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 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 66, 2018 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.aidic.it/cet 

Guest Editors: Songying Zhao, Yougang Sun, Ye Zhou 
Copyright © 2018, AIDIC Servizi S.r.l. 

ISBN 978-88-95608-63-1; ISSN 2283-9216 

Study on Parameter Optimization of Cutting Surface 

Roughness of Aluminum Alloy 

Wei Zhou 

Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China 

290464667@qq.com 

This study is to develop a parameter optimization strategy for cutting surface roughness of aluminum alloy. 

The machining and manufacturing process of metal parts is deeply studied, and the influence law and 

significance of parameters and interaction on target value are analyzed comprehensively, then the 

combination value of cutting parameters of aluminum alloy is optimized to ensure that the target value doesn’t 

exceed the specified range value. Through a series of research and analysis, the quality characteristics of 

processed workpiece is understood, and the difference of cutting process between aluminum alloy and other 

metals is distinguished. Conclusion has been drawn that the research result of cutting surface roughness of 

aluminum alloy provides important reference for optimizing its parameters, so it has high application value.  

1. Introduction 

Along with the rapid development of social economy, the demand of various industries for metal parts has 

been increasing, especially for the quality of metal parts. Therefore, it is very important to optimize the 

machining parameters of metal parts. In view of the status quo of metal parts machining in China, it is found 

that in the machining and manufacturing process, numerical control programming personnel rely on personal 

previous working experience or inquiry manual in the stage of selecting machining parameters. This is one of 

the main reasons why the efficiency of metal parts machining has not been improved obviously. However, with 

the rapid development of high performance numerical control technology, the cutting parameters selected by 

traditional methods can’t meet the requirements of modern social development and can’t give full play to the 

actual machining performance of tools, so they are gradually wed out and even abandoned by the high 

performance numerical control technology industry. At the same time, with the computer technology level 

increasing, simulation software and CAM come into being, which makes many manufacturers set NC program 

for automatic target-specific optimization based on the characteristics of new software and new technology in 

order to enlarge the scale of development, and then select reasonable machining parameters. However, in the 

process of optimizing machining parameters by using related software, many manufacturers don’t have a 

comprehensive analysis and research on the actual characteristics of tools, so the set program is not practical. 

Therefore, it is very necessary to study the machining and manufacturing process. 

2. Literature review 

At present, domestic and foreign research institutes have made some achievements in the study of surface 

roughness. The regression statistics and response surface method were applied to Long Zhenhai, Wang Xibin 

and Chiang in Beijing Institute of Technology, and the influence of factors on the surface roughness of 

aluminum alloy was analyzed with factorial and uniform design, and the empirical formula of surface 

roughness was established. The Zhang Lei of Nanjing University of Science and Technology was used to 

analyze the cutting parameters by using the orthogonal test. The influence of number on the surface 

roughness in high speed milling; the influence factor of surface roughness and the optimization of cutting 

parameters were analyzed by the application of Taguchi method in Yanbian University. The cutting force and 

surface roughness of the titanium alloy in the high speed cutting process of the Shandong University were 

studied by Tian Gang and Zhang Song. The prediction model of cutting force and surface roughness based on 

                                

 
 

 

 
   

                                                  
DOI: 10.3303/CET1866035 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Please cite this article as: Zhou W., 2018, Study on parameter optimization of cutting surface roughness of aluminum alloy, Chemical 
Engineering Transactions, 66, 205-210  DOI:10.3303/CET1866035   

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neural network is used to obtain the optimal combination parameters for reducing the cutting force and 

improving the surface quality as a constraint condition. The most widely used regression analysis method is 

used in the response surface method of Zhu Dairu and Liang Chun Chun in Harbin Institute of Technology. 

