Design Optimization for the Coating of Machine Tools Based on Eye-Tracking Experiments and Virtual Reality Technology

Abstract

The coating design of machine tools refers to the exterior appearance of a machine tool and is an important method for improving the user experience and brand image. In this study, we studied and optimized the coating design of serial CNC cylindrical grinder machines of the Shanghai Machinery Factory in China using eye-tracking experiments and virtual reality technology. Firstly, based on eye-tracking technology, experiments were carried out on various elements of the coating design of the machine tools. Secondly, the experimental data were analyzed to extract design criteria that fit the user’s visual habits and to determine the details of the coating design optimization. Thirdly, the design scheme was verified using virtual reality technology and a user questionnaire survey. The results show that it provides support for the optimization of the design and working efficiency of machine tool coatings in enterprises. Future work should investigate optimization design tools that include ergonomics based on vision experiments and virtual reality.

1. Introduction

CNC (computer numerical control) machine tools are essential pieces of fundamental machinery used in the modern manufacturing industry. The coating design of machine tools is one of the visual features of machine tools, and it is also a significant component of the company’s brand image. With the continuous improvement of the technical level of modern machine tools, the market is becoming increasingly competitive.

At present, the research on the optimization design of the appearance of mechanical products has attracted extensive attention from scholars, and these studies have mostly integrated experiments into, and conducted research on, Kansei engineering, ergonomics, and product image. Xue et al. [1] proposed a comprehensive decision system based on experiments in Kansei engineering for forecasting and evaluating the optimization design of the product image. Chen et al. [2] proposed a new design method for modelling the appearance of electromechanical products based on solid pattern genes, which shortened the product development cycle while enriching the product pattern design. Liu et al. [3] modified the appearance of the CY-PTC5655 CNC lathe using ergonomic design principles and methods to increase the product production efficiency. Wang et al. [4] constructed an innovative design model of agricultural machinery products based on KE-TRIZ to innovate the design of the products’ appearance and function matching, improving the overall quality of agricultural machinery products. Zhu et al. [5] provided a method for developing new appearance designs for CNC equipment based on product identification (PI) using the DMG company as an example.

Due to the limits of the product function, size, and processing technology, it is difficult to update and improve on traditional machine tools. However, coating design, as one of the key elements of the exterior appearance of large machinery products, has the advantages of a short development cycle and high design efficiency, which can help enterprises to adapt to market changes in a short time. Most of the coating designs of machine tools depend on the designer’s subjective preference, lacking objective design specifications and effective verification methods. In this study, we use eye-tracking experiments and virtual reality technology to optimize the coating design of machine tools and extract design specifications.

2. Literature Review

At present, the research on the coating design of machine tools mainly includes studies of coating materials, coating processing, and coating applications and evaluation.

(1) The material properties of the coating are employed in conjunction with the product in order to optimize it. He et al. [6] developed a novel approach applying several PVD and CVD (coating + substrate) integrated systems for the purpose of austenitic stainless-steel machining, which increased the tool wear life performance. Sampath et al. [7] experimentally evaluated the machining performance of TiAlN-, AlCrN-, and TiAlN/AlCrN-coated carbide cutting tools and uncoated carbide cutting tools for machining Inconel 825 alloy. Cecilia et al. [8] obtained sol-gel coating materials with an enhanced corrosion protection capacity for galvanized steel by incorporating organic precursors into an epoxide-functionalized silica–zirconia matrix, which could extend the service life of the galvanized parts. Anna et al. [9] fabricated thermoplastic polymer composites using different lateral flow materials as fillers and confirmed the improvement of their mechanical properties. Yuxin et al. [10] used a new ionic co-discharge method to prepare Ag-Bi nanocomposite coatings, which significantly improved the mechanical properties of the coatings.

(2) Combined with various modern technologies, quality control research on the coating process has been carried out. Aydin et al. [11] determined the optimum CNC machining parameters for the wood surface quality through the optimization and adjustment of CNC machining parameters by ANN (artificial neural network). Filip et al. [12] proposed a new method that can be used to capture and edit the anisotropic behavior of effect coatings, allowing users to quickly explore and evaluate the visual effects of anisotropy on effect coatings in a virtual environment. Katırcı et al. [13] developed an artificial intelligence method to automatically classify coated parts, which has shown great potential in controlling the plating process parameters. Rani et al. [14] developed an Ni-Cr-40Al2O3 coating on stainless steel (SS316L) substrate by high-speed flame spraying and then used post-treatment agitated friction machining, providing an effective solution to the problem of corrosion in fluid machinery.

