Study on the application of particle swarm optimization in the virtual reality of the modified wood furniture

Abstract

Wood modification is a processing method to change the physical and chemical properties of wood, and it can improve its corrosion resistance, flame retardancy and other properties. With the rapid development of the furniture industry, the application of high-end technology is the inevitable way to meet the needs of the vast majority of consumers. Therefore, this paper based on the PSO algorithm, the properties of the modified wood were grasped as a whole and then used the virtual reality technology, and applied the design of modified wood to the furniture field which could ensure the safety of users at the same time and also take into account the aesthetic needs of the user. In this paper, the 3D model based on virtual reality technology could be modified and the accuracy of nodes was counted, and its feasibility was determined through its use.

Keywords

Particle swarm optimization (PSO)modified wood furniture virtual reality discussion

1 Introduction

The rapid development of virtual reality technology has brought more opportunities and challenges for today’s society. It is a common concern of today’s technicians to use virtual reality technology to design wood modified wood for all walks of life [1]. Virtual reality technology has rapidly spread to every corner of the world, which plays an indispensable role in the progress of industry and cultural innovation [2]. At the same time, in the computer industry of our country, the application of virtual technology is not extensive. Such huge resources cannot be used for people, which is a great pity. In China’s furniture industry, it has followed the traditional wood production, the procedure is extremely cumbersome in the user experience and in the innovation of the modified wood that means furniture can’t do the version innovation on the basis of a finished product, and it needs extremely heavy manual processing in the processing [3].

In the background of large data, it is an unstoppable trend that the traditional industry is in line with computer algorithms. The application of particle swarm optimization is derived from the computer system. In this paper, a set of virtual reality technology has been designed for furniture industry’s innovative design to assist in the design and development of virtual furniture for furniture modification [4]. The system has made a technical construction of the virtual modified wood, and the structure of the frame has been built, the units of the modified wood has been split and reorganized, the specifications and requirements of the furniture design are recorded in the system processing port, and the virtual modified wood design for virtual reality technology is finally completed [5].

2 State of the art

In the initial understanding of virtual reality technology, we have found that virtual reality technology abroad has been applied to many fields, such as vehicle manufacturing, engineering construction, and so on. According to the effective data, the application coverage of foreign virtual reality technology has reached 63.4% in all walks of life [6]. This data indicates that the problem is also one of the original intentions of developing furniture virtual modified wood design in this paper. There is immeasurable application energy in the resource of virtual technology [7]. The construction means of virtual system in foreign countries are maturing, and our country is still at the initial stage. This is a goal that needs to be weighed through the joint efforts of all walks of life. The computer technology is the foundation of virtual technology, which is far from the computer field in our country in terms of server lap, data chain integration, and final expression, so we need to try to catch up with it [8].

China’s virtual reality technology rose in twenty-first century. In terms of technology, China should follow the steps of the western countries to carry out the technology development smoothly. In the later follow-up, China has shown great learning ability and has never stopped in the development of virtual technology. China’s furniture industry is one of the mainstream industries, to break the traditional furniture modified wood design and make use of virtual reality technology, applying it to the furniture industry, and then radiating to other industries, we need to learn the advanced western technology continuously [9]. On this basis, we integrate particle swarm optimization algorithm, and make breakthroughs in modeling and programming of virtual reality technology, and achieve the integration of three aspects in technology, theory and practice, which plays a supporting role on the design technology of furniture virtual modified wood [10].

3 Methodology

3.1 Virtual 3D model construction

To build up the huge and tedious 3D model and further improve the furniture design information, we should first consider the virtual construction level of the 3D model. In this paper, considering the process of building three-dimensional models, and able to process the huge system, we choose the construction model of virtual 3D models for technical support. The three-dimensional model is composed of three axes and element filling that are the cardinal number of lateral development, the base of longitudinal development and the extension base. In this paper, the computer virtual reality technology is used to carry out the structure of the 3D model, and then the information of furniture design is entered into the system by computer. According to the indication standard and specification requirement of each system, the information data is processed for the output of the 3D model, the model has received the digested data, and finally the three-dimensional model has been built. In this process, the three dimensional model operation rule is adopted. First, the number of three dimensional data is obtained and the headline is encoded by the upper calculation. The specific code is used to classify decentralized data and automatically regularization by computer data, so as to provide a precondition for further work. Based on this data encoding, for the consolidation of decentralized data, we regard F as the sum of target data, Si is the number of scattered data, k1 is the number of scattered data, C is the number of unit data blocks, and M represents vertical coordinates. By checking the following formula, we have obtained the classification scheme of the integration of distributed data.

