Статті в журналах: "Roughness optimization"

1

Kamaruzaman, Anis Farhan, Azlan Mohd Zain, Razana Alwee, Noordin Md Yusof, and Farhad Najarian. "Optimization of Surface Roughness in Deep Hole Drilling using Moth-Flame Optimization." ELEKTRIKA- Journal of Electrical Engineering 18, no. 3-2 (December 24, 2019): 62–68. http://dx.doi.org/10.11113/elektrika.v18n3-2.195.

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This study emphasizes on optimizing the value of machining parameters that will affect the value of surface roughness for the deep hole drilling process using moth-flame optimization algorithm. All experiments run on the basis of the design of experiment (DoE) which is two level factorial with four center point. Machining parameters involved are spindle speed, feed rate, depth of hole and minimum quantity lubricants (MQL) to obtain the minimum value for surface roughness. Results experiments are needed to go through the next process which is modeling to get objective function which will be inserted into the moth-flame optimization algorithm. The optimization results show that the moth-flame algorithm produced a minimum surface roughness value of 2.41µ compared to the experimental data. The value of machining parameters that lead to minimum value of surface roughness are 900 rpm of spindle speed, 50 mm/min of feed rate, 65 mm of depth of hole and 40 l/hr of MQL. The ANOVA has analysed that spindle speed, feed rate and MQL are significant parameters for surface roughness value with P-value <0.0001, 0.0219 and 0.0008 while depth of hole has P-value of 0.3522 which indicates that the parameter is not significant for surface roughness value. The analysis also shown that the machining parameter that has largest contribution to the surface roughness value is spindle speed with 65.54% while the smallest contribution is from depth of hole with 0.8%. As the conclusion, the application of artificial intelligence is very helpful in the industry for gaining good quality of products.

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2

Lan. "Parametric Deduction Optimization for Surface Roughness." American Journal of Applied Sciences 7, no. 9 (September 1, 2010): 1248–53. http://dx.doi.org/10.3844/ajassp.2010.1248.1253.

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3

Fan Di, 范镝. "Optimization of SiC Mirror Surface Roughness." Laser & Optoelectronics Progress 51, no. 9 (2014): 092206. http://dx.doi.org/10.3788/lop51.092206.

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Cardoso, Pedro, and J. Paulo Davim. "Optimization of Surface Roughness in Micromilling." Materials and Manufacturing Processes 25, no. 10 (December 3, 2010): 1115–19. http://dx.doi.org/10.1080/10426914.2010.481002.

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5

Nosonovsky, Michael, and Bharat Bhushan. "Roughness optimization for biomimetic superhydrophobic surfaces." Microsystem Technologies 11, no. 7 (July 2005): 535–49. http://dx.doi.org/10.1007/s00542-005-0602-9.

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Fabre, D., C. Bonnet, J. Rech, and T. Mabrouki. "Optimization of surface roughness in broaching." CIRP Journal of Manufacturing Science and Technology 18 (August 2017): 115–27. http://dx.doi.org/10.1016/j.cirpj.2016.10.006.

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7

Dikshit, Mithilesh K., Asit B. Puri, and Atanu Maity. "Optimization of surface roughness in ball-end milling using teaching-learning-based optimization and response surface methodology." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 14 (February 29, 2016): 2596–607. http://dx.doi.org/10.1177/0954405416634266.

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Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth ( fz) = 0.06 mm, axial depth of cut ( Ap) = 0.74 mm, cutting speed ( Vc) = 145.8 m/min, and radial depth of cut ( Ae) = 0.38 mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 µm and has been validated by confirmation experiments.

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8

Bhushan, B. "Methodology for roughness measurement and contact analysis for optimization of interface roughness." IEEE Transactions on Magnetics 32, no. 3 (May 1996): 1819–25. http://dx.doi.org/10.1109/20.492871.

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9

Rao, Ch Maheswara, S. Srikanth, R. Vara Prasad, and G. Babji. "Simultaneous Optimization of Roughness Parameters using TOPSIS." International Journal of Engineering Trends and Technology 49, no. 3 (July 25, 2017): 150–57. http://dx.doi.org/10.14445/22315381/ijett-v49p223.

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10

Nosonovsky, Michael, and Bharat Bhushan. "Hierarchical roughness optimization for biomimetic superhydrophobic surfaces." Ultramicroscopy 107, no. 10-11 (October 2007): 969–79. http://dx.doi.org/10.1016/j.ultramic.2007.04.011.

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11

Lan, Tian-Syung. "Fuzzy Linguistic Optimization on Surface Roughness for CNC Turning." Mathematical Problems in Engineering 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/572506.

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Surface roughness is often considered the main purpose in contemporary computer numerical controlled (CNC) machining industry. Most existing optimization researches for CNC finish turning were either accomplished within certain manufacturing circumstances or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme is deemed to be necessary for the industry. In this paper, the cutting depth, feed rate, speed, and tool nose runoff with low, medium, and high level are considered to optimize the surface roughness for finish turning based onL9(34)orthogonal array. Additionally, nine fuzzy control rules using triangle membership function with respective to five linguistic grades for surface roughness are constructed. Considering four input and twenty output intervals, the defuzzification using center of gravity is then completed. Thus, the optimum general fuzzy linguistic parameters can then be received. The confirmation experiment result showed that the surface roughness from the fuzzy linguistic optimization parameters is significantly advanced compared to that from the benchmark. This paper certainly proposes a general optimization scheme using orthogonal array fuzzy linguistic approach to the surface roughness for CNC turning with profound insight.