(3) Various measuring devices have been developed in combination with different research methods to optimize the evaluation of coating applications. Shuhei Watanabe [15] developed a measuring device that can analyze coating colors and flash values at multiple angles with a high angular resolution, which was helpful in developing the appearance of effective coatings. Walsh et al. [16] used a prototype infrared transmission microscope to assess the micron-level coating coverage on a polypropylene-based conveyor, identifying the machine breakdown by identifying artefacts in the coating. Yassin et al. [17] discussed and evaluated the imaging capabilities of PAUT (5 MHz) and near-field microwave imaging systems (33.5 GHz) for multiple artificial surface defect samples and benchmarked the images produced by the systems to avoid the machine breakdown or material defects.

As can be seen from the above study, these studies on the coating design of machine tools mainly focus on technical areas, such as the materials, processing technology, and application evaluation, while coating research related to the optimization design of the product appearance is lacking. This study combines eye-tracking experiments and virtual reality technology to further research and establish design criteria.

2.1. Eye-Tracking Experiment

In contemporary vision experiments, the eye-tracking experiment is a common research and analytical tool. It can accurately capture the visual state and data of human eye vision in space and record eye-tracking trajectories within a defined timeframe, which can offer a powerful complement to subjective evaluation [18]. Currently, eye-tracking technology is widely used in medicine, behavioral research, human–computer interaction design, and design evaluation.

(1) In the medical field, many scholars have used eye-tracking experiments to assist in and improve medical diagnoses. Loamier et al. [19] used eye-tracking methods to optimize the design of a user-centered self-injection system. Nadja et al. [20] assessed deficits in sports medicine clinicians’ perceptions of eye movements associated with concussion through an online survey and determined that eye-tracking experiments could be effectively used in concussion diagnosis. Sharma et al. [21] demonstrated that the use of eye-tracking experiments and think-aloud protocols could help clinical laboratories to improve MDX report designs in a precision oncology setting. Aideen et al. [22] investigated the fixation behavior of children with ASD and TD in a classroom setting and used an eye-tracking experiment to develop and test a manipulative training tool that can be used to improve the typical fixation behavior of children with ASD.

(2) Research and applications on user fixation behavior during various activities have been conducted in combination with eye-tracking experiments. McIntyre et al. [23] used eye-tracking to study the association between teacher fixation and student-rated teacher interpersonal behavior. Kim et al. [24] proposed a generative framework based on the hidden Markov model (HMM) and applied it to study selective sustained attention in children. Castro et al. [25] used an eye-tracking experiment to compare the visual behavior and decision making in interactions between coaches and young players in the analysis of volleyball attack scenarios.

(3) Eye-tracking experiments are used in the field of human–computer interactions to improve the user experience of interactive systems. Bissoli et al. [26] developed an eye-tracking-based assistive system to control and monitor an IoT-based smart home. Radu et al. [27] developed an eye-tracking typing application for a virtual keyboard based on eye-tracking experiments. Li et al. [28] designed human-centered cockpits to improve the safety and personnel performance in aviation by using eye-tracking to compare interactions with two different designs for crew alert systems.

(4) Eye-tracking technology has been used to investigate the physiological-visual perception of users so as to explore the factors and effects of a design on their visual perception. Anderson et al. [29] used eye-tracking experiments to explore the size and design of alcohol content labels and how the strength of alcohol displayed on these labels affects consumers’ visual attention. Martin et al. [30] proposed combining VR with eye-tracking to measure consumers’ visual attention in markets. Moon et al. [31] used EEG and eye-tracking to investigate the issue of the implicit monitoring of users’ perceptual responses to vehicle design.

