Effect of Laser Parameters on Surface Texture of Polyformaldehyde and Parameter Optimization

This research aimed to investigate the influence of laser process parameters on the surface texture of Polyformaldehyde (POM) and to improve its processability and process predictability. A comparative experiment and analysis involving multiple processing parameters, including laser power, scanning speed, and pulse width, were conducted on POM. Statistical prediction models of laser processing POM were established among the laser power, scanning speed, pulse width, texture depth, surface roughness at the bottom of texture, and multi-objective optimization and experimental verification of process parameters were carried out based on the grey-Taguchi analysis method. Experimental results show that the laser power and scanning speed significantly affect the texture depth. Higher laser power and lower scanning speed are conducive to forming depth. The surface roughness at the bottom of the texture increases with the increase in scanning speed and shows a tendency to rise and then fall as the laser power increases. The surface roughness and texture depth obtained under the optimal process parameters(A 5 B 1 C 1 ) are 1.373 μm and 466.891 μm, respectively, which were reduced by 10.08 % and increased by 3.42 % compared with the minimum surface roughness and maximum depth in the orthogonal experiments. The validation experiments of the prediction model show that it can meet the reliability requirements, and the errors of the predicted values of depth and surface roughness are 1.86 % and 7.60 %, respectively. The above research provides theoretical and experimental support for the precise control of surface texture prepared by laser processing POM.


INTRODUCTION
With the development of synthesis and processing technology for polymer materials, the use and scale of various polymers are growing rapidly [1] and [2].As one of the most commonly used polymers today, POM has become a suitable substitute for traditional materials in many application fields (gears, seals, bearings, etc. [3]) due to its high stiffness, hardness, elastic modulus, excellent wear resistance, and low weight [4] to [6].With the development of technology and industry, relying solely on the performance of the material itself has gradually failed to meet the requirements of surface performance.The method of enhancing surface performance by processing micro-texture on the material surface has been widely studied and applied [7] to [10].Many techniques for fabricating surface structures have been developed, such as etching technologies [11], atomic layer deposition [12], abrasive jet processing [13], and micro-casting [14].However, these methods are generally costly, and it is difficult to prepare micronanostructures on the material surface, which do not meet the current low-carbon processing concept and high-precision processing requirements.
In recent years, lasers have proven to be a powerful tool for the precise processing of structures at the micron and nanoscale.The unique feature of laser processing is that it can modify materials in multiple length dimensions and produce complex micro-and nanostructures [15] to [17], resulting in better surface properties.Most of the existing research focuses on laser processing of metals [18] and [19] and ceramics [20] and [21], with less emphasis on polymers [22].The majority of research aims to improve surface performance by altering texture types while studying the geometric quality of the texture itself, and the effect of laser process parameters on the texture quality has become a hot topic in recent years.Zhao et al. [23] systematically investigated the effect of repetition frequency on hole size morphology and described the relationship between repetition frequency and micropore size (such as diameter and depth) during picosecond laser ablation of metallic materials.Deepu et al. [24] investigated the effect of laser fluence, pulse overlap and pulse repetition rate on hole diameter, depth, and other factors.It was found that the best hole geometry could be obtained with a fluence of 0.44 J/cm, 10 kHz repetition rate and 85 % pulse overlap.
At present, the lack of research on process parameters is the key reason for the inability to consistently obtain surface textures with excellent structure and significant functionality.The interaction and coupling between various process parameters also make it difficult to establish a feasible and complete theoretical model.Therefore, to achieve precise control of laser processing and obtain better quality texture, it is crucial to establish a predictive model between process parameters and texture geometric features, as well as to optimize the process parameters.Some scholars have studied the relationship between the geometric characteristics of material surface texture and laser process parameters using various analytical methods.Lian et al. [25] established a predictive model for the relationship between laser process parameters and the width of the clad layer, surface roughness, and dilution rate.They analysed the impact of process parameters on various response variables, and experimental results showed that the predictive model had good accuracy.Cui et al. [26] established regression prediction models between process parameters and coating width, coating height, coating depth, aspect ratio and dilution rate.The average error between the predicted values of each regression prediction model and the experimentally measured values is less than 10 %.
To improve the surface performance of POM materials, it is necessary to achieve precise control of the surface texture of POM.In this paper, the elliptical texture is processed on the surface of POM by picosecond laser, and the influence of process parameters (laser power, scanning speed, pulse width) on the texture characteristics (depth, surface roughness) is investigated, and the relationship between process parameters and texture characteristic morphology is determined.The effect of process parameters on texture characteristics and the interaction between the process parameters were analysed using the grey-Taguchi method, and the optimal combination of process parameters was obtained for the elliptical texture.Also, a prediction model of the geometric characteristics of the surface texture of POM was established, which provides an experimental and theoretical basis for further optimization of ultrafast laser processing of POM materials.

