In order to effectively avoid the violent vibration in the process of mechanical processing and to achieve high efficiency and high precision machining of mechanical parts, the improved algorithm of adaptive neuro-fuzzy inference system is used to study the optimization of parameters in the process of side milling of mechanical parts, and an adaptive network structure is formed. It has the learning ability of artificial neural network and the expression ability of “if-then” of fuzzy reasoning system, which is a new prediction and control method. The results validate the applicability of the stability. The machined surface topography is measured and the effect of flutter on the surface topography is analyzed. The three-dimensional stability of milling provides a theoretical basis for the rational selection of milling parameters of mechanical parts, the realization of stable milling and the improvement of processing efficiency. Thus, the relationship between the radial depth of cut, the axial depth of cut and the spindle speed is established, and the contour of material removal rate is obtained. The corresponding spindle speed and radial shear depth are obtained when the material removal rate is maximum. The reasonable selection of machining parameters is carried out in the region near the maximum spindle speed with stability.
BaldovinoR.G., ValenzuelaI.C., BandalaA.A. and DadiosE.P., Optimization of CO2 Laser Cutting parameters using adaptive neuro-fuzzy inference system (anfis), Journal of Telecommunication Electronic and Computer Engineering (JTEC)10(1–9) (2018), 103–107.
2.
KarimM.R., DilwarF. and SiddiqueR.A., Predictive modeling of surface roughness in mql assisted turning of sic-al alloy composites using artificial neural network and adaptive neuro fuzzy inference system, Journal of Advanced Research in Manufacturing Material Science & Metallurgical Engineering5(3) (2018), 12–28.
3.
MathaiV.J., DaveH.K. and DesaiK.P., End wear compensation during planetary edm of ti–6al–4v by adaptive neuro fuzzy inference system, Production Engineering12(1) (2018), 1–10.
4.
TeimouriR. and AminiS., Forward and reverse mapping of multiresponses turning process using adaptive network-based fuzzy inference system and simulated annealing algorithm, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science232(10) (2018), 1746–1758.
5.
MasoudiS., SimaM. and Tolouei-RadM.A.J.I.D., Comparative study of ANN and ANFIS models for predicting temperature in machining, Journal of Engineering Science and Technology13(1) (2018), 211–225.
6.
UnuneD.R., BarzaniM.M., MohiteS.S. and MaliH.S., Fuzzy logic-based model for predicting material removal rate and average surface roughness of machined Nimonic 80A using abrasive-mixed electro-discharge diamond surface grinding, Neural Computing and Applications29(9) (2018), 647–662.
7.
ChauN.E., NguyenM.Q., DaoT.P., HuangS.C., HsiaoT.C., Dinh-CongD. and DangV.A., An effective approach of adaptive neuro-fuzzy inference system-integrated teaching learning-based optimization for use in machining optimization of S45C CNC turning, Optimization and Engineering20(3) (2019), 811–832.
8.
SavkovicB., KovacP., DudicB., RodicD., TaricM. and GregusM., Application of an Adaptive “Neuro-Fuzzy” Inference System in Modeling Cutting Temperature during Hard Turning, Applied Sciences9(18) (2019), 3739.
9.
JainV. and RajT., An adaptive neuro-fuzzy inference system for makespan estimation of flexible manufacturing system assembly shop: a case study, International Journal of System Assurance Engineering and Management9(6) (2018), 1302–1314.
10.
SaranyaK., JegarajJ.J.R., KumarK.R. and RaoG.V., Artificial intelligence based selection of optimal cutting tool and process parameters for effective turning and milling operations, Journal of The Institution of Engineers (India): Series C99(4) (2018), 381–392.
11.
XuL., HuangC., SuR., ZhuH., LiuH., LiuY. and WangJ., Estimation of tool life and cutting burr in high speed milling of the compacted graphite iron by DE based adaptive neuro-fuzzy inference system, Mechanical Sciences10(1) (2019), 243–254.
12.
LiangS., RajoraM., LiuX., YueC., ZouP. and WangL., Intelligent manufacturing systems: a review, International Journal of Mechanical Engineering and Robotics Research7(3) (2018), 324–330.
13.
JainV. and RajT., Prediction of cutting force by using ANFIS, International Journal of System Assurance Engineering and Management9(5) (2018), 1137–1146.
14.
RameshP., NandaS.R., KulkarniV. and DwivedyS.K., Application of neural-networks and neuro-fuzzy systems for the prediction of short-duration forces acting on the blunt bodies, Soft Computing23(14) (2019), 5725–5738.
15.
García-GutiérrezG.,
Arcos-AvilesD.,
CarreraE.V.,
GuinjoanF.,
MotoascaE.,
AyalaP. and
IbarraA., Fuzzy logic controller parameter optimization using metaheuristic cuckoo search algorithm for a magnetic levitation system, Applied Sciences9(12) (2019), 2458.
16.
MukhopadhyayA., BarmanT.K., SahooP. and DavimJ.P., Modeling and optimization of fractal dimension in wire electrical discharge machining of en 31 steel using the ann-ga approach, Materials12(3) (2019), 454.
17.
DosdogruA.T., Improving weather forecasting using de-noising with maximal overlap discrete wavelet transform and ga based neuro-fuzzy controller, International Journal on Artificial Intelligence Tools28(03) (2019), 1950012.
18.
RaoD.S. and TripathyD.P., A genetic algorithm approach for optimization of machinery noise calculations, Noise & Vibration Worldwide50(4) (2019), 112–123.
19.
BabuK.K., PanneerselvamK., SathiyaP., HaqA.N., SundarrajanS., MastanaiahP. and MurthyC.S., Parameter optimization of friction stir welding of cryorolled AAalloy using artificial neural network modeling with genetic algorithm, The International Journal of Advanced Manufacturing Technology94(9–12) (2018), 3117–3129.
20.
RustamovS., MustafayevE. and ClementsM.A., Context analysis of customer requests using a hybrid adaptive neuro fuzzy inference system and hidden Markov models in the natural language call routing problem, Open Engineering8(1) (2018), 61–68.
21.
RoshaniG.H., KaramiA. and NazemiE., An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water, Computational and Applied Mathematics38(1) (2019), 5.
22.
CuevasE., DíazP., AvalosO., ZaldivarD. and Pérez-CisnerosM., Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm, Applied Intelligence48(1) (2018), 182–203.
23.
DasP.P., DiyaleyS., ChakrabortyS. and GhadaiR.K., Multi-objective optimization of wire electro discharge machining (wedm) process parameters using grey-fuzzy approach, Periodica Polytechnica Mechanical Engineering63(1) (2019), 16–25.
24.
AlsamhanA., RagabA.E., DabwanA., NasrM.M. and HidriL., Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques, Plos One14(8) (2019), e0221341.
25.
ZhouQ., RenY., DuY., HanW., HuaD., ZhaiH. and WangH., Identifying the significance of Sn addition on the tribological performance of Ti-based bulk metallic glass composites, Journal of Alloys and Compounds780 (2019), 671–679.