Based on the above research, this study combines eye-tracking experiments to quantitatively analyze the visual-perceptual traits of users when they observe the coating design of machine tools and provide optimization guidance regarding the details of the coating design schemes of machine tools. Machine tools are large and complex industrial machines, and any flaw in the modification process will likely result in major costs and time expenditure. Thus, we summarize the coating design specification of machine tools based on an eye-tracking experiment and use this information to guide the optimization of the design schemes to improve the economic efficiency of the optimization process of the coating design of machine tools. Additionally, virtual reality technology is used to demonstrate and evaluate the effect of the coating design of machine tools.

2.2. Virtual Reality Technology

To improve the efficiency of the design optimization and verification, virtual reality (VR) technology is widely used as a stable and promising technology for the presentation and evaluation of design schemes for various industrial products [32]. Elisa et al. [33] used virtual reality to help designers to create interactive workstation prototypes and validate early stage design schemes. Kostis and Ritala [34] used virtual reality to create digital workpiece systems used to help enterprises to solve real-world problems. Chen et al. [35] compared the performance differences between traditional and context-based instructional designs using a self-developed virtual reality computer numerical control (VRCNC) training environment. Gorobets et al. [36] evaluated the effectiveness of virtual MTM and traditional MTM in the early stages of manufacturing through a user study. In the display and evaluation of machine tool designs, VR technology can enhance the realistic representation of the design scheme and aid in the test-based assessment and optimization of the design scheme.

3. Materials and Methods: Eye-Tracking Experiment

In this study, to select suitable samples for visualization, a series of CNC cylindrical grinding machines from the Shanghai Machinery Factory in China were selected as samples for the study of the coating design. The process of this method is as follows. Firstly, select suitable experimental objects, experimental samples, and experimental equipment and formulate experimental procedures. Secondly, collect and analyze the relevant eye-movement experimental data, including the subject’s focus area, eye-tracking trajectory, and eye-tracking interest area, using design specifications to guide the development of the optimal design scheme. Finally, the optimization of the coating design schemes is finished based on the earlier study.

3.1. Experimental Subjects

In December 2020, 20 people (10 males and 10 females) were recruited for the eye-tracking experiment. The subjects were all between 23 to 58 years old, with an average naked or corrected visual acuity of 1.0, and none had color blindness or color weakness. Before the experiment, all subjects had a basic understanding of, or training in, product design and machine tools.

3.2. Experimental Samples

This experiment was conducted in collaboration with the Shanghai Machinery Factory in China. It is a large manufacturer of precision grinder machine tools in China and occupies an important position in the Chinese grinder machine industry. A total of six models of the best-selling CNC cylindrical grinder machine tool series were selected for the experimental sample, as shown in Figure 1. It’s the external coating design of machine tools including many elements, such as the company’s logo, machine tool model, base, observation window, handle, and operating panel.

Through prior communication with the enterprise, the designer completed the preliminary sketch scheme for the coating design. The experimental materials were screened and prepared taking into account four aspects, including the scale of the logo and the machine tool models, the typographic position of the logo and the machine tool models, the color coating scheme of the machine tools, and the color matching scheme of the machine tools. Additionally, the corresponding experiments 1, 2, 3, and 4 were set up, respectively. Some picture materials for the eye-tracking experiments are shown in Figure 2. The logo of Shanghai Machinery Factory was placed in the upper left corner, and machine tool model was placed in the upper right corner.

3.3. Experimental Equipment

The Tobii X120 Eye Tracker is the eye-tracking equipment utilized in the experiment, manufactured by Tobii in Sweden, which is used in conjunction with the Dell computer to form a desktop vision-tracking system. In this experiment, the monitor used by the subject is the Dell G15 monitor, with a sampling rate of 60 Hz, a screen size of 15.6 inches, a maximum screen resolution of 1080 p (1920 × 1080), and a screen technology of 100% sRGB. The experimental visual distance is approximately 70 cm, with the binocular acquisition and the rest of the parameters kept at the system default settings. The operation specification of the eye-tracking experiment is shown in Figure 3. The eye-tracking device provides a source for the data analysis as part of the visual research on the coating design of the machine tools by capturing and recording data on the subject’s eye trajectory, area of interest, and fixation duration.