The surface roughness of milling microgrooves is predicted by the method of heart compound design and the 

model is established. The purpose of improving the metal removal rate and controlling the surface roughness 

of the micro milling is achieved. Meng Fanyu and Wang Longshan of Jilin University are using the method of 

laser atlas to measure the surface roughness of the outer longitudinal grinding. The requirement of on-line 

measurement in the process of circular grinding is convenient for a series of optimization work. The Fang of 

Dalian University of Technology has predicted the surface roughness of the ceramic surface, pre-evaluated 

the surface quality after grinding and adjusted the process parameters in time, so the numerical control based 

on neural network has been achieved. The study on the on-line prediction of the surface roughness of the 

parts during the turning process has made a good theoretical paving for the realization of the real-time 

optimization of the cutting parameters which can ensure the efficiency while ensuring the quality of the 

machining surface, but there is no real actual optimization proof (Fang et al., 2016); Wang and others in 

Tianjin University studied the influence of the factors on the milling force and the surface roughness of the 

typical refractory austenitic stainless steels L Crl8Ni9 in high speed milling. However, the analysis and 

comparison of various methods were compared, but the research conclusions were not tested, but the surface 

roughness based on artificial neural network was proposed and established. The prediction model, taking 

ultra-precision turning as the research object, further studies the optimization of turning processing parameters 

based on genetic algorithm, but only studies the relationship between the surface roughness and the cutting 

amount, and the application has limitations (Wang et al., 2016). 

The effect of cutting speed, radial cutting depth, axial depth and feed per tooth on surface roughness is 

studied by Khorasani and other AA2024 aluminum alloy. It is proved that the influence of axial depth on 

surface roughness is the most, and the influence of radial cutting depth is smaller (Khorasani et al., 2016). 

Sahoo and others have studied the effect of tool radial angle and rear angle on the surface roughness of 

AA6061 aluminum alloy (Sahoo et al., 2017). Horváth and Drégelyi-Kiss use a single blade high speed cutting 

tool for surface milling of AA7075T6. The blades are made of carbide and diamond, and the influence of 

cutting tool material, feed speed, cutting speed and depth on surface roughness is studied. Finally, the cutting 

speed of the best surface quality is 1524m/min, and the bigger the cutting depth, the worse the surface quality 

(Horváth and Drégelyi-Kiss, 2015). 

Compared with the traditional full factorial test design, the response surface method can achieve more 

optimization experiments. To verify the effect of the radius of the nose, the width of the side and the cutting 

conditions on the residual stress and the surface roughness, the Santos and the response surface method 

were used to obtain the prediction model, and the variance analysis was used to verify the adequacy of the 

model and to determine the significant factors in the model. The nose radius has a significant effect on the 

surface roughness (Santos et al., 2016). Najiha and others studied the influence of cutting depth, spindle 

speed and feed rate on surface roughness by the response surface method and established the model to 

optimize the surface roughness under the prescribed constraints (Najiha et al., 2015). Prasad and others have 

studied the optimization of surface roughness of AA7075-T6 mold by combining response surface method and 

genetic algorithm. The effects of four factors on feed rate, cutting speed, axial cutting depth and radial depth of 

cut were considered, and the optimization model was established by response surface methodology (Prasad 

et al., 2015). The value of surface roughness is obtained by the third level full factor test design. Finally, the 

desired surface roughness is obtained by using genetic algorithm to optimize the cutting process. The results 

show that the genetic algorithm can improve the surface quality by about 10%. 

To sum up, the optimization method based on modern optimization algorithm will be the development trend 

and direction of cutting parameter optimization research. However, based on the development of numerical 

control technology in China and the selection of most of the machining parameters of NC machining, the 

optimization method based on the experiment is a good choice in the process of actively guiding the NC 

machining towards the optimization parameter direction. It is not too complex, and it is easy to be in the CNC 

system. In the process of integration, it is easy to popularize. This paper will also focus on theoretical research 

and system embedment based on the experimental optimization methodand provide a useful reference for 

actual production. 

3. Research Methods 

(1) The orthogonal test is used to study several factors and interaction on surface roughness. It is not limited 

to study the influence of cutting three factors. In this study, the interaction of cutting angle, angle of bank and 

cutting three factors is added, which provides a reference for numerical control programming personnel to 

select cutting parameters more scientifically and reasonably. (2) This study is no longer limited to use 

