Emotional Fusion and Design Optimization Method Based on Kansei-AD: A Case Study of Laser Additive Manufacturing Equipment

: To improve the level of integration between the psychological and emotional needs of laser additive manufacturing equipment personnel and the cognitive interactive design of human - machine systems, an emotional optimization design method for laser additive manufacturing equipment based on Kansei engineering and axiomatic design (Kansei - AD) is proposed. By exploring the emotional cognitive process of personnel on equipment, the representation means of the psychological and emotional needs of personnel are clarified. To clarify the psychological and emotional needs of users, the Kansei semantic difference experiment is applied to determine the user domain of axiomatic design. To resolve the difficulty of accurately mapping and expressing between user emotional factors and design elements in the design process, a combination of rough analytic hierarchy process (rough AHP) and quality function deployment (QFD) is used in the functional domain to determine the user Kansei design needs. In addition, the mapping relationship between Kansei design requirements and design requirements is judged by independent axioms of axiomatic design. A matrix of morphological elements - design requirements is constructed, and the morphological design elements that meet the psychological and emotional needs of personnel are determined using the GRA - TOPSIS method. LDM4030 laser coaxial powder feeding equipment is taken as an example to verify the feasibility of the optimization method. The result shows that the method effectively optimizes the design of the equipment according to the psychological and emotional needs of personnel. It provides a new research idea for the psychological and emotional design of laser additive manufacturing equipment.