3.4. Experimental Procedures

To ensure the validity of the experiment, this experiment is conducted in the same environment, where the subjects are emotionally stable and free from external human interference. During the experiment, focus is placed on the optimization of the color, shape, scale, and spatial position of the external coating elements of the machine tools, including the logo and signage, the overall coating, the door cabin system, the viewport window, the control panel and display, etc. Then, the subjects navigate through the standardized areas containing each experimental material in turn, and the equipment records eye-tracking data based on the observation.

(1)

Before the experiment, the experimental researcher installs the equipment and introduces the subject to the experimental procedure and precautions.

(2)

The subjects sit in front of the screen in a comfortable and upright position, while keeping the head stable and the eyes focused on the center of the screen. Then, a pre-experimental equipment test is conducted to ensure that the equipment and the users are adjusted according to the most appropriate conditions.

(3)

The subjects follow the task flow and view 28 frontal views of the coating design scheme. The images are set to switch automatically, and each image is given a 5s fixation time.

(4)

At the end of the experiment, the experimental data, such as the focus area, the eye-tracking trajectory, and the eye-tracking interest areas, are analyzed separately to extract objective information related to visual habits, and the coating design specification and design scheme optimization are completed on this basis.

3.5. Data Analysis and Result

The main indicators of the eye-tracking experiment and their meanings are analyzed in order to select the eye-tracking indicators required for the experiment. As shown in

Table 1. Eye-tracking indicators in the eye-tracking experiment.

Eye-Tracking Metric	Implication
Residence time	The total amount of time spent focusing on a certain area of interest, including all the fixation points and eye movements in the area of interest, as well as the time of the return visit.
Number of fixation points	The total number of fixation points in the area of interest, which is closely related to the residence time.
Fixation duration	The average duration of time spent on all the fixation points, which is usually in the range of 150ms~300ms.

, these include the residence time, number of fixation points, and fixation duration.

This study analyzes eye-tracking data in three ways. (1) Firstly, one collects and analyzes the data on the sight-focused area. The fixation on each experimental image is fixed at 5 s. One observes the area of the coating design that is of most concern to the subject during that timeframe. (2) Secondly, one extracts and analyzes the trajectory information regarding the eye-tracking and explores the relationships between the layout of the coating design elements and the changes in the area of concern. (3) Thirdly, one extracts data, combined with the user’s time to first fixation and total fixation duration, and analyzes the key areas of interest. Through the collection and analysis of the above experimental research data, one can effectively reveal the visual cognitive rules of the coating of machine tools, form design specifications, and optimize the coating design scheme of the machine tools.

3.5.1. Analysis of the Sight-Focused Area

The collected sight-focused areas are analyzed using a heat map, and the key focus area and design elements of the coating design of the machine tools are summarized. Some of the heat map results obtained by the experiment are shown in Figure 4. The level of focus is achieved by distinguishing between different colors in the pictures.

The color of the focus area in Figure 4, moving from green to red, represents a gradual increase in the user’s visual attention to the elements of the area. The visual heat maps of the subjects in the three experiments are analyzed separately. In Experiment 1 (scale of logo and machine tool model), 76% of the subjects preferred a logo and machine model height to machine tool height ratio of 0.125 to 0.15, which demonstrates that the size range allows the user to quickly identify the logo and machine tool model.

In Experiment 2 (typographic position of the logo and machine type), 63% of the subjects preferred a layout with the logo and machine tool model on the top left and right of the machine tool. The layout is convenient for the human eye in distinguishing the information conveyed by the logo and the machine tool model and is consistent with the goal that the information is not easily obscured, which is an important reference for the optimization of the following coating design scheme.

In Experiment 3 (color coating scheme of machine tools), 53.4% of the subjects provided the strongest feedback when observing the heat map formed in Experiment C-1, followed by Experiment C-4. This indicates that the subjects preferred the form of color coating scheme in which the observation window is coated with dark grey, the machine tool bed is coated with a relatively light grey, the machine tool base is coated with the darkest grey and enhanced by the color of the logo, and the observation window area with the deeper coating extension is the key area of the machine tool, whose color scheme can quickly attract the user’s attention. Meanwhile, the darkest-coated base enhances the visual feeling of the lighter top and heavier bottom, increasing the stability of the machine. The lightest-coated machine bed is embellished with a red logo and the machine tool model number, conveying the message of the machine and increasing its aesthetics while transmitting the machine tool information.