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optimization algorithm to optimize the theoretical model. It is more targeted for the actual analysis of the 

specific dynamic characteristics of tools. The small calculation amount of the optimization algorithm doesn’t 

affect the actual performance of the numerical control system, and the high memory required by other 

optimization algorithms is omitted. The general optimization parameter software can’t complete this work. This 

ensures numerical control machining to give full play to the maximum capacity of enterprises’ manufacturing 

system, improving the efficiency of enterprises’ production. (3) The research and development of cutting 

parameter optimization based on experiment has high guiding value, which can provide a good reference for 

the research and development of multi-target parameter optimization system. (4) It gets rid of the dependence 

of the optimization work on the third-party software, and the optimization of cutting parameters can be 

completed on the self-developed numerical control system. In addition, we can independently develop 

optimization objectives according to the function parameters of our own needs. In this study, the optimization 

work will be subdivided to become transparent so that readers can clarify the ideas and principles of software 

writing so as to provide ideas for the development of relevant test data analysis software. Based on the actual 

machining data obtained from the orthogonal test, the optimal cutting parameter combination is obtained by 

the Taguchi method with minimum surface roughness as the goal, and the aluminum alloy impeller with 

smaller surface roughness is machined. The optimization algorithm can guide us to select cutting parameters 

scientifically. The experimental research method proposed in this study is closely combined with the actual 

machining, and the cutting verification is carried out on the TDNC-A1M6-H8 five-axis double-rotating 

numerical control machining center, which provides reference for the selection of cutting parameters of 

complex parts. This can effectively improve the machining quality of complex surface parts and make 

contributions to the research of related technologies of high-end digital manufacturing equipment. Figure 1 

shows some complex surface parts, which are generally required to have a high surface quality. The surface 

roughness of mechanical products can affect their various functional properties, such as surface friction, wear, 

light reflection, heat conduction, lubrication, fatigue resistance. There are several ways to describe surface 

roughness, of which the most common is mean roughness value, marked as Ra which is defined as the 

arithmetic value of the contour deviating from the centerline within the measured length of the sample, as 

shown in Figure 2. X: contour direction; Y: contour curve; Z: average height; L: sample length; H: contour 

height. 

 

Figure 1: Common complicated curved surface parts (propeller, vane, impeller) 

 

Figure 2: Surface roughness contour 

4. Research Results and Discussion 

4.1 Experimental design method 

In this study, Taguchi experimental design method is used to optimize the cutting parameters: 1. parameter 

variables and levels that affect the surface roughness are selected and determined; 2. an appropriate 

orthogonal matrix is selected according to the controllable factors and the corresponding level; 3. test is 

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conducted according to the selected orthogonal matrix (the noise factor is considered or not considered); 4. 

test data is analyzed the to determine the optimization level of controllable factors; 5. test is conducted to 

verify the predicted surface roughness. Taguchi method proposes that the machining design of any product 

must include three stages: system design, parameter design and error design. 

In the system design stage, the designer uses the knowledge of science and engineering to design and 

establish the basic function original model. This stage includes the product design stage and the machining 

design stage. Product design includes selection of materials and components, and preliminary and tentative 

selection of product parameter values. Machining design includes the analysis of machining procedures and 

the selection of product machining equipment. In terms of product quality and cost, the system design doesn’t 

cover optimization work and it is only the preliminary function design. The purpose of parameter design is to 

optimize a series of machining parameters for improving the performance characteristics of the product and to 

determine the corresponding performance target value of the product. In addition, we hope that the optimized 

parameter value obtained from the parameter design is less affected by environment variables or other noise 

as much as possible. Therefore, in the Taguchi design method, parameter design is a key step in achieving 

high quality without increasing cost. The parameter design stage is the most important part. In this stage, we 

will determine the characteristic parameters to measure the product quality of the machining process, and 

most importantly, we will put forward the optimization value of the parameters to achieve the quality 

characteristics of the machining product optimization. 

4.2 Study on cutting process 

After the numerical control simulation environment is set up in VERICUT, such as establishing coordinate 

system and tool system, setting collision check and route limit, and importing numerical control program, the 

numerical control program generated by CAM software (SolidCAM) can be simulated by VERICUT to 

determine the limit value of the angle. The cutting angle and angle of bank are continuously changed and 

simulated. The simulation process for determining the angle of bank is as shown in Figure 3. The correct 

simulation machining effect is shown in Figure 4. After the factors and factor levels are determined, they are 

summarized and listed in the factor level table as shown in Table 1.  