Introduction
Laser additive manufacturing equipment has become one of the most widely used emerging manufacturing apparatuses in the industry.However, most traditional laser additive manufacturing equipment human-machine system design process and interaction still only meets the interaction requirements at the functional cognitive level.
As a result, pleasant design problems, such as humanization and poor comfort, have become increasingly prominent.Operators are no longer satisfied with meeting the needs of laser additive manufacturing equipment only to achieve operational functions and pay more attention to their inner satisfaction and comfort in the interaction process.The degree of equipment emotional design has gradually become an important factor affecting the efficiency of human-computer interactions.Therefore, research is needed on how to effectively integrate the psychological and emotional needs of the operator with the cognitive interaction design of the laser additive manufacturing equipment man-machine system and achieve humanization.This research has important practical significance for improving the human-computer interaction efficiency of laser additive manufacturing equipment.
As a human factors research method for improving the level of emotional design and humanization of equipment for human-machine systems and interactions, Kansei engineering can accurately grasp the psychological and emotional factors of personnel and perform emotionalization from the perspective of personnel perception and cognitive psychology [1][2] .To achieve product emotional cognition and interaction design, ALANIZ [3] et al. proposed a new product idea, an emotion-driven innovation method centered on emotions, which defines the emotional intentions for consumers and makes strategic decisions while focusing on creative thinking and emotional design.KONGPRASERT [4] and others used principal component analysis to statistically analyze the customer's personalized emotional intentions and performed clustering interpretation to guide designers to design products that meet customers' personalized emotional needs.SIMEUNOVIC [5] studied how to apply emotional design methods in the development of new concepts for autonomous vehicles.KIM [6] proposed an emotional variable extraction and classification method combined with self-organizing maps to facilitate the collection and analysis of the user emotional experience.VIEIRA [7] et al. explored the interface rubber keyboard design method that integrates functions and user experience through Kansei engineering methods.GUO Fu [8] combined a back propagation neural network (BPNN) to identify the quantitative relationship between key design factors and emotional dimensions with a genetic algorithm (GA) to search for a design near the optimum.The proposed method helps to design web pages that can satisfy participants' emotions.Li Rui [9] et al. studied the design method of the NC machine tool panel based on emotional design, analyzed the emotional level of the machine tool panel, and extracted the design elements of emotional design through the semantic difference method.ZENG Dong [10] discussed the psychological cognitive changes in fire extinguisher users during the operation and improved the design of fire extinguishers by constructing the user's psychological model.JIA Dan-ping [11] and others used the word2vec model and sliding window technology to generate a user's Kansei semantic space and constructed a feature-Kansei emotion model to guide the emotional design of products.In the Kansei interaction design of product modeling, CHENG Yong-sheng [12] and others combined Kansei engineering with AHP to analyze and evaluate the modeling image of automobiles.WANG Xin-ting [13] aimed at the styling design of electric scooters, constructed a multiple regression prediction model of styling design with quantitative type I theory, clarified the correlation between the design factors of appearance elements and Kansei evaluation results, and guided the styling design of electric scooters.
In terms of interactive design decision-making based on Kansei engineering, SHIEH [14] et al. proposed an approach to a hybrid Kansei engineering system (HKES) combining the methodologies of support vector regression (SVR) and MOEAs.Through examples, it was verified that this method could accurately determine the emotional needs of users.Park [15] et al. used Kansei engineering methods to evaluate the effectiveness of vehicle interior warning and auxiliary information design under reduced visibility driving conditions.To determine the relationship between information features and design elements and the driver's expected emotional needs under low visibility conditions, a quantitative type I theory with a two-level hierarchical structure and a Kansei engineering method based on partial least squares were adopted.SUTONO [16] et al. proposed a hybrid method combining grey relational analysis and principal component analysis to solve the multiresponse optimization decision-making problem in Kansei engineering research.HARTONO [17] et al. combined TRIZ theory with Kansei engineering methods and proposed an improved KE method for quantifying customer satisfaction; it assisted designers to quickly find the focus of product design and improve product satisfaction.LI Lan-you [18] quantified the relationship between Kansei intention and modeling genes by constructing a gene double helix structure model of the product and assisted in the decision-making of product portfolio design.
In summary, researchers have achieved certain results for emotional research and the interaction of equipment man-machine systems on Kansei engineering, and they have formed more mature research ideas in the application of Kansei engineering methods to explore equipment emotional fusion design methods and decision-making.
However, the following problems in the specific laser additive manufacturing equipment design process remain unsolved: (1) In the process of transforming the emotional factors of Kansei into design factors, it remains necessary to rely on the experience of designers to realize product design.Methods that can accurately express and reflect the relationship between emotional factors and design factors are lacking.
(2) In the design process of traditional Kansei engineering, no design stage can accurately correct and promptly provide feedback to the program, which leads to the completion of the design plan and the beginning of the verification stage.Once the result does not meet expectations, more time and energy are needed to make corrections.
Thus, the axiomatic design ideas are introduced into the optimization process of laser additive manufacturing equipment, and an emotional optimization design method based on Kansei engineering and axiomatic design (Kansei-AD) is proposed in this paper.Based on the application of Kansei engineering to clarify the psychological and emotional needs of the operator, the axiomatic design is used to judge the fit between the design elements and the user's emotional needs and to test the optimization effect of the design plan.This method can solve the problem of underestimating the emotional needs of personnel in the process of laser additive manufacturing equipment human-machine system and interaction design.In addition, it can improve the interaction efficiency and humanization and realize the improvement of the pleasant design of laser additive manufacturing equipment.This work delivers three distinctive innovations: (1) Combining rough set theory with the analytic hierarchy process (AHP) to overcome the strong subjective weakness of the House of Quality (HOQ) weight setting.
(2) Testing the coupling relationship between the user's Kansei design requirements and the equipment's emotional design requirements through independent axioms and assisting the designer in being able to accurately judge the rationality of Kansei design.
(3) Combining grey relational analysis (GRA) and the TOPSIS method to improve the objectivity and credibility of the final form elements.
The remainder of this paper is organized as follows.The equipment emotional fusion and design optimization model based on Kansei-AD is established in Section 2. Section 3 describes concrete methods of the model.A case study is provided in Section 4. Section 5 discuss the effectiveness of this method, and Section 6 concludes our work.