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12

Lu, Xiaohong, FuRui Wang, Liang Xue, Yixuan Feng, and Steven Y. Liang. "Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718." Industrial Lubrication and Tribology 71, no. 6 (August 12, 2019): 787–94. http://dx.doi.org/10.1108/ilt-07-2018-0259.

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Purpose The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718. Design/methodology/approach Taguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. Findings This paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718. Originality/value There are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.

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13

Sankar, M., A. Gnanavelbabu, and R. Baskaran. "Optimization of Surface Roughness in Electro Chemical Machining." Applied Mechanics and Materials 606 (August 2014): 193–97. http://dx.doi.org/10.4028/www.scientific.net/amm.606.193.

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Electro chemical machining seems to be the future of micro and nanomachining due to its advantages like high MRR, no tool wear, highly precise, reliability, better control over machining and so on. Surface roughness is an important factor in electro chemical machining. ECM can produce surface roughness of the order of 0.4μm. This paper is devoted to the study of micro ECM process to obtain a surface roughness of about 0.3μm in an alluminium alloy specimen using a copper electrode.

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14

Choi, J., C. W. Lee, and J.-H. Park. "Development of the process model for plunge grinding and optimization of grinding process." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 11 (July 25, 2011): 2628–37. http://dx.doi.org/10.1177/0954406211406201.

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This article presents improved grinding roughness model established for cylindrical plunge grinding though analysing the existing roughness models. The proposed roughness model is to consider the uncertain effects of grinding, such as changes of grinding conditions and circumstance, as grinding procedure is progressed. In order to consider the uncertain effect of grinding, the time delay between programmed and actual infeed rate of grinding table is selected as weighting factor of the proposed roughness model. The developed roughness model is also used for the optimization algorithm of grinding procedure. Optimization algorithm in this study is constructed to minimize the grinding cost and to obtain the optimized dressing and grinding conditions under grinding constraints such as no-burn condition, limitation of roughness of workpiece, grinding power, etc. The optimized results also give an optimized dressing interval in batch production. The used optimization algorithm is an Evolutionary Strategy algorithm, and performance of the proposed algorithm was evaluated with experiments.

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15

Abhijith, Pai Srinivasa, D’Mello Grynal, and Hebbar Gautama. "Surface roughness optimization in machining of AZ31 magnesium alloy using ABC algorithm." MATEC Web of Conferences 144 (2018): 03006. http://dx.doi.org/10.1051/matecconf/201814403006.

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Magnesium alloys serve as excellent substitutes for materials traditionally used for engine block heads in automobiles and gear housings in aircraft industries. AZ31 is a magnesium alloy finds its applications in orthopedic implants and cardiovascular stents. Surface roughness is an important parameter in the present manufacturing sector. In this work optimization techniques namely firefly algorithm (FA), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC) which are based on swarm intelligence techniques, have been implemented to optimize the machining parameters namely cutting speed, feed rate and depth of cut in order to achieve minimum surface roughness. The parameter Ra has been considered for evaluating the surface roughness. Comparing the performance of ABC algorithm with FA and PSO algorithm, which is a widely used optimization algorithm in machining studies, the results conclude that ABC produces better optimization when compared to FA and PSO for optimizing surface roughness of AZ 31.

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16

Nguyen, Quoc-Manh, and The-Vinh Do. "Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles." Advances in Materials Science and Engineering 2022 (October 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/2627522.

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Productivity and quality are always two goals in the production process. In metal cutting, two prominent representatives of quality and productivity are roughness and material removal rate (MRR). In this study, the Response Surface method was used to perform single-objective and multiobjective optimizations during the hard milling of SKD11 steel. From there, comparative analyzes are carried out to give effective advice for different approaches in actual production. The selected inputs are the nanoparticle concentration in the cutting oil and three typical cutting parameters including cutting velocity, depth of cut, and feed rate. Each input will have three levels including low, high and average. The L27 orthogonal array developed by Taguchi was applied to the experimental design. In addition, ANOVA was also used to evaluate the statistical indicators of the study. The results of single-objective optimization show that the feed rate is the main influencing factor for the roughness followed by the nanoparticle concentration. They contribute 51.2% and 21.12% of the total roughness effect, respectively. On the other hand, the main factors affecting the material removal rate are the depth of cut and feed rate. In multiobjective optimization, a compromise solution has also been proposed to achieve small roughness and high material removal rate. The minimum roughness was 0.1956 μm and the maximum material removal rate was 1479.8688 mm3/min when applying the multiobjective optimal machining condition.

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17

Aurtherson, P. Babu, S. Sundaram, A. M. Shanawaz, and M. Siva Prakash. "Grinding Process on AlSic composite material and Optimization of surface roughness by ANFIS." International Journal of Engineering and Technology 3, no. 4 (2011): 425–30. http://dx.doi.org/10.7763/ijet.2011.v3.264.

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18

Abbas, Adnan Jameel, Mohammad Minhat, and Md Nizam Abd Rahman. "Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm." Applied Mechanics and Materials 761 (May 2015): 132–36. http://dx.doi.org/10.4028/www.scientific.net/amm.761.132.