Materials and Equipment
The experiment sample is M90-type POM (YUNTIANHUA CO., Ltd., Yunnan, China) with the specification of 25 mm × 25 mm × 10 mm (length×width×thickness).The samples were ultrasonically cleaned in anhydrous ethanol before and after laser processing, then naturally dried and placed on a two-dimensional moving table, which was computer-controlled for precise movement along x and y directions.The laser processing system used in this experiment is shown in Fig. 1a, where the laser is an infrared ultrashort pulse picosecond laser developed and produced by LinitiaLase, and the main technical parameters of the laser are shown in Table 1.The 3D measuring laser microscope (LEXT a) b) Fig. 1.Experimental instruments; a) laser processing system, and b) 3D laser scanning microscope OLS5000, Olympus) shown in Fig. 1b enables fast and efficient accurate sub-micron 3D measurements and surface roughness measurements.It was used to observe the texture morphology and measure texture depth and surface roughness.1.2 Experimental Design

Univariate Experiments
To study the effect of laser parameters on the geometry of the POM ellipse (long axis 1.5 mm × short axis 1 mm) texture, the controlled variable method was used to explore the effect of laser power, scanning speed and pulse width on the depth of texture and surface roughness.Table 2 displays the specific experimental parameters used, while Table 3 outlines the laser parameters that were not discussed in this study.
During the laser processing, the POM workpiece is placed on a high-precision motion platform, and the pulse laser with high repetition frequency and high energy density is applied to the surface of the workpiece using different laser processing parameters [26], which is radiated on the surface of the specimen according to the set scanning path and causes it to melt and vaporize [27], thus processing the elliptical texture with a certain depth.The ultrasonic cleaning device was used to clean the processed samples.After natural air drying, the elliptical texture was observed and calculated with a 3D measuring laser microscope on the surface of the POM.To ensure the accuracy of the surface roughness measurement on the bottom surface of the texture, the overall measurement of the centre area of the elliptical texture bottom surface was performed using a 20× magnification, as shown in

Orthogonal Experimental Design
The Taguchi method is a local optimization algorithm based on an orthogonal experiment and signal-tonoise ratio (S/N) designed by Genichi Taguchi [28].

Orthogonal table design experiments can reasonably reduce the number of experiments and blindness.
Statistical analysis of the experimental results with the S/N as a measure of quality characteristics can lead to the best combination of reliable and stable process parameters, achieving a combination of high efficiency and low cost.This article takes the depth of texture and surface roughness as the response targets, and the three process parameters of laser power, scanning speed, and pulse width are the influencing factors.The levels of the three process parameters are shown in Table 4. Based on the Taguchi experiment principle, the L 25 (5 3 ) orthogonal experiment table with five levels and three factors is designed as shown in Table 5, where A, B, and C represent laser power, scanning speed, and pulse width, respectively.After the corresponding data were measured experimentally, the influencing factors of each response target were explored independently, and then the multi-mass target optimization problem was transformed into a singleobjective optimization problem using grey correlation analysis to obtain the degree of influence [29], trend, and the best process parameter combination of the three process parameters on the response target.The optimal process parameters were also used for laser processing to verify the effectiveness of process parameter optimization, and the accuracy of the model was verified by substituting it into the established prediction model for the solution.The measured data from the orthogonal experiments are shown in Table 6.