Cognitive process model of laser additive manufacturing equipment operators
The product emotional fusion design focuses on the emotional experience of users in the process of using the product.Its essence is to explore the generation of emotional factors in the cognitive process of using the product, clarify the emotional needs of the personnel and integrate them into the equipment design process [19][20] .Fig. 1 shows the operator's cognitive process model of laser additive manufacturing equipment.In the process of emotional cognition, different forms of interactive information and logical judgments of the operation process are transformed into the personnel's attitude and emotion through cognitive psychology and then form the personnel's psychological and emotional factors.The cognition of morphological interaction plays a dominant role in this process, and the operation process has a certain influence on the formation of attitude and emotion.Therefore, to discuss the emotional fusion and design optimization of laser additive manufacturing equipment, the equipment form should be taken as the main entry point for analyzing the psychological and emotional needs of operators during the process of the form interaction.The approach and emotion of personnel can reflect the inner psychological and emotional state and can be represented by semantics.Therefore, when using Kansei engineering for the emotional fusion design of laser additive manufacturing equipment, the operator's psychological and emotional needs can be determined through the form of semantic experiments.On this basis, the equipment functions and the formation of design elements can be proposed, and the emotional fusion design can be finally launched for optimization.However, as the equipment design work continues to deepen, designers need to constantly review user needs and compare them with the current design situation and judge the integration of emotional needs with the current design based on their own experience.Based on subjective experience, the mapping relationship between the user's psychological and emotional factors and the design elements is usually biased, which affects the effect of the fusion design, and program modification is more complicated and difficult.
Axiomatic design is a method that guides designers to design product functions and product design parameters through mapping methods, or it guides designers to make correct decisions [21] .Through the mapping analysis between emotional needs and design elements, designers can accurately grasp the relationship between the user's psychological and emotional factors and design factors, effectively transform the emotional factors into equipment design factors, and improve the accuracy of the emotional integration design process.Therefore, this article combines Kansei engineering with axiomatic design to realize the emotional integration and design optimization of laser additive manufacturing equipment.

Emotional fusion and design optimization model based on Kansei-AD
The axiomatic design divides the product design process into four design domains, namely, the user, functional, physical and process domains [22] .Through the hierarchical mapping between the four design domains, designers can easily clarify the relationship between user needs, product functions, product parameters and design solutions [23] .
According to the mapping design process of axiomatic design, the optimization process of laser additive manufacturing equipment based on Kansei-AD proposed in the manuscript is shown in Fig. 2. It first determines the psychological and emotional needs of operators through Kansei engineering semantic experiments.Aiming at the problem of accurate reflection between psychological and emotional needs and design factors, the rough AHP-QFD method is used to determine the relationship between user needs and design needs, and clarifies important design needs.Transform important design requirements into functional requirements, and verify the coupling relationship between functional requirements and design parameters through independent axioms.The GRA-TOPSIS method is applied to combine the design parameters with morphological elements, and clarify the morphological elements that should be included in the optimal plan.Use information axioms to verify the optimization effect and realize the humanized design of laser additive manufacturing equipment.

Optimized model construction 3.1 User needs determination based on the Kansei semantic experiment
The pleasant design of laser additive manufacturing equipment itself is primarily based on the subjective feelings of operators in the process of using the equipment.Therefore, when discussing the emotional optimization design of equipment, it is necessary to thoroughly study the psychological and emotional factors of the operator during use and explore the specific user requirements of the operator for the humanization and comfort of the equipment.The semantic difference experiment method of Kansei engineering is used to explore the needs of operators from the perspective of personnel psychology and emotion.
The semantic difference experiment method converts the Kansei knowledge of personnel into evaluation data by establishing the connection between the semantic space and the representative product, and then it analyzes the design requirements [24] .Its advantage is that it can directly determine the product status that operators value most at the psychological and emotional levels.The semantic difference experiment is primarily divided into the following steps: (1) Establish the Kansei vocabulary space and sample space.Through an investigation and collection of the communication media, create the personnel's Kansei image vocabulary for related products (this vocabulary is usually an adjective describing the product form design). Similarly, collect product pictures of the same type as the product.Filter adjectives and pictures, sort out representative adjective pairs and representative samples, and construct the Kansei semantic space and sample space.
(2) Semantic difference investigation.Compile a representative sample Kansei survey experimental questionnaire and invite industry-related practitioners to rate the adjective pairs under each representative sample in the form of a scoring scale.Statistical software is used to count the average scores of adjective pairs under each sample to test the reliability and validity of the questionnaire results.
(3) Semantic factor analysis.The purpose of semantic factor analysis is to use a few factors to describe the relationship between multiple variables and summarize the Kansei cognition of personnel.Using statistical software, the principal component analysis method is used to extract the common factors; the number of factors is judged by outputting the gravel graph and the principal component characteristic value.Under normal circumstances, the component with a characteristic value greater than 1 is taken as a common factor as a summary result of the Kansei cognition of the personnel.