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. The minimum cost and high productivity of the recent industrial renaissance are its main challengers. Selecting the optimum cutting parameters play a significant role in achieving these aims. Heat generated in the cutting zone area is an important factor affecting workpiece and cutting tool properties. The surface finish quality specifies product success and integrity. In this paper, the temperature generated in the cutting zone (shear zone and chip-tool interface zone) and workpiece surface roughness is optimized using an artificial immune system (AIS) intelligent algorithm. A mild steel type (S45C) workpiece and a tungsten insert cutting tool type (SPG 422) is subjected to dry CNC turning operation are used in experiments. Optimum cutting parameters (cutting velocity, depth of cut, and feed rate) calculated by the (AIS) algorithm are used to obtain the simulated and ideal cutting temperature and surface roughness. An infrared camera type (Flir E60) is used for temperature measurement, and a portable surface roughness device is used for roughness measurement. Experimental results show that the ideal cutting temperature (110°C) and surface roughness (0.49 μm) occur at (0.3 mm) cut depth, (0.06 mm) feed rate, and (60 m/min) cutting velocity. The AIS accuracy rates in finding the ideal cutting temperature and surface roughness are (91.70 %) and (90.37 %) respectively. Analysis shows that the predicted results are close to the experimental ones, indicating that this intelligent system can be used to estimate cutting temperature and surface roughness during the turning operation of mild steel.

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JAROSZ, Krzysztof, and Piotr LÖSCHNER. "THE EFFECT OF CHANGES IN DEPTH OF CUT ON SURFACE ROUGHNESS IN MACHINING OF AISI 316 STAINLESS STEEL." Journal of Machine Engineering Vol.18, No.1 (February 22, 2018): 73–80. http://dx.doi.org/10.5604/01.3001.0010.8824.

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Currently, process optimization is an important part of design of CNC toolpath, allowing overall process improvement in accordance to a range of criteria. Available CAE software for CNC toolpath optimization works only by changing the feed rate value specified in the base toolpath. The authors are planning to devise a solution allowing for optimization of other process parameters, including depth of cut. In some cases, it would be important for surface roughness to remain unaltered after optimization by means of increasing depth of cut. In this work, the effect of depth of cut on surface roughness was investigated. Depth of cut was altered for the roughing pass, while technological parameters for the finish pass remained constant. Roughness measurements were performed on-machine after rough turning and finish turning. The authors have found that depth of cut has a noticeable effect on investigated roughness parameters, both in the case of rough turning and subsequent finish turning operations.

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20

Alajmi, Mahdi S., and Abdullah M. Almeshal. "Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material." Applied Sciences 11, no. 5 (February 27, 2021): 2126. http://dx.doi.org/10.3390/app11052126.

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Surface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research investigated the performance of state-of-the-art machine learning and quantum behaved evolutionary computation methods in predicting the surface roughness of aluminum material in a face-milling machine. Quantum-behaved particle swarm optimization (QPSO) and least squares gradient boosting ensemble (LSBoost) were utilized to simulate numerous face milling experiments and have predicted the surface roughness values with high extent of accuracy. The algorithms have shown a superior prediction performance over genetics optimization algorithm (GA) and the classical particle swarm optimization (PSO) in terms of statistical performance indicators. The QPSO outperformed all the simulated algorithms with a root mean square error of RMSE = 2.17% and a coefficient of determination R2 = 0.95 that closely matches the actual surface roughness experimental values.

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Zhang, Shu Ren, Xue Guang Li, Jun Wang, and Hui Wei Wang. "Research on Optimization of Cutting Parameters Based on Genetic Algorithm." Applied Mechanics and Materials 121-126 (October 2011): 4640–45. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4640.

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Three elements of Cutting dosages have a great effect on parts surface quality and working efficiency, the best organization of cutting three elements for parts surface quality must be found before machining, in order to surface roughness and machining cost of parts, multi-objective optimization model is established in this paper, model is solved by using genetic algorithm. Based on the BP neural network of three layers, forecasting model of surface roughness is established. According to existing experiment data and optimized cutting dosages, analysis and prediction of surface roughness is done. Machining experiment is done by using optimized data. The experiment result verifies feasibility of this optimistic method and prediction method of surface roughness.

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Al Hazza, Muataz H. F., Erry Yulian Triblas Adesta, Muhammad Riza, and M. Y. Suprianto. "Surface Roughness Optimization in End Milling Using the Multi Objective Genetic Algorithm Approach." Advanced Materials Research 576 (October 2012): 103–6. http://dx.doi.org/10.4028/www.scientific.net/amr.576.103.

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In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

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23

Xiong, Neng, Yang Tao, Zhiyong Liu, and Jun Lin. "Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840048. http://dx.doi.org/10.1142/s0217984918400481.

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The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the [Formula: see text]-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

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24

Tian, Yu, Shifu Liu, Le Liu, and Peng Xiang. "Optimization of International Roughness Index Model Parameters for Sustainable Runway." Sustainability 13, no. 4 (February 18, 2021): 2184. http://dx.doi.org/10.3390/su13042184.

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Pavement roughness is a critical airport pavement characteristic that has been linked to impacts such as safety and service life. A properly defined roughness evaluation method would reduce airport operational risk, prolong the life of aircraft landing gear, and optimize the decision-making process for pavement preservation, which together positively contribute to overall airport sustainability. In this study, we optimized the parameters of the International Roughness Index (IRI) model to resolve the current poor correlation between the IRI and aircraft vibration responses in order to adapt and extend the IRI’s use for airport runway roughness evaluation. We developed and validated a virtual prototype model based on ADAMS/Aircraft software for the Boeing 737–800 and then employed the model to predict the aircraft’s dynamic responses to runway pavement roughness. By developing a frequency response function for the standard 1/4 vehicle model, we obtained frequency response distribution curves for the IRI. Based on runway roughness data, we used fast Fourier transform to implement the frequency response distribution of the aircraft. We then utilized Particle Swarm Optimization to determine more appropriate IRI model parameters rather than modifying the model itself. Our case study results indicate that the correlation coefficient for the optimized IRI model and aircraft vibration response shows a qualitative leap from that of the original IRI model.