 

Figure 3: Determination of roll angles              Figure 4: Impeller simulation machining with VERICUT 

Table 1: Factor-Level table 

factors unit 
The level of 

1 2 3 

Spindle speed S(A) r/min 1500 2500 4000 

Feed speed F(B) mm/min 1000 1500 2000 

Cutting dept ap(C) mm 0.1 0.2 0.5 

Before and after the Angle 
 

-5 0 5 

Roll Angle (E) 
 

-10 0 10 

4.3 Design of test header with interaction and preparation of test scheme 

For the test to investigate the interaction, the header design is the process of arranging the test factors and 

the interaction to be investigated into the columns of the orthogonal table. In the process of investigating the 

interaction, the interaction should be arranged in the interaction column of the selected orthogonal table and 

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can’t be arranged arbitrarily so as to prevent the “mixing” phenomenon of the design, which is an important 

characteristic and a key step of the interaction orthogonal test design. The design method based on the linear 

table method is shown in Figure 4. This chapter mainly introduces Taguchi experimental design theory and the 

three stages of Taguchi method design, the orthogonal experimental design with interaction, and the general 

flow of experimental design. Four key technologies are emphatically introduced: the determination of test 

purpose, requirement, assessment index, selection of factors, determination of level, listing of factor level 

table, the selection of orthogonal table with interaction, the design of test header and the preparation of test 

scheme. 

 

Figure 5: Design method of linear form 

4.4 Selection of machining platform 

In this study, TDNC-A1M6-H8 five-axis numerical control machining center of Tianda Jingyi Numerical Control 

Technology Development Co., Ltd. is selected for the machining test, as shown in Figure 3-2. The following 

reasons are considered: 1. the spindle speed of hardware configuration of machining center reaches up to 

8000 r/min and the speed of fast movement is 10000 mm/min, which can meet the requirements of spindle 

speed and feed speed of this test; 2. the machining center is equipped with Germany peislor double turntable 

with large torque so that the five-axis linkage and the large dip angle can test can be realized; 3. TDNC-A1M6-

H8 numerical control system is an open and embeddable frame system, making it convenient for embedding 

in optimization system automatically. According to the orthogonal test scheme table, a set of combined 

parameters is set for each flow channel in SolidCAM for carrying out the actual machining test.  

 

Figure 6: TDNC-A1M6-H8 five-axis linkage machining center. 

4.5 Measurement of surface roughness  

In this study, American NANOVEA three-dimensional non-contact surface topography measuring instrument is 

selected to measure the surface roughness of impeller passage, as shown in Figure 3-3. The main reasons 

are as follows: 1. it is suitable to measure the material surface with high gradient and high tortuosity, and the 

highest gradient is close to vertical; 2. its measurement speed is fast, up to 1 m/s; 3. its measurement 

accuracy is high and minimum resolution of 2 nm can be obtained; 4. it has professional three-dimensional 

analysis software function. For the surface sample, it first flattens and then automatically gives the surface 

roughness value aR of the sample. In order to ensure the accuracy of the measurement result, three areas are 

randomly selected for each flow passage obtained by each group of combined parameters for measurement 

and recorded in the three columns a1R, a2R and a3R of Table 3-3. Figure 7 shows three-dimensional non-

contact surface topography measurement.  

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Figure 7: Three-dimensional non-contact surface profilometer and measure area selection 

5. Conclusions 

Based on the demand analysis of the system, this study obtains the specific requirements conforming to 

enterprises’ optimization system after considering the research results of the cutting generation optimization 

theory and the machining process. The design of the optimization system can be divided into three layers: 

optimization module layer, database realization layer and human-computer interaction layer. At the same time, 

in the process of system design, the overall scheme design content of the system is preliminarily determined 

after the operation habit and level of operators around the world are objectively analyzed. On this basis, the 

key technology of the optimization system is summarized, which makes the content of the aluminum alloy 

cutting system more perfect and provides theoretical basis for the optimization of its processing generation. 

After a series of research and analysis, it is found that in order to further optimize the cutting parameters of 

aluminum alloy, it is necessary to strengthen the research on machining technique and technology of metal 

parts according to the development needs of times and idea of keeping pace with the times so as to meet the 

requirements of social development and expand the scope of optimization, as well as continuously 

supplement the content of aluminum alloy cutting machining. 

Acknowledgments 

The high level key professional construction project of Higher Vocational Education in Jiangsu Province, the 

high [2017]17of Jiangsu Education. 

Changzhou city industrial robot system integration technology key laboratory, project number "CM20153002". 

This paper from the Jiangsu colleges and universities "sponsored by QingLan Project" funded 

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