Influence of Laser Power on Texture
To investigate the effect of laser power on the geometry of the texture, the surface of the POM sample was irradiated with lasers at power levels within the range of 19.5 W to 30 W under constant scanning speeds of 600 mm/s and pulse widths of 12 μs.The variation of the relevant parameters is shown in Fig. 3.With the gradual increase in laser power, the depth of elliptical texture shows an increasing trend, increasing from 160.709 μm to 302.197 μm.
The increase in laser power means that the energy of individual laser pulses gradually increases, the energy density acting on the surface of POM increases, and when the laser energy density is higher than the ablation threshold of POM, the absorption of laser energy by POM produces ionization.The process of ionization becomes significant with increasing laser power, resulting in an increase in the volume of material removed and a greater depth of texture.Laser radiation to the site, with the increase in depth, began to move away from the original focal surface, and the laser energy to reach the bottom of the texture gradually decreased.Coupled with the gradual increase in the depth of the texture, the formation of molten spatter takes longer to fly out of the texture, as well as the laser processing process generated by the plasma on the laser shielding effect.As a result, the depth of the texture will increase with the laser power and tend to level off.Meanwhile, it can be seen from Fig. 3 that the surface roughness of the elliptical texture bottom increases with the increase in laser power, but when the power reaches 24 W, the surface roughness reaches the maximum and continues to increase the power afterwards, the surface roughness starts to decrease instead.As shown in Fig. 4, the structure of the bottom surface of the texture structure is relatively regular when the laser power is small (19.5 W to 24 W), and most of the bottom surface is formed by the uniform cooling of the melt that fails to vaporize in time.However, as the laser power gradually increases, the melt vaporized on each laser path increases, and the melt that did not vaporize at this time could not effectively lap to form a good surface, thus making the overall surface roughness gradually increase.Also, when the laser power is higher (25.5 W to 30 W), the macrostructure of the texture bottom surface is poorer, but its overall surface roughness starts to decrease compared to the power of 24 W.This phenomenon is caused as follows: the melt vaporized on the laser sweeping path is more than the non-vaporized melt, and the non-vaporized melt becomes a single individual after cooling, thus forming a structure of multiple crystalline particles on a smooth surface on the bottom surface, at which time the overall surface roughness of the bottom surface will gradually decrease as the smooth surface area increases and the number and volume of crystalline particles decrease.To further investigate the influence of laser scanning speed on texture, in a fixed other parameter situation, different laser scanning speeds (ranging from 450 mm/s to 800 mm/s with an increment of 50 mm/s) were employed for texture processing.The variations in texture-related parameters are shown in Fig. 5. From Fig. 5, it can be seen that the depth decreases with the increase in scanning speed, with a relatively stable depth variation within the range of 600 mm/s to 650 mm/s.The scanning speed affects the action time of the laser and the material surface.With the increase of the scanning speed, the laser irradiation time per unit area becomes shorter, and the number of times radiated by the pulsed laser becomes less, the energy absorbed by the material surface decreases, and the material removal rate decreases, resulting in a decrease in the depth of the elliptical texture.Additionally, Fig. 5 indicates that the surface roughness of the bottom surface of the elliptical texture increases with increasing scanning speed, and the increase is gradually increasing.Combined with Fig. 6, it is found that the molten structure formed by laser ablation of POM gradually decreases with increasing scanning speed, and although the decrease of melt-like structure is beneficial to the macroscopic morphology, it has a negative effect on the surface roughness.As a common polymer, the crystallization properties of POM materials are positively correlated with heat treatment time and temperature in a specific temperature range [30].As the scanning speed increases, the time for the texture to be radiated by the laser becomes shorter, the conduction and diffusion processes of energy on the material are shortened, the heat treatment time is shorter, and the crystallinity of the bottom surface of the texture is poor, which leads to the rise of its overall surface roughness.