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25

Kumar, Deepak. "Optimization of Surface Roughness Using Quality Engineering Techniques." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 2717–22. http://dx.doi.org/10.22214/ijraset.2018.3437.

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Rumman, Md Raihanuzzaman, and Soon Jik Hong. "Optimization of Surface Roughness by Taguchi Design Method." Advanced Materials Research 156-157 (October 2010): 392–95. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.392.

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Maintaining good surface quality usually involves additional manufacturing cost or loss of productivity. The Taguchi design is an efficient and effective experimental method in which a response variable can be optimized, given various control and noise factors, using fewer resources than a factorial design. This study included feed rate, spindle speed and depth of cut as control factors, and the noise factors were the operating chamber temperature and the usage of different tool inserts in the same specification. An orthogonal array of L9 (34) was used and the optimal cutting combination was determined by seeking the best surface roughness (response) and signal-to-noise ratio.

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Subhashini, P. V. S., N. Amulya, S. Kirthana, and G. Pradeep. "Parametric Optimization Of Surface Roughness Using SCARA Manipulator." Materials Today: Proceedings 5, no. 5 (2018): 11971–76. http://dx.doi.org/10.1016/j.matpr.2018.02.171.

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28

Bozdemir, M., and Ş. Aykut. "Optimization of surface roughness in end milling Castamide." International Journal of Advanced Manufacturing Technology 62, no. 5-8 (December 27, 2011): 495–503. http://dx.doi.org/10.1007/s00170-011-3840-2.

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Padhy, Chinmaya, and Pariniti Singh. "Optimization of Machining Parameters using Taguchi Coupled Grey Relational Approach while Turning Inconel 625." Journal of Mechanical Engineering 18, no. 2 (April 15, 2021): 161–76. http://dx.doi.org/10.24191/jmeche.v18i2.15151.

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In manufacturing industries preparation of quality surfaces is very important. The surface roughness will influence the quality and effectiveness of the subsequent coatings for protection against corrosion, wear resistance etc. For achieving desired surface roughness, factors like cutting force (N) and material removal rate (mm3/sec) plays an important role towards final product optimization. This study helps to determine the contribution of each machining parameters [cutting speed (v), feed rate (f) and depth-of-cut (d)] and their interaction to investigate their optimum values during dry turning of Inconel 625 with the objective of enhancing the productivity (optimum production) by minimizing surface roughness (Ra), cutting forces (Fc), whereas maximizing material removal rate (MRR). This kind of multi response process variable (MRP) problems usually known as multi-objective optimizations (MOOs) are resolved with the help of Taguchi and Grey relation approach (T-GRA). As a result, the attained optimum cutting parameters are viz. cutting speed (60 m/min), feed rate (0.3 mm/rev), depth-of-cut (0.25 mm) lead to value of desired variables - cutting forces (340 N), surface roughness (0.998 μm) and material removal rate (0.786 mm3/min).

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Naseem Abbas, Naseem Abbas, Muzamil Hussain Muzamil Hussain, Nida Zahra Nida Zahra, Hassaan Ahmad Hassaan Ahmad, Syed Muhammad Zain Mehdi Syed Muhammad Zain Mehdi, and Uzair Sajjad and Mohammed Amer Uzair Sajjad and Mohammed Amer. "Optimization of Cr Seed Layer Effect for Surface Roughness of As-Deposited Silver Film using Electron Beam Deposition Method." Journal of the chemical society of pakistan 42, no. 1 (2020): 23. http://dx.doi.org/10.52568/000612.

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The surface roughness is an important parameter in determining the physical properties and quality of thin films deposited by physical vapor deposition (PVD) method. The presence of an intermediate layer between metallic nanoparticles and substrate significantly promotes the adhesion and reduces the surface roughness. In this article, we have investigated the effect of Chromium (Cr) seed layer to optimize the surface roughness on the growth of as-deposited silver (Ag) film using borosilicate glass and silicon wafer substrates. For this purpose, Ag thin films were deposited with a Cr seed layer of different thickness on borosilicate glass and silicon wafer substrates using an electron beam (E-Beam) deposition method. The Cr thin film of different thickness ranging from 1 nm to 6 nm was thermally evaporated and pure Ag with the same thickness was evaporated at the same rate on previously coated substrates. The deposition of the nanostructured thin film was confirmed by UV-Vis and XRD characterizations. The difference in transmittance for uncoated and coated substrates ensured the deposition. The presence of pure Ag crystalline phase was confirmed by XRD pattern. Surface roughness was measured using Atomic Force Microscopy (AFM) and the conductance was measured using 4-probe conductivity method. The density of nanoparticles and smoothness were visualized from two dimensional (2D) and three dimensional (3D) surface height histograms of representative AFM images. The quantitative roughness was measured in terms of root mean square (RMS) roughness and mean roughness. The high dense and smoother thin films were found for ~2-4 nm Cr layer thickness in case of the glass substrate. The slight increase in roughness was observed for ~1-6 nm Cr layer thickness in case of the silicon substrate. The dependence of the conductivity of thin films on surface roughness is investigated to verify the effect of surface roughness on different applications of Ag thin film. The conductance results have been analyzed as; for a glass substrate, conductivity was maximum for thin films containing ~2 nm Cr seed layer thickness, while for silicon substrate the maximum conductivity was found for the thin film containing ~1 nm Cr seed layer.