In Experiment 4 (color matching scheme of the machine tools), the subjects’ feedback on the different shades of the color matching schemes was collected and analyzed. This area of the heat map shows that the subjects provided stronger feedback on the color matching scheme of the observation window with a higher color concentration. This indicates that in the process of selecting the color scheme, it is important to try to provide the observation window and the machine tool bed form with a more obvious color contrast, enabling the viewer to more clearly distinguish between the primary and secondary areas. The body should be coated mainly in grey so as to avoid visual fatigue caused by large areas of bright colors.

3.5.2. Analysis of the Sight-Focused Area

The main purpose of this step is to investigate the priority level of attention given to different areas, the trajectory of the user fixation points as it switches between areas, and the position and sequence of the fixation points when the subjects observe the experimental materials of the coating scheme. The digital serial number in the fixation point indicates the order of eye-tracking when the user observes the experimental materials. The line between the two fixation points near the serial number represents the behavioral process of the user’s vision switching from one fixation point to another. The size of the circle indicates the length of time during which the picture material was observed. The longer the fixation time was, the larger the circle is.

The experimental results are shown in Figure 5. The images of the eye trajectories for each of the four experimental groups are clustered and grouped in order to observe and analyze the characteristics of the subjects’ eye trajectory changes.

In Experiment 1 (scale of logo and machine tool model), it was found that when the subjects observed the experimental materials, the fixation point numbers of A-2, A-3, and A-4 with the larger-scale logo and machine tool model, the gaze was more forward and the fixation point was smaller, indicating a short fixation time. This reflects that the identified elements of a larger scale can attract the users’ attention faster, and the priority of fixation is higher. At the same time, due to the larger and clearer font, it takes less time for the user to identify the information, and the ability to identify information is stronger. The designs of the logo and model size within these ranges can more effectively attract people’s attention and transmit information.

In Experiment 2 (typographic position of the logo and machine type), the layout schemes B-1, B-2, and B-3, where the logo and machine tool model are coated on both sides of the machine tool bed, respectively, received better feedback from the users. Schemes B-2 and B-3, in which the two elements are coated on the upper sides of the bed, respectively, were viewed earlier, with smaller fixation points and a shorter time. In addition, scheme B-3, with the logo coated on the right side and the model number coated on the left side of the bed, is more in line with the habit of reading from left to right, and it received the best feedback from the subjects. Therefore, when optimizing the design scheme, we can refer to the experimental analysis results and the layout of the experimental picture B-3.

In Experiment 3 (color coating scheme of the machine tool), the first fixation point of the experimental picture was located in the central observation window of the picture, followed, in turn, by the scanning of the logo and machine tool models, the content of which could arouse the interest of the subjects and cause the subjects to fixate for a long time. Then, the bed and the base were briefly scanned in turn. This indicates that the subjects’ observation of the coating design scheme of the machine tools stared with the observation window in the center of the machine tool. The content of the logo was more visually attractive to the subjects, while the bed and base were less attractive. The attractiveness of Schemes C-1 and C-4 was greater than that of the other schemes in the group, which indicates that these two coating design schemes are more likely to attract the user’s attention and observation interest, and the information conveyed is clearer, which can aid in the optimization of the design scheme.

In Experiment 4 (color matching scheme of the machine tools), when the subjects observed Schemes D-1 and D-3, with a stronger color contrast, they showed larger fixation points, which means a longer fixation time and greater interest. The elements that mainly attracted the user’s first sight were usually the logo, followed by the text, the more complex observation window, and the other parts, as well as certain details of the appearance of machine tools. Therefore, it is necessary to make a reasonable color matching selection for the design elements that attract the users’ interest. One should focus the users’ attention to a certain area and avoid distracting users with too many complicated details.

The experimental and analytical results of the eye-tracking trajectories are consistent with the results of the eye-tracking focus heat map, which can be used to guide the optimization of the coating design of machine tools.

3.5.3. Areas of Interest Analysis

In eye-tracking experiments, defining areas of interest (AOIS) is the best way to test how long and how many times subjects pay attention to a product. By dividing the experimental materials into multiple areas of interest, the corresponding eye-tracking data can be obtained and analyzed through eye-tracking experiments [18], which can reflect the users’ tendency to observe the product with interest.