Influence of Pulse Width on Texture
Pulse width is an important parameter in laser processing, which represents the duration of laser pulse energy output.Under the condition of unchanged pulse energy, increasing the pulse width will extend the period during which each laser pulse acts on the material surface, consequently elevating the total heat absorbed by the material and expanding the thermally affected zone of the processing site.As can be seen from Fig. 7, the depth of the elliptical texture becomes more significant with increasing pulse width, and the depth of the texture increases from 215.591 μm to 224.512 μm when the pulse width increases from 4 μs to 44 μs, but the overall change is not significant.A comparison with Figs. 3 and 5 shows that the effect of pulse width on depth is much less than that of power and scanning speed.Fig. 7 also shows that the surface roughness of the texture's bottom surface fluctuates between 2.65 μm and 2.95 μm with the variation of pulse width, which is relatively small compared with the influences of power and scanning speed.During the process from a pulse width of 4 μs to 44 μs, the overall surface roughness exhibits a continuous decreasing trend.The rate of decrease is faster when the pulse width increases from 4 μs to 20 μs, while it becomes more moderate when the pulse width increases from 20 μs to 44 μs.From Fig. 8, it can be observed that the morphology of the texture bottom surface does not undergo significant changes with increasing pulse width.Typically, larger pulse widths imply an extended duration of individual laser pulses and longer conduction times for laser energy inside the material, resulting in increased amounts of melt and corresponding changes in surface morphology.However, for a material like POM, variations in pulse width at the microsecond level have less effect on the melting process.

Model Building
Based on the data results obtained from the orthogonal experiment, the quadratic regression polynomials between laser power p, scanning speed v, pulse width w, depth D, and surface roughness Ra were constructed by Minitab.The obtained expressions are shown in Eqs.(1) and (2).According to the variance analysis results of depth shown in Table 7, the model coefficient P of the depth prediction model is much less than 0.05, indicating the high significance of the model at a 95 % confidence level.The multiple coefficients of determination R 2 value is 0.9957, and the adjusted R 2 value is 0.9945, both of which are close to 1, indicating the reliable predictive power of the model.Fig. 9a shows the residual normal plot of the depth regression model, from which it can be seen that the data points are evenly distributed around the fitted line with no abnormal data points, indicating a good fit for the regression model.In addition, the effect of pulse width on depth is not significant enough to exceed the 95 % confidence level, and variance analysis has automatically eliminated it.Similarly, as shown in Table 8 and Fig. 9b, the model coefficient P is less than 0.001, the P values of linear, square, and interaction terms are all less than 0.05, the multivariate coefficient R 2 is 0.9319, the adjusted R 2 is 0.9091, and the distribution of data points in the residual normal plot is reasonable, indicating that the prediction model has high overall reliability and fit.

Parameter Optimization
The Taguchi method evaluates the results of orthogonal experiments using the S/N ratio, which typically can be classified into three categories based on the research objectives: larger-the-better (LTB), smaller-the-better (STB), and nominal-the-best (NTB) [31].In this study, the depth and surface roughness of the elliptical texture are chosen as the response variables.The objective was to achieve a large depth while maintaining a low surface roughness at a certain number of processing cycles.Therefore, the LTB criterion was used for the depth target, while the STB criterion was applied for the surface roughness target in calculating the S/N, and the magnitude of the influence of each process parameter on the target was sought using the range analysis.The specific results and data are shown in Tables 9 to 11, and the S/N ratio calculation formula is shown in Eq. ( 3  The degree of influence of each parameter on the texture depth at different levels can be seen in Tables 9 and 11.Due to the LTB characteristic of depth, when the combination of laser power (A), scanning speed (B), and pulse width (C) is set to A 5 B 1 C 3 (laser power correlation values obtained by the three steps are shown in Table 12. where () is the value of the response quantity, is the number of experiments.

