machining system in a virtual environment before work orders are released to the shop floor. Testing different scenarios with simulation models ensures the best setting option available can be chosen. The virtual NC model provides the necessary data for this assessment. In this paper, a list of environmental impact indicators and their metrics has been identified, and modeling elements for sustainable machining have been discussed. Inputs and outputs of the virtual machining model for sustainable machining are described. A case study to experiment the proposed methodology is discussed.

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