$F (S_{i}) = k_{1} \times (1 - C (S_{i})) + k_{2} \times M (S_{i})$ (1)

In this formula, the main consideration is that every three dimensional terminal data can be applied. Where S1 to Si represents the number of each independent server in this three-dimensional technology system. As these servers are involved in the overall load, the overall load capacity will also be strengthened.

In addition, C (Si), M (Si) and W (Si) represent the expressiveness of their input nodes respectively. And once a node fails, the other nodes will complement each other, that is, the explanation of our formula. When k1 + k2 = 0, there will be the following formula:

$W (S_{i}) = F (S_{i})$ (2)

The above formula is mainly a special case when the nodes are equal in the three dimensional construction systems, and it is also an embodiment of the self-regulation mechanism of the algorithm. Among them, B indicates the node coefficient, which can be recorded next after the wood properties are filled. Since then, we set the value of the K1 greater than the value of K2. It may be assumed that the ratio of K2 to K1 is approximate to the Euler ratio, then K1 : K2 =0.382 : 0.618. Because the floating-point operation has a certain load, the approximate ratio is 0.4 : 0.6. The value of K1 is 0.6, and the value of K2 is 0.4. The weight function expression can be expressed as: $\begin{matrix} F (S_{i}) = 0.6 \times (1 - C (S_{i})) + 0.4 \times M (S_{i}) \\ {\begin{matrix} | W (S_{i}) - F (S_{i}) | \leq BW (S_{i}) \\ | W (S_{i}) - F (S_{i}) | \geq BW (S_{i}) = F (S_{i}) \end{matrix} \end{matrix}$ (3)

After sorting and sorting the data, we can reflect the overall structure of the data source, and upload it to the system process step by step. The package data is entered into the processor terminal, and the terminal is processed and integrated through the data. The specific process is shown in Fig. 1.

Fig. 1

Construction process of virtual 3D model.

After the consideration and study of the three dimensional theoretical factors, the three-dimensional modified wood system is optimized. We use three dimensional virtual reality technologies to receive data from each self-supporting server’s data port collection system, cross platform mode, and finally integrate server cluster system, and we have developed a modified wood operation system. The focus is on the establishment of wood properties and requirements, and we then divide the whole system into the following important components, which includes virtual terminal information collection, that is, using the technology of three-dimensional algorithm, extract the information data of each subset of servers, and the basic operation information of each server, and including the furniture design data provided by the designer. Secondly, it is imported into the design of virtual reality furniture, according to the performance of modified wood, the virtual design of all kinds of furniture is carried out, and then the location of the placement is accurately determined.

3.2 Particle swarm optimization

After the completion of the virtual 3D model, it is necessary to consider the blending of the virtual design of the furniture and the particle swarm optimization. For the construction of the whole system, it is divided into four important components, which includes information collection of particle swarm optimization, namely, on the basis of three-dimensional virtual model, and extract information and data of modified wood materials from various furniture design factors, and the basic operation information of each furniture size. Secondly, from the perspective of security, we take the lead way to expand the server’s credit data collection mode and process the data. What we need to do is to integrate and process the chemical mix from different modified wood. Through particle swarm optimization algorithm, and after reaching a consensus with all wood furniture manufacturers, we can achieve smooth operation of the various programs, which provides guarantee for the successful operation of this research. Finally, it is imported into the furniture virtual design page and the furniture design requirements and rendering requirements collected by the above virtual reality technology are displayed on the general server management website. For the convenience of the furniture industry virtual network platform for editing, the final page is used in furniture design and display design of virtual window for people.

As shown in Table 1, in the integration phase of the furniture design factors, we use X1 to represent the system model of total data, use Y1 to represent the dispersed data assembly of the assembly of the Y axis, Z1 represents the coefficient composition of the small data unit of the vertical axis, αn represents element factors, fk represents three dimensional integration coefficient, βn represents the scattered cardinality of various angles. X1, Y1 and Z1 are taken as auxiliary nee ds into the formula to calculate, and finally get the frame information of each data level, so as to further fill the furniture rendering framework.