X k y k y k y k y k LTB y k y k
where X i (k) is the normalized value of the k th response quantity for the i th experiment; y i (k) is the value of the SNR for the i th experiment of the k th response quantity; max y i (k) and min y i (k) represent the maximum and minimum values of the k th response volume in the 25 sets of experiments, respectively.
where GRC i (k) is the value of the grey correlation coefficient of the i th experiment of the k th response; x i (k) is the normalized value of the k th response quantity for the i th experiment; x i 0 is the ideal value for the experiment, which is set to 1 in this study; ξ is the distinguishing coefficient which is defined in the range of 0 ≤ ξ ≤ 1, ξ was taken as 0.5 in this study.
where GRC i is the grey correlation value of the i th experiment; GRC i (k) is the value of the grey of 30 W, scanning speed of 400 mm/s, pulse width of 20 μs), this combination represents the optimal process parameter combination for controlling texture depth.Additionally, based on the extreme difference, the impact of each parameter on depth can be ranked in descending order as follows: laser power > scanning speed > pulse width.Similarly, the optimal combination of process parameters for controlling the surface roughness of the texture bottom surface is A 1 B 2 C 1 (laser power of 18 W, scanning speed of 500 mm/s, pulse width of 4 μs).The impact of each parameter on surface roughness can be ranked in descending order as follows: scanning speed > laser power > pulse width.
Although the optimal parameters of the two responses are obtained respectively, the optimal parameters of one response quantity could not take into account the other response quantity due to the different influence of each process parameter on both.To obtain the best combination of process parameters with two response quantities, the grey correlation method is used.The grey correlation method is a method to determine the significance of the relationship between factors in the order of grey correlation.The combination of grey relational analysis theory with the Taguchi method provides an effective solution for complex multi-response problems.By transforming multiple laser process parameter optimization problems into optimizing a single grey relational degree problem, the optimal combination of parameters can be obtained to achieve minimal surface roughness and maximum depth.The grey correlation analysis requires a series of calculations on the signal-to-noise data: S/N normalization, calculation of the grey correlation coefficient for each response quantity, and calculation of the grey correlation.The formulas used in the three steps are shown in Eqs. ( 4) to (6), and the final grey correlation coefficient of the i th experiment of the k th response quantity; n is the number of response quantities and is 2 in this study.According to grey relational analysis theory, the degree of correlation between factors and the system depends on the value of the correlation degree.The larger the correlation deegree value of a process parameter, the greater its response in optimizing the objective function.Fig. 10 shows the main effect plot of the grey correlation of each process parameter, and it can be seen that the optimal process parameter is A 5 B 1 C 1 , i.e., the laser power is 30 W, the scanning speed is 400 mm/s, and the pulse width is 4 μs.

Validation Analysis
Since the optimal combination of process parameters (A 5 B 1 C 1 ) was not included in the orthogonal experiment, a new set of machining experiments was conducted using the optimal parameter combination.Table 13 presents the optimized process parameters and the relevant parameters of the elliptical texture.Combining grey correlation theory with the data from the orthogonal experiment, the minimum surface roughness and maximum depth were selected as references.The surface roughness obtained from the optimized parameters was 1.373 μm, which decreased by 10.08 % compared to the minimum surface roughness obtained from the orthogonal experiment (1.527 μm); the depth obtained from the optimized parameters was 466.891 μm, which increased by 3.42 % compared to the maximum depth obtained from the orthogonal experiment (451.443μm).The results demonstrate that the effect of the optimized parameters is consistent with the previous expectations, as it enables the simultaneous attainment of low surface roughness and a greater depth.It can also be seen from Fig. 11 that compared to the two texture substrates obtained through separate optimization using the Taguchi method, the substrate prepared after further optimization using the grey correlation method exhibits a greater abundance of molten-like structures.Fig. 10.Main effects plot for the SNR analysis of GRC Additionally, these molten structures are distributed in a wider and more uniform manner, which is also the reason for the further reduction in surface roughness.By substituting the optimal combination of process parameters into Eqs.( 1) and (2), the predicted values for depth and surface roughness are obtained as 475.570 μm and 1.486 μm, respectively, and the errors between these predicted values and the actual measured values are 1.86 % and 7.60 %.This shows that the predictive model exhibits high accuracy and reliability, meeting the objectives of predicting and optimizing process parameters, and it has a positive effect on the accurate control of the texture morphology and quality as well as the improvement of the efficiency of ablation.