Determination of design requirements based on rough AHP-QFD
The emotional fusion design of laser additive manufacturing equipment should be designed around user needs to meet the pleasant requirements of the equipment.However, because the specific feelings of the operators are different, in the actual design process, the relative importance of the factors must be determined, and the best Kansei factors recognized by most operators must be found to optimize the design as the focus of the design.
Quality function deployment (QFD) based on meeting user needs reasonably transforms Kansei factors into product technical goals to clarify specific design requirements that should be focused on [25] .However, in the traditional QFD method, weight determination is usually calculated in the form of a summation.It is subjective and arbitrary.In this paper, rough number theory is combined with the AHP method to calculate the weight value of the human Kansei factor with the rough AHP to solve the problem of strong subjectivity in the process of QFD weight determination [26][27] .Suppose U is the universe which contains all the objects, and R is the set of classes that cover all the objects in U, where   Y is an arbitrary object of U.For any element in the set, Then, t R can be represented by a rough number   t RN R , which is determined by its corresponding lower limit and upper limit where where where tL gh x is the lower limit of Step 4. Calculate the rough weight g w of each criterion: Finally, the standard weight is obtained The relative weight of each Kansei factor is obtained from Eq. ( 16), and the best Kansei cognition of the operator of laser additive manufacturing equipment is determined.The Kansei factors (user needs) and their weights of personnel and the design needs are imported into the House of Quality, and the quantitative relationship between Kansei factors and design needs is analyzed to determine that the equipment meets the design needs of Kansei cognition.Let i n be the relative weight of the Kansei factor and ij a be the size of the correlation between the Kansei factor i and the design requirement j .Then the relative importance j  and importance j  of the design requirements can be expressed as:

Design factors determination based on independent axiom and GRA-TOPSIS
After determining the Kansei design requirements of laser additive manufacturing equipment, this paper transforms the equipment design requirements into functional requirements in axiomatic design, proposes the corresponding emotional fusion design requirements for functional requirements, and transforms them into design parameters of axiomatic design.Next, we analyze the mapping relationship between equipment function requirements and design parameters through the independence axiom and use the design parameters to determine the form elements that meet the requirements of equipment emotional fusion design.The axiom of independence avoids redundant design in the design process and simplifies the design content by judging the independence between functional requirements and design parameters [28] .The design equation of the independence axiom can be expressed as: where FR represents the functional requirements, A is the independence axiom design matrix, and DP is the design parameter.According to the different mapping relationships between functional requirements and design parameters, A has three forms: (1) Noncoupling matrix.It represents a one-to-one mapping relationship between functional requirements and design parameters.It meets the requirements of axiomatic design, and its expression is (2) Quasi-coupling matrix.It meets the requirements of axiomatic design.It can be decoupled and transformed into a noncoupling matrix by redefining the functional domain or physical domain.Its expression is (3) Coupling matrix.The coupling matrix does not meet the requirements of axiomatic design.It must be redefined to decouple the coupling matrix into a quasi-coupled matrix or a noncoupled matrix.Its expression is When the independent axiom test results show a noncoupling relationship or a quasi-coupling relationship, the emotional fusion design requirements of laser additive manufacturing equipment meet the Kansei design requirements of personnel for equipment.We construct and score a design requirement-form element evaluation matrix with equipment form elements as the evaluation object and emotional fusion design requirements as the evaluation index, and we solve the weight value of each form element.The equipment form elements that meet the psychological and emotional needs of personnel can be obtained.Based on this result, the GRA-TOPSIS method is used to solve the evaluation matrix.This method can fully use the sample data and avoid the disadvantage of the traditional TOPSIS method, which has high accuracy and credibility [29][30] .Suppose there are m morphological elements and n design requirements.They form the evaluation matrix K where ij K is a score for the degree to which each morphological element meets the design requirements.Then, the steps for applying the GRA-TOPSIS method to calculate the weight of morphological elements are as follows: Step 1.According to Eq. ( 24), the evaluation matrix K is normalized to form a standardized matrix V, where ij V is the standardized morphological element score.
Step 2. The maximum and minimum values of each column in the standardized matrix V are judged to form the set of positive ideal solutions and the set of negative ideal solutions shown in Eqs. ( 26)- (27).On this basis, the gray correlation coefficients between the morphological elements and the positive and negative ideal solutions are solved using Eqs.( 28)-(   12 , ,..., , max ( )   12 , ,..., , min ( ) min min max max max max min min max max max max where ρ is the resolution coefficient, which is usually 0.5. 1 Step 4. The gray correlation degree and the proximity degree are processed in a dimensionless manner and integrated into the Euclidean distance, as shown in Eqs.(32)-(34).max max where λ and γ are the coefficients reflecting the degree of attention between the form elements and the emotional fusion design requirements, and they take λ=γ=0.5.On this basis, calculate the final score of the equipment form elements, denoted According to Eq.( 35), the final scores of each form element of the equipment are calculated and compared.The form element with the highest score is the perceptual optimization design element of laser additive manufacturing equipment that meets the psychological and emotional needs of personnel.