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Naseem Abbas, Naseem Abbas, Muzamil Hussain Muzamil Hussain, Nida Zahra Nida Zahra, Hassaan Ahmad Hassaan Ahmad, Syed Muhammad Zain Mehdi Syed Muhammad Zain Mehdi, and Uzair Sajjad and Mohammed Amer Uzair Sajjad and Mohammed Amer. "Optimization of Cr Seed Layer Effect for Surface Roughness of As-Deposited Silver Film using Electron Beam Deposition Method." Journal of the chemical society of pakistan 42, no. 1 (2020): 23. http://dx.doi.org/10.52568/000612/jcsp/42.01.2020.

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The surface roughness is an important parameter in determining the physical properties and quality of thin films deposited by physical vapor deposition (PVD) method. The presence of an intermediate layer between metallic nanoparticles and substrate significantly promotes the adhesion and reduces the surface roughness. In this article, we have investigated the effect of Chromium (Cr) seed layer to optimize the surface roughness on the growth of as-deposited silver (Ag) film using borosilicate glass and silicon wafer substrates. For this purpose, Ag thin films were deposited with a Cr seed layer of different thickness on borosilicate glass and silicon wafer substrates using an electron beam (E-Beam) deposition method. The Cr thin film of different thickness ranging from 1 nm to 6 nm was thermally evaporated and pure Ag with the same thickness was evaporated at the same rate on previously coated substrates. The deposition of the nanostructured thin film was confirmed by UV-Vis and XRD characterizations. The difference in transmittance for uncoated and coated substrates ensured the deposition. The presence of pure Ag crystalline phase was confirmed by XRD pattern. Surface roughness was measured using Atomic Force Microscopy (AFM) and the conductance was measured using 4-probe conductivity method. The density of nanoparticles and smoothness were visualized from two dimensional (2D) and three dimensional (3D) surface height histograms of representative AFM images. The quantitative roughness was measured in terms of root mean square (RMS) roughness and mean roughness. The high dense and smoother thin films were found for ~2-4 nm Cr layer thickness in case of the glass substrate. The slight increase in roughness was observed for ~1-6 nm Cr layer thickness in case of the silicon substrate. The dependence of the conductivity of thin films on surface roughness is investigated to verify the effect of surface roughness on different applications of Ag thin film. The conductance results have been analyzed as; for a glass substrate, conductivity was maximum for thin films containing ~2 nm Cr seed layer thickness, while for silicon substrate the maximum conductivity was found for the thin film containing ~1 nm Cr seed layer.

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32

Xu, Qing Zhong, Fang Yi Li, Shi Lei Ma, and Jun Zhuang Liu. "Study on Milling Parameters Optimization in Remanufacturing Cold-Welding Area." Advanced Materials Research 500 (April 2012): 123–27. http://dx.doi.org/10.4028/www.scientific.net/amr.500.123.

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Considering cold-welding repaired layer is rough and microstructure contains hard phases, firstly, the two-factor three-level orthogonal milling experiment was conducted in remanufacturing cold-welding repaired area, then the influences of different parameters on Ni-base cold-welding repaired layer milling were studied. At the end, cutting force and surface roughness were used as objectives to optimize the milling parameters. Cutting force and surface roughness were processed by range analysis, and their influence sequences were obtained. The empirical formula of milling force and surface roughness can be obtained through linear regression method, which provided theoretical base for the prediction. The influences on cutting force and surface roughness of different parameters were studied to further optimization, which can provide technical support for high efficiency and precision cutting in cold-welding area.

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33

Das, Suman Kalyan, and Prasanta Sahoo. "Roughness Optimization of Electroless Ni-B Coatings Using Taguchi Method." International Journal of Manufacturing, Materials, and Mechanical Engineering 1, no. 3 (July 2011): 53–71. http://dx.doi.org/10.4018/ijmmme.2011070105.

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In this paper, the authors present an experimental study of roughness characteristics of electroless Ni-B coatings and optimization of the coating process parameters based on L27 Taguchi orthogonal design. Three coating process parameters are considered viz. bath temperature, reducing agent concentration, and nickel source concentration. It is observed that concentration of reducing agent together with bath temperature play a vital role in controlling the roughness characteristics of the coatings. The analysis yields the optimum coating parameter combination for minimum roughness. A reduction of about 15% is observed in roughness at the optimal condition compared to the initial condition. The microstructure, composition, and the phase content of the coating are also studied with the help of scanning electron microscopes energy dispersive X-ray analysis, and X-ray diffraction analysis, respectively.

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EKŞİ, Seçil, and Çetin KARAKAYA. "Alüminyum 6013-T6 Alaşımlarının Tornalama İşlemlerinde Yüzey Pürüzlülüğünün Optimizasyonu." Konya Journal of Engineering Sciences 10, no. 2 (June 1, 2022): 337–45. http://dx.doi.org/10.36306/konjes.1064663.

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One of the most common methods of machining is turning. Cutting speed, depth of cut, and feed rate are the most effective cutting parameters on the surface roughness. In addition to cutting parameters, the use of cooling type, the cutting tool is also essential on the surface roughness of materials. In this study, the surface roughness properties of Al 6013-T6 material were investigated depending on feed rate and cutting speed in turning process. Experiments were planned according to L9 orthogonal array. Optimum conditions were found via Taguchi’s Signal/Noise analysis. Variance analysis (ANOVA) was performed to determine the parameters that affect the turning process. As a result of experimental studies surface roughness values increased as feed rate increased and decreased as cutting speed increased. The analysis results showed that feed rate is a dominant parameter on surface roughness. It was also observed that the cutting parameters had a significant effect on the machining time. As the machining time decreases, the surface roughness increases.