Table 1
The application of particle swarm optimization in the virtual reality of the modified wood furniture

Garment design factors and 3D virtual model overlap Data comparison rate of 3D virtual model in three angles of X, Y and Z

Xi(%) Yi(%) Zi

Angle of clothing size 69.3 5 0.34

Clothing color rendering 72.4 3 0.62

Comparison clothing materials 82.9 7.6 0.73

Garment integrity effect 76.1 08.2 0.87

Garment design factors and 3D virtual model overlap	Data comparison rate of 3D virtual model in three angles of X, Y and Z
Angle of clothing size	69.3	5	0.34
Clothing color rendering	72.4	3	0.62
Comparison clothing materials	82.9	7.6	0.73
Garment integrity effect	76.1	08.2	0.87

${\begin{matrix} X 1 = α_{11} f_{1} + α_{12} f_{2} + α_{13} f_{3} + α_{1 k} f_{k} + β_{1} \\ Y 1 = α_{21} f_{1} + α_{22} f_{2} + α_{23} f_{3} + α_{2 k} f_{k} + β_{2} \\ Z 1 = α_{31} f_{1} + α_{32} f_{2} + α_{33} f_{3} + α_{3 k} f_{k} + β_{3} \end{matrix}$ (4)

We calculate the results of each of the parameters and arrange the three-dimensional architecture in each direction. η represents the average value, xi and yi represent the cardinality of the transverse longitudinal unit, respectively. k, p represent the level of transverse and longitudinal numbers, gj, βj represent its three-dimensional surface coefficient. i, j represent the number of various levels, and βi represents the lateral base. We compare the integration of three-dimensional frame combination of color, then the average value, and the calculation results are as follows: $η (x_{i}) = \sum_{j = 1}^{k} g_{j}^{2} + \sum_{i - 1}^{p} β_{i}^{2}$ (5) $η (y i) = \sum_{j - 1}^{k} g j^{2} + \sum_{i - 1}^{p} β j^{2}$ (6)

Secondly, through the calculation and integration of information and data, in the process of the design, we will focus on the matching of particle swarm optimization and furniture design factors. In data operation and collection, based on the three-dimensional framework built above, we calculate particle swarm algorithm for information from furniture design, and divide it into three main aspects: size data, material data and wood durability. Through collation of furniture size data, we can draw a big direction of integration, take it as the foundation of virtual reality technology, and make the first step of changing complexity into orderliness. After the completion of the above work, the inserting of material data is carried out, which is fitted with the size data, and the range of the material is obtained. As a link between the three dimensional data frame, the requirement of its design precision must meet the construction principle of the system, and its precision must be determined. Finally, we will finish the work of this link and collect the material input. In the front, we can see that through computer technology, we can numbered it by algorithm, and then accomplish the virtual technology of furniture design. The input system flow of each factor is shown in Fig. 2 below.

4 Result analysis and discussion

Fig. 2

The application of particle swarm optimization in the virtual reality of the modified wood furniture.

Furniture design faces the group of the consumer; therefore, the designer should adhere to the people-oriented design purposes. In terms of the safety of the material, the depth, test and improvement of the material cannot be harmful to the human body. After the design of the corresponding 3D virtual framework system is completed, we have done a centralized spot check test for the data framework of the algorithm and the improved system. By putting the design results into an input test, to verify the accuracy of the algorithm, we use a set of tables and chairs for example, build the framework of virtual reality technology, and then improve the wood material index simple entry, and let the system does some design to observe its size in the design if there is deviation. And then we put it into a set of 104 square meters of furniture design in three rooms and one hall, all of the furniture is involved in the design of the model. The performance and effect of the improved wood furniture in this design are observed. The specific results are shown in Table 2 as follows:

Table 2

Comparison of 3D model and 2D model test results

Factor	Three dimensional virtual base acquisition factors
	Design size	Traditional manual processing	Material preparation	Overall effect	Design inspiration
2D processing system	No accord with	0.3	3.2	rough	0.3
Traditional manual processing	No accord with	0.7	5.6	rough	0.2
3D processing system	accord with	1	9.1	fine	0.7

Through the observation of the results of this test, we have found that the overall effect of the modified wood furniture is very excellent. By recording the standard data into the program, it can be seen that the damage of traditional wood materials has been down by three percentage points. It can be seen that this scheme has a great advantage and accuracy compared with the traditional manufacturing process. Meanwhile, during the testing process, it is found that there is a mistake in the mixture ratio of materials, because the deviation exists in the knowledge standard of CNC machine tools. Therefore, this paper introduces particle swarm optimization (PSO), which first finds out the design code of CNC machine tool, and then categorizes the code according to the code, and tests these codes one by one. Through the test results, we can see that the characteristics of the existing code are very obvious. Therefore, we only need to add the intermediate plug-in to screen the mixture ratio of the material, so that we can get the material mix ratio and detect the data accurately. The application of intermediate plug-ins can be used to optimize the system in the process of application, and can constantly adjust it according to the environment of the wood. Therefore, in the control process of the program, we constantly test and use virtual reality technology to do practical research, fill it constantly, and use particle swarm optimization algorithm to improve the accuracy of the virtual design model.