In this study, the user’s area of interest (AOI) in the eye-tracking experiment consists of the logo, console, and observation window, etc. Additionally, the experiment is carried out in combination with the data on the user’s first fixation time and total fixation duration. The time to the first fixation is the amount of time the user spends observing the element, from the time when the image is displayed to the time when it is noticed by the user. The shorter the time is, the more attractive the element is to the user [37]. The total fixation duration is the total time that user spends in an area, with longer fixation durations reflecting higher levels of interest in what the user are looking at [38].

Table 2. Illustration of the machine tool picture AOI.

AOI of a Single Machine Tool Picture	AOI of Multiple Machine Tool Pictures	AOI List
		The brand logo, machine tool model The operating table The protective door The commodity shelf

offers an illustration of the machine tool picture AOI.

To reduce data deviation caused by the observation of the machine tool pictures due to uncertain factors, it is necessary to analyze the AOI data by combining multiple machine tool coating experimental materials of the machine tools.

Table 3. The data on the machine tool AOIs.

Elements of Concern		LOGO	Machine Tool Model	Machine Tool Bed	Observation Window	Base
Time to First Fixation (s)	frequency	10	9	27	30	8
	average	1.37	2	2.14	0.36	3.7
	total	13.73	18.04	57.76	10.92	29.63
Total Fixation Duration (s)	frequency	10	9	27	30	8
	average	0.55	0.56	0.8	1.44	0.48
	total	5.52	5.05	21.63	43.14	3.82

shows the AOI data list of the coating interface of the machine tools.

The shorter the average time to the first fixation is, the less time it takes for the subjects to notice the area of interest. This indicates that the area is more likely to attract attention. Otherwise, it is less likely to attract attention. The shorter the average total fixation duration is, the less total time it takes for subjects to observe the area completely, which means that the information conveyed in this area is relatively simple or less attractive to the subjects. Conversely, the longer the average total fixation duration is, the more complex the information conveyed in the area is, and the more attractive it is to the subjects.

From the data calculated in the above table, it can be concluded that, among the relevant elements of the coating design, the subject’s average first fixation time, with respect to the observation window, is 0.36s at the shortest, while the average total fixation time is 1.44s at the longest. This indicates that the design element is the most likely to attract attention. Because of its complexity, the longest time is required for the subjects to observe it, and it is their main area of interest. The machine base has the longest first fixation time and the shortest total fixation duration, being 3.7s and 0.48s, respectively, and it is the weakest area of attention, followed by the logo, the machine tool model, and the machine tool bed. The data provide support for the optimal design of the coating scheme of the machine tool.

4. Design Optimization of Schemes

Combined with the analytical results of the eye-tracking experiment above, the initial definition of the machine coating scale determines that the ratio of the height of the logo to the height of the machine tool operating body is 1:10, with the logo and the machine tool model number above the machine tool operating body being symmetrically distributed to the left and right.

Combined with the experimental results, the logo and model specifications for the CNC cylindrical grinder series products are summarized in

Table 4. The modelling scale specification of the logo and machine tool model.

Type Code	Logo Scale/Mm	Position Dimension
A (Short)	Length and width: D4 = 510 E4 = 310	Horizontal: A4:A1 = 4:5 A1:Z3 = 20:1 Vertical: B1:B4 = 13:1 B1:C4 = 5:4 B1:F4 = 11:8
B (Mid-length)	Length and width: D4 = 510 E4 = 330	Horizontal: A4:A1 = 4:5 A1:Z3 = 28:1 Vertical: B1:B4 = 13:1 B1:C4 = 5:4 B1:F4 = 11:8
C (Long)	Length and width: D4 = 510 E4 = 600	Horizontal: A4:A1 = 9:10 A1:Z3 = 65:1 Vertical: B1:B4 = 17:1 B1:C4 = 6:5 B1:F4 = 5:4

. As shown in the illustration of the machine tools in Figure 6, the corresponding logo and machine model dimensions and position data are calculated in order to provide a reference for the design optimization of the coating scheme for the machine tools.