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35

Zhang, H. Z., Qing Long An, Yun Shan Zhang, Gang Liu, and Ming Chen. "Optimization of Surface Roughness by Uniform Design of Experiments in Milling of 1Cr18Ni9Ti." Advanced Materials Research 69-70 (May 2009): 490–94. http://dx.doi.org/10.4028/www.scientific.net/amr.69-70.490.

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This paper presents the optimization process of a surface roughness model for the milling 1Cr18Ni9Ti. The model is developed in term of milling speed, feed per tooth and radial depth of cut. Therefore, the regression model predicting formula for surface roughness has been established by means of uniform design of experiment, and then the response surface methodology was applied to generate response contours of surface roughness. The experimental results indicate that the material removal rate can be improved by selecting optimal milling parameters without increasing the surface roughness. Moreover, it is seen that the feed rate is the most significant factor on the surface roughness.

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Wu, Tian-Yau, and Chi-Chen Lin. "Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints." Applied Sciences 11, no. 5 (February 28, 2021): 2137. http://dx.doi.org/10.3390/app11052137.

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The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.

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37

Jianwei, Ji, Khan Muhammad Ajmal, Zhan Zejin, Yi Rong, and Deng Hui. "Electrochemical Polishing of Tungsten: An Investigation of Critical Spatial Frequency and Ultimate Roughness." Journal of The Electrochemical Society 169, no. 4 (April 1, 2022): 043509. http://dx.doi.org/10.1149/1945-7111/ac63fa.

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Electrochemical polishing (ECP) offers incomparable advantages and great potential in metal polishing by surface errors correction. This paper systematically investigates the ultimate roughness and surface errors correction ability of ECP over different spatial frequency ranges. This paper further explores the law of ECP influencing errors at different frequency ranges, proposes and clarifies the concept of critical spatial frequency, and studies the law of polishing parameters affecting critical spatial frequency by using spatial frequency spectrum analysis. The surface roughness evolution and ultimate roughness of ECP were investigated using the surface error filtering method based on the critical spatial frequency. The ultimate roughness of ECP was determined by two different strategies, (i) stepwise polishing and (ii) one-step polishing. In addition, the stepwise polishing was also investigated for any possible inconsistency with one-step polishing on the final surface roughness. As ECP progressed, the optimization speed of surface roughness gradually decreased, and the surface roughness eventually reached a stable limiting value. Further analysis revealed that crystal corrosion is mainly responsible for inhibiting surface roughness optimization.

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38

Haleel, Aseel Jameel. "Optimization Drilling Parameters of Aluminum Alloy Based on Taguchi Method." Al-Khwarizmi Engineering Journal 14, no. 2 (March 12, 2019): 14–21. http://dx.doi.org/10.22153/kej.2018.12.001.

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This paper focuses on the optimization of drilling parameters by utilizing “Taguchi method” to obtain the minimum surface roughness. Nine drilling experiments were performed on Al 5050 alloy using high speed steel twist drills. Three drilling parameters (feed rates, cutting speeds, and cutting tools) were used as control factors, and L9 (33) “orthogonal array” was specified for the experimental trials. Signal to Noise (S/N) Ratio and “Analysis of Variance” (ANOVA) were utilized to set the optimum control factors which minimized the surface roughness. The results were tested with the aid of statistical software package MINITAB-17. After the experimental trails, the tool diameter was found as the most important factor that has effect on the surface roughness. The optimal drilling factors that minimized the surface roughness are (20mm/min cutting speed, 0.2 mm/rev feed rate, and 10mm tool diameter).

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39

Chen, Wen-Jong, Chuan-Kuei Huang, Qi-Zheng Yang, and Yin-Liang Yang. "OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM." Transactions of the Canadian Society for Mechanical Engineering 40, no. 5 (December 2016): 883–95. http://dx.doi.org/10.1139/tcsme-2016-0072.

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This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

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40

Łętocha, Aneta, Tatiana Miller, and Janusz Kalisz. "Optimization of measurement and analysis parameters of burnishing surfaces." Mechanik 90, no. 11 (November 13, 2017): 1030–34. http://dx.doi.org/10.17814/mechanik.2017.11.171.

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Studies related to the optimization of roughness measurements for surface topography obtained by milling and subsequent burnishing of hardened aluminum alloys, are presented. The measurements were made using the TOPO 01 contact profilometer. The best measurement parameters were selected. Additional measurements were also made with selected configuration, geometric surface analysis, and profile roughness statistics.

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41

Song, Yu Mei, Jie Yu, Ning Ding, and Xiang Hui Yu. "Optimal Control of Curved Surface Roughness in NC Milling Based on CAXA Finishing Methods." Advanced Materials Research 631-632 (January 2013): 1129–31. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1129.

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Through CAXA finishing methods, based on the geometric factors of the formation of surface roughness, this article proposes optimization control strategies of curved surface roughness. The study was focused on the optimal selection in curved surface processing methods, row spacing, milling cutters, feed and cutting speed. The experiment verified that the developed optimization control strategies were feasible.

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42

Zhang, Jian Min, Chu Wang Su, Jing Da Huang, Yi Ren, and Ze Kun Wang. "Optimization of Sanding Parameters for Surface of Pyinkado Plates." Applied Mechanics and Materials 174-177 (May 2012): 175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.175.

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Abstract: In order to analyze the relation between surface roughness of Pyinkado plates and sanding parameters, an orthogonal experiment and a single factor experiment were performed. The results show that the mesh of the sandpaper has remarkable influence on surface roughness of Pyinkado plates, but the feeding speed and sanding thickness don’t; when the mesh of the sandpaper is 150, the feeding speed is 6 m/min and sanding thickness is 0..4 mm during the first sanding and the mesh of the sandpaper is 240, the feeding speed is 9m/min and sanding thickness is 0.15mm during the second sanding, the smallest roughness (Ra) was obtained as 2.81μm. Considering production efficiency, changing feeding speed into 9 m/min in the theory optimization scheme, engineering optimization scheme was obtained, and the wood surface roughness (Ra) is 2.91μm.