We will make the specific model as shown in the figure. We can see that in the process of testing, the improved link of failure can be automatically repaired by particle swarm optimization. This is the advantage of particle swarm optimization algorithm over traditional methods, that is, it can expand memory performance advantage continuously, optimize the system continuously in the process of application, and constantly adjust according to the atmosphere in which it is located. Using the improvement link of the furniture operation, the mixture ratio factor of the traditional capital convective processing software is collected, and then the model is calculated by computer. According to the indication standard of each algorithm, the information data is input to the improved system. After receiving the data, the model is digested and processed, and finally we complete the whole test. The traditional algorithm will weaken with the particle swarm optimization algorithm. When the bottom line reaches the bottom line, the particle swarm optimization algorithm will enter a state of proportioning, so the particle swarm optimization algorithm can express its advantages, which fully shows the results of this design in this aspect. In the field that the vector support model can’t improve the processing, the data mining technology will be supplemented in a timely manner. With the improvement of the ratio, we can quickly modify it to ensure the accuracy of the improvement. We intercept a part of the data in the form of wave graph, as shown in Fig. 3.

Fig. 3

Processing flow of 3D virtual system.

According to the research statistics of material mix proportion in this PSO, we can find that in the improvement of matching ratio, we can achieve almost perfect improvement, effectively collecting the mix points, and make accurate improvements. As shown above, the virtual system is in a state of smooth operation without a large operation error or deviation. Once the variables are controlled, and some other variables are changed, for example, while maintaining the same deviation, there will be a lot of operational errors. At this point, the system can make a mixture ratio report through the operation of the network model. In the report, we can find that the errors and deviations of each part can be carefully reflected. Therefore, we can see that in the optimization mode of software processing, the improvement plan has been perfected in the optimization process. When dealing with the bivariate model, it can also make a report based on the analysis and mix proportion. Therefore, the improved system can select the most suitable material plan in the huge database when dealing with the index of mixture ratio, and at the same time, it can also show strong handling ability in dealing with technical problems.

Table 3

Comparison of the application of particle swarm optimization in the virtual reality of the modified wood furniture

Information integration phase			GX model layer detection design data of free form surface
Factor	Accuracy rate	Resonant adjustment	efficiency	Overall effect
Finite element method	model 1	95.3%	excellent	0.93
GX model	model 2	89.6%	excellent	0.97
Hybrid GX model	model 3	90.7%	excellent	0.89
Traditional inverse algorithm	model 4	98.6%	excellent	0.87

5 Conclusion

With the rapid development of the furniture industry, the application of high-end technology is the inevitable way to meet the needs of the vast majority of consumers. The application of virtual reality technology to change the physical and chemical properties of wood can improve its corrosion resistance, flame retardancy and other properties. Therefore, based on the virtual reality technology of furniture virtual and modified wood design research, this paper adopts the front way to expand the server’s credit data collection mode, and carries out data processing. The main thing to complete is to integrate and deal with the chemical mix ratio of different modified wood. Through particle swarm optimization algorithm, and after reaching a consensus with all wood furniture manufacturers, we can achieve smooth operation of the various programs, which provides guarantee for the successful operation of this research. Finally, it is imported into the furniture virtual design page, and the furniture design requirements and rendering requirements collected by the above virtual reality technology are displayed on the general server management website. By putting it into a set of 104 square meters of furniture design in three rooms and one hall, all the furniture involved adopts the model built by this design. The performance and effect of improving the design results of wood furniture are observed and the results show that the scheme is credible. In this process, we still need further efforts for the systematic integration capability of 3D point data units and the matching of material mix ratio with industry standard indicators.

Footnotes

Acknowledgments

The study was supported by “Research on the key technology of furniture manufacturing made of plantation wood, State Forestry Bureau(No. 201404502-1)”.

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Garment design factors and 3D virtual model overlap	Data comparison rate of 3D virtual model in three angles of X, Y and Z
	Xi(%)	Yi(%)	Zi
Angle of clothing size	69.3	5	0.34
Clothing color rendering	72.4	3	0.62
Comparison clothing materials	82.9	7.6	0.73
Garment integrity effect	76.1	08.2	0.87