Examples of the calculations are as follows:

The determination of the logo and machine tool model scale: D4 = 510 mm; E4 = 600 mm.

The determination of the logo and machine tool model position dimensions:

A4:A1 = 0.881, calculated: A4 = 6167 mm ≈ 6165 mm;

A1:Z3 = 64.22, calculated: Z3 = 109 mm ≈ 110 mm;

B1:B4 = 16.37, calculated: B4 = 167.9 mm ≈ 170 mm;

B1:C4 = 1.198, calculated:C4 = 2295.5mm ≈ 2295 mm;

B1:F4 = 1.273, calculated: F4 = 2160.3 mm ≈ 2160 mm.

By applying the above calculation method for the logo scale, the final layout scale scheme of the logo and model is attained, as shown in Figure 7.

According to the application of the above design specifications, the rest of the coating design schemes are shown in Figure 8.

5. Validation and Evaluation of the Design

To improve the efficiency of the validation and evaluation of design schemes and reduce operating costs, virtual reality technology is used to build a platform for the coating designs of machine tool series. The design scheme is evaluated and verified by the user’s scoring method. Additionally, it also provides a new verification platform for the optimization of the coating designs of other series of machine tools.

5.1. Virtual Reality System Principles and Hardware and Software Equipment

Combined with virtual reality technology, a platform for the coating design of the machine tool series is built. First, a model encompassing the entire range of the CNC cylindrical grinder machines is created in 3Ds Max. Then, the model is applied to the same scene, built in Unity, by controlling the lighting variables and simulating the environment of the machine tool at different times of the day. The subjects are asked to wear head-mounted VR equipment so as to participate in each virtual scenario. Through their VR experience, users can observe the effect of the simulated coating design on the series of machine tools and comment accordingly.

The following equipment is associated with the construction of the platform:

(1) Lenovo laptop (Legion R7000 2020), with the following parameters. CPU series: AMD Ryzen 5. Base Clock: 3.0G Hz. Graphics card category: high-performance gaming discrete graphics. Physical resolution: full HD screen (1920 × 1080). Screen color gamut: 100% sRGB.

(2) By using Unity to simulate the virtual reality scene of the machine coating and importing the machine models created by 3Ds Max, a complete VR test environment for the machine coating design schemes is formed.

(3) HTC Vive Pro VR device. The HTC Vive Pro device is a professional version of the Vive Pro virtual reality device launched by HTC Corporation in 2018. The device uses 360° tracking technology, with a resolution of 2880 × 1600 PPI. Its tracking range is 10m x 10m, which conforms to the needs of this scene building experiment.

5.2. Virtual-Reality-Based Machine Tool Coating Simulation Platform Construction

The machine model is first created in 3Ds Max, and then Vary is used to map the product and change the settings based on the sample of the coating design scheme of the machine tools, completing a detailed rendering of the product. The rendered coated models of the machine tools are then imported into Unity in the correct format, while the position, orientation, and scale of the models are adjusted, and the lighting parameters are set to match a more realistic environment.

Before the VR equipment is applied to the wearer, two spatial positioners are fixed to tripods and placed at the diagonal ends of the laboratory, with a diagonal length of approximately 7.4 m, a height of approximately 2 m, and a downward inclination angle of 30°. During the scene construction process, the subjects are required to wear the VR headsets several times in order to make adjustments to the headset position, angle, and other details. The interaction between the HTC Vive Pro and the scene is achieved in Unity through the Steam VR plug-in. The perspective of the VR device replaces the original camera in Unity, and at this time, the simulation environment of the machine tool coating can be observed through the device, bringing a more realistic experience to the subjects. The effects of the virtual scenes are presented in Figure 9. Finally, each machine tool scenario is simulated in turn, with the subject wearing the VR headset correctly, and the results recorded for the real machine coating prototype are compared to the results of the virtual simulation of the machine coating design based on the subject’s observations.

5.3. Validation of the Coating Design Schemes Based on User Evaluation

The participants are invited to rate the series of coating designs presented in the VR scenarios in turn, including the original machine coating prototype and the optimized coating design. The scoring team consists of 20 randomly selected technicians, marketers, and users from the company. The content and scoring criteria of the evaluation questionnaires for the coating design of machine tools are established. The evaluation indicators include considerations about whether the visual image of the coating design scheme is uniform, whether the scale proportion of the coating module is in line with the user’s observation habits, and whether the information on the coating layout can be quickly and comprehensively accessed by the user.