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43

Uchiyama, Ryota, Yuki Inoue, Fumihiro Uchiyama, and Takashi Matsumura. "Optimization in Milling of Polymer Materials for High Quality Surfaces." International Journal of Automation Technology 15, no. 4 (July 5, 2021): 512–20. http://dx.doi.org/10.20965/ijat.2021.p0512.

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High quality surfaces with transparency are required for manufacturing of plastic products. In cutting of polymer materials, surface quality is sometimes deteriorated by tarnish and/or unequal spaces of area on a surface. The cutting parameters should be determined through understanding of surface finish characteristics. This paper presents an optimization approach in milling of polycarbonate with polycrystal diamond tools in terms of the surface finish. Surfaces are finished with changing the feed rate and the clearance angle of the tool. The surface finishes, then, were observed to classify the deterioration type into welding, adhesion, and the unequal space of cutter marks with measurement of the surface profiles. The measured surface roughnesses are decomposed into the theoretical/geometrical term and the irregular term induced by the thermal and the dynamic effects. A map is presented to characterize the irregular term for the feed rates and the clearance angles. Because the surface roughnesses are measured at discrete sets of the cutting parameters in the actual cutting tests, the process design cannot be conducted to optimize the operation parameters. Therefore, a neural network is applied to associate the cutting parameters with the irregular term in the map. An approach is presented to determine the number of hidden nodes/units in the design of the neural network. Three prominent areas of welding, adhesion, and unequal spaces of the cutter marks, appear in the map of irregular roughness. The map of the surface roughness is made to optimize the cutting process. The applicable feed rates and clearance angles are determined for the tolerable surface roughnesses. The gradient information in the map is used to evaluate the stability/robustness of the surface quality for changing the parameters. The optimum parameters were determined to minimize the gradient information in the applicable feed rates and clearance angles.

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44

Mărguță, Daniel, Ramona-Iuliana Popa, Eugen Herghelegiu, and Constantin Cărăușu. "TECHNICAL OPTIMIZATION OF WATER JET CUTTING OF BIODEGRADABLE MATERIALS." International Journal of Manufacturing Economics and Management 2, no. 1 (June 20, 2022): 23–34. http://dx.doi.org/10.54684/ijmem.2022.2.1.23.

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The main purpose of the technical optimization is to determine the optimal values of the processing parameters in order to increase the processing performance or decrease the processing time. Abrasive material water jet cutting is a processing process whose applicability is increasing in the conditions of the appearance of high-performance equipment. The technical optimization of this machining process aims at determining the distance between the machined material and the cutting head, determining the optimum length of the focusing tube, establishing the optimum machining pressure and determining the optimum amount of abrasive material so as to ensure maximum penetration depth of water jet with abrasive or minimizing surface roughness. During the research, the part subjected to abrasive water jet cutting was obtained by injection from Arboblend V2 Nature. The experiments were carried out according to a complete factorial plan 23, where the parameters on two levels were: water jet pressure, cutting speed and abrasive material flow. The optimization criterion followed was to minimize the standard roughness Ra. The experimental results showed that the parameter flow rate of abrasive material has the greatest influence on the roughness, the highest values of roughness are obtained when using a larger amount of abrasive (300g / min). The lowest value of the roughness of the cut surfaces is obtained for the following process parameters: low water pressure - 100MPa, high cutting speed - 150 mm / min and high flow of abrasive material - 300g / min.

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45

Deng, Yong, Zhongfa Mao, Nan Yang, Xiaodong Niu, and Xiangdong Lu. "Collaborative Optimization of Density and Surface Roughness of 316L Stainless Steel in Selective Laser Melting." Materials 13, no. 7 (April 1, 2020): 1601. http://dx.doi.org/10.3390/ma13071601.

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Although the concept of additive manufacturing has been proposed for several decades, momentum in the area of selective laser melting (SLM) is finally starting to build. In SLM, density and surface roughness, as the important quality indexes of SLMed parts, are dependent on the processing parameters. However, there are few studies on their collaborative optimization during SLM to obtain high relative density and low surface roughness simultaneously in the literature. In this work, the response surface method was adopted to study the influences of different processing parameters (laser power, scanning speed and hatch space) on density and surface roughness of 316L stainless steel parts fabricated by SLM. A statistical relationship model between processing parameters and manufacturing quality is established. A multi-objective collaborative optimization strategy considering both density and surface roughness is proposed. The experimental results show that the main effects of processing parameters on the density and surface roughness are similar. We observed that the laser power and scanning speed significantly affected the above objective quality, but the influence of the hatch spacing was comparatively low. Based on the above optimization, 316L stainless steel parts with excellent surface roughness and relative density can be obtained by SLM with optimized processing parameters.

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46

Khan, Md Jafar. "Optimization of Surface Roughness in Turning Operation using Al- 6063 and Effect of Dry Machining." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4210–15. http://dx.doi.org/10.22214/ijraset.2021.35993.