CONCLUSION
This paper investigates the impact of laser process parameters on the surface texture characteristics of POM.The predictive model was established to correlate three laser parameters (laser power, scanning speed, and pulse width) with texture depth and surface roughness at the bottom of the texture.The grey-Taguchi method was employed for multi-objective optimization and experimental verification of the process parameters.The results indicate that with the increase of laser power and the decrease of scanning speed, the depth of the weave gradually increases, but its increase decreases with the further change of these two influencing factors.The surface roughness at the bottom of the texture decreases with increasing pulse width and exhibits a trend of initially increasing and then decreasing with increasing scanning speed, with a maximum value observed at a power of 24 W. The variation of pulse width has no significant effect on either the depth or surface roughness.Multi-objective optimization using the grey-Taguchi method resulted in obtaining the optimal combination of laser parameters as A 5 B 1 C 1 (laser power is 30 W, the scanning speed is 400 mm/s and pulse width is 4 μs).Compared with the corresponding data obtained from the orthogonal experiments, the texture depth increased by 3.42 %, and the surface roughness of the bottom surface decreased by 10.08 % under the optimal parameter combination, achieving a significant optimization of surface roughness while obtaining a larger depth.Experimental validation of the established prediction model using the optimal parameter combination showed that the predicted values of depth and surface roughness had errors of 1.86 % and 7.60 %, respectively, compared to the experimental measurements, and the model was accurate enough to achieve accurate prediction of the surface texture properties of POM.

ACKNOWLEDGEMENTS
Financial support for this work was provided by the National Natural Science Foundation of China Youth Project (Grant no.51905170) and the Natural Science Foundation of Hunan Province (Grant no.2020JJ4334).

Fig. 2 .
Measurement of a) surface roughness and b) depth

Fig. 2a .
Fig.2a.The depth is measured as shown in Fig.2b.Both values are taken as the average value after three repeated measurements.

Fig. 3 .Fig. 4 .
Fig. 3.The changes in texture depth and surface roughness under different laser power

Fig. 5 .Fig. 6 .
Fig. 5.The changes in texture depth and surface roughness under different scanning speeds

Fig. 7 .Fig. 8 .
Fig. 7.The changes of texture depth and surface roughness under different pulse width

Fig. 9 .
Fig. 9. Normal probability plot of residuals for a) depth data and b) surface roughness data

Table 1 .
Main technical parameters of ultraviolet nanosecond laser

Table 2 .
Laser processing experimental parameters

Table 3 .
Values of laser parameters not discussed

Table 4 .
Factors and their levels

Table 6 .
Results of orthogonal experiment Effect of Laser Parameters on Surface Texture of Polyformaldehyde and Parameter Optimization 2 RESULTS AND DISCUSSION 2.1 Texture Morphology Analysis ).

Table 7 .
Variance analysis table of depth

Table 8 .
Variance analysis table of surface roughness

Table 9 .
SNR of response targets

Table 10 .
The SNR mean and range of depth

Table 11 .
The SNR mean and range of surface roughness

Table 12 .
Grey relational analysis data