(1)

Design of user evaluation questionnaires for the coating design schemes

The set of evaluation indicators of the effectiveness of the external protective coating of machine tools is X = {x1, x2, ..., x6}, and the set of evaluation levels is Y = {y1 (excellent), y2 (good), y3 (average), y4 (fair), y5 (poor)}, with the evaluation description levels Y corresponding to scores of 5, 4, 3, 2, and 1, respectively. Based on the content of the coating design schemes, the evaluation scoring table of the coating design schemes is constructed, as shown in

Table 5. Evaluation indicators and scoring table for the coating design schemes.

Scheme No.	Evaluation Levels X	Evaluation Description Levels Y
Scheme P	Harmonious and vivid design combinations x1	excellent y1	good y2	average y3	fair y4	poor y5
	Overall layout is rational, unified in style, with natural transitions and harmonious proportions x2	excellent y1	good y2	average y3	fair y4	poor y5
	The design matches the form and function, with a strong sense of integrity and a logical layout x3	excellent y1	good y2	average y3	fair y4	poor y5
	Uniform style, highlighting the corporate identity x4	excellent y1	good y2	average y3	fair y4	poor y5
	Accords with the need to promote the development of machine tool products x5	excellent y1	good y2	average y3	fair y4	poor y5
	Beneficial to the product image x6	excellent y1	good y2	average y3	fair y4	poor y5

.

(2)

Analysis of the questionnaire results on the coating schemes

Subjective opinions based on the user’s experience are assessed based on the experiences of the subjects when observing the coating design schemes in a virtual environment, using a generated scale. The scoring results are shown in Figure 10.

As shown in Figure 10, the comparison between the original and the optimized designs evaluated six aspects: the matching of the coating design of the machine tools, overall layout and proportions, matching of the form and function, the style and identity, the level of accordance with the promotion and development of the company, and the level of contribution to the enhancement of the product image. The average score of the optimization of the coating design schemes is higher than that of the original one. The indicator with the most significant difference in the evaluation scores is x2 (reasonable overall layout, unified style, natural transition, and proportional coordination), with an average score of 3.6 for the original scheme and 4.45 for the optimized schemes. The indicator x4 (uniform style, highlighting the corporate identity) also shows a tremendous difference between scores. The average score of the original scheme is 3.45, while that of the optimized schemes is 4.25. The results show that the coating design optimization schemes in this study were approved by the users and are in line with the corporate brand image. The validity of the experiments for the optimization of the coating design of the machine tools is verified, and the results provide support for the coating design optimization and application of other series of machine tool products.

6. Conclusions

(1) To assist companies in establishing their brand identity and improving the optimization design efficiency of machine tools in a short time, this research used the CNC cylindrical grinder series machine tools from the Shanghai Machinery Factory in China as a sample, designed and studied coating design schemes for the machine tools, completed the preliminary design plan, and provided a foundation for subsequent experiments.

(2) Through eye-tracking experiments, data such as the focus areas, eye trajectories, and eye interest areas were collected and analyzed in order to provide an objective design specification for the optimization of the coating design scheme of the machine tools. Optimization design schemes were established based on this.

(3) A virtual reality platform for the coating design of the machine tool series was constructed with the support of virtual reality technology to increase the effectiveness of the verification and assessment of design solutions and decrease operational expenses. Additionally, the pre-designed scheme and the actual scheme were compared, reviewed, and validated by the users’ subjective assessment, based on scoring. These results serve as a guide for the ideal design and application of coatings for other machine tool products.

(4) This research presents an optimization approach for the design of the exterior surface coatings of machine tools. The results show that this approach provides support for the optimization of the design and working efficiency of machine tool coatings in enterprises. Future work should investigate design optimization tools that include ergonomics based on vision experiments and virtual reality. With the continuous progress of experimental conditions and the expansion of human, machine, and environmental test data, the accuracy of the data collection and analysis will improve, and the external surface coating design of machine tools will become more in line with user demands.