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ALUMINIUM-6063 is a widely used alloy material in the production of aerospace, aircraft, gas turbine components. This investigation focuses on the influence of machining parameters, viz., spindle speed, depth of cut and feed rate on the surface roughness obtained in Lathe operation of Al 6063 alloy. In the present study, experiments are conducted for nine different Al 6063 work piece materials to see the effect of work piece material variation in this respect. This roughness parameters, viz., Centre line average roughness, root mean square roughness. The roughness models as well as the significance of the machining parameters have been validated with analysis of variance. In addition, a good agreement between the predicted and measured surface roughness was observed. Therefore, the developed model can be effectively used to predict the surface roughness on the machining of Al6063 within 95% confidence intervals ranges of parameters studied.

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47

Meylan, Bastian, Ivan Calderon, and Kilian Wasmer. "Optimization of Process Parameters for the Laser Polishing of Hardened Tool Steel." Materials 15, no. 21 (November 3, 2022): 7746. http://dx.doi.org/10.3390/ma15217746.

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In mold making, the mold surface roughness directly affects the surface roughness of the produced part. To achieve surface roughness below 0.8 μm, the cost of surface finish is high and time-consuming. One alternative to the different grinding and polishing steps is laser polishing (LP). This study investigates and models the LP of tool steel (X38CrMoV5-1-DIN 1.2343), typical for the mold industry, having an initial rough surface obtained by electrical discharge machining. The microstructures of the re-melted layer and heat-affected zone due to the LP process were also studied. Four parameters: the laser spot size, velocity, maximum melt pool temperature and overlapping were investigated via a design of experiments (DoE) approach, specifically a factorial design. The responses were line roughness (Ra), surface roughness (Sa), and waviness (Wa). The surface topography was measured before and after the LP process by white light profilometer or confocal microscopy. DoE results showed that the selected factors interact in a complex manner, including the interactions, and depend on the responses. The DoE analysis of the results revealed that the roughness is mainly affected by the velocity, temperature and overlap. Based on a first DoE model, an optimization of the parameters was performed and allowed to find optimum parameters for the LP of the rough samples. The optimum conditions to minimize the roughness are a spot size of 0.9 mm, a velocity of 50 mm/s, a temperature of 2080 °C and an overlap of 90%. By using these parameters, the roughness could be reduced by a factor of almost 8 from 3.8 µm to approximately 0.5 µm. Observations of the microstructure reveal that the re-melted layer consists of columnar grains of residual austenite. This can be explained by the carbon intake of the electro-machined surface that helps stabilize the austenitic phase.

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48

Su, Chu Wang, Jing Da Huang, Jian Ju Luo, Lian Lai, and Yuan Yi Wuang. "Optimization of Sanding Parameters for Wood Surface of Plantation-Mytilaria laosensis." Advanced Materials Research 538-541 (June 2012): 1360–64. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.1360.

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In order to analyze the relation between wood surface roughness of plantation-Mytilaria laosensis and sanding parameters, two orthogonal experiment were performed. The results show that the mesh of the sandpaper has remarkable influence on wood surface roughness of plantation-Mytilaria laosensis,but the feeding speed and sanding thickness don’t; when the mesh of the sandpaper is 150, the feeding speed is 7 m/min and sanding thickness is 0.6 mm during the first sanding and the mesh of the sandpaper is 240, the feeding speed is 8m/min and sanding thickness is 0.15mm during the second sanding, the smallest roughness (Ra) was obtained as 2.79μm. Considering production efficiency, changing feeding speed into 9 m/min in the theory optimization scheme, engineering optimization scheme was obtained, and the wood surface roughness (Ra) is 2.80μm.

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49

Zahedi, Ali, Bahman Azarhoushang, and Javad Akbari. "Optimization and Application of Laser-Dressed cBN Grinding Wheels." Advanced Materials Research 1136 (January 2016): 90–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1136.90.

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Laser-dressing has been shown to be a promising method for overcoming some shortcomings of the conventional methods such as high wear of the dressing tool and its environmental concerns, high induced damage to the grinding wheel, low form flexibility and low speed. In this study, a resin bonded cBN grinding wheel has been dressed with a picosecond Yb:YAG laser. The efficiency of the laser-dressed grinding wheels has been compared with the conventionally dressed and sharpened grinding wheels through execution of cylindrical grinding tests on a steel workpiece (100Cr6). The conventional dressing and sharpening processes have been performed by using a vitrified SiC wheel and vitrified alumina blocks, respectively. By recording the spindle power values along with the surface topography measurements of the ground workpieces and the extraction of two roughness parameters (the average roughness Ra and the average roughness depth Rz), it is possible to provide an assessment of the cylindrical grinding process with different dressing conditions i.e. laser-dressing and conventional dressing. Accordingly, a strategy will be proposed to optimize the cylindrical grinding process with laser-dressed wheels regarding the forces and roughness values.

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50

Xu, Jin, Fuwu Yan, Yan Li, Zhenchao Yang, and Long Li. "Multiobjective Optimization of Milling Parameters for Ultrahigh-Strength Steel AF1410 Based on the NSGA-II Method." Advances in Materials Science and Engineering 2020 (July 27, 2020): 1–11. http://dx.doi.org/10.1155/2020/8796738.

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In this paper, ultrahigh-strength steel AF1410 was milled with the carbide tool, and a total of thirty experiments were performed based on central composite design (CCD) of response surface methodology. The prediction models of milling force and surface roughness are established, respectively. The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. The surface roughness, cutting force, and material removal rate are important indexes to measure the energy consumed in the process of product, the surface machining quality, and machining efficiency of processing, respectively. In order to minimize milling force and surface roughness and maximize material removal rate, NSGA-II was used for multiobjective optimization to obtain the optimal fitness value of the objective function. The NSGA-II has been applied to obtain a set of optimal combination of parameters from the Pareto-optimal solution set to enhance the machining conditions.

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