Design optimization plan determination
After determining the morphological elements of the laser additive manufacturing equipment through the independent axioms, the information axioms of axiomatic design are used to simplify the uncertainty and uncertainty in the design process and other factors that affect the equipment design process, and the design is selected so that the scheme with the least information content is the optimal design scheme.The design information content of the information axiom is expressed by Eq. ( 36) where I is the content of design information contained in the design of a certain program, P is the probability that the program meeting the user's expectations will be successful, which is determined by the matching degree of the actual and feasible design quantity to meet user's needs and the amount of user demand design quantity expected.
Specifically, it is expressed as: Actual and feasible design quantity to meet user needs The amount of user demand design quantity expected P  (37) When the actual and feasible design quantity is higher than the user's expected demand design quantity, the probability of success of the design scheme can be considered high.According to Eq. (37), the higher the probability of success of the scheme is, the smaller the corresponding amount of information contained is.Under normal circumstances, the demand design quantity expected by a certain product user is usually fixed.Therefore, to meet the requirement of less program information, it is necessary to ensure that the amount of practical and feasible designs that meets the needs of users is large.The actual and feasible design quantity is usually related to the complexity of the design scheme, the operability and the satisfaction of user needs.

Case study
This article takes LDM4030 coaxial powder feeding laser additive manufacturing equipment as the specific optimization object (as shown in Fig. 3).According to the determined morphological elements and specific objects, multiple optimization schemes were designed.We analyze the optimization plan with the information axioms in the axiomatic design and select the optimal design plan.Finally, a Kansei comparison investigation between the optimal program and the optimized object is conducted to test the effect of the optimized program.This paper first constructed the semantic space and representative samples of laser additive manufacturing equipment.A total of 70 adjectives that directly or indirectly describe equipment were collected through multimedia methods, such as the internet, magazines, and newspapers.Invited researchers and operators engaged in laser additive manufacturing equipment to group and analyze the vocabulary through the KJ method, and finally, 15 adjectives were obtained.Additionally, we determined that their antonyms constitute a semantic space composed of 15 pairs of adjectives, as shown in Tab. 1.We collected the pictures of laser additive manufacturing equipment, screened out similar pictures, and obtained 12 representative pictures, as shown in Tab. 2.  Next, we determined the adjective pair that summarizes the psychological and emotional cognition of laser additive manufacturing equipment operators through factor analysis, that is, the user needs.To ensure the correlation between adjective pairs and the sample, the correlation degree is first checked before the factor analysis, and the words with a correlation degree less than 0.4 are removed.Tab. 4 shows the correlation coefficient of each adjective after reliability analysis.Because the correlation coefficient of Q12 is less than 0.4, the Q12 "natural-man-made" adjective pair is excluded.The variance interpretation results and gravel graph results of each adjective pair after factor analysis are shown in Tab. 5 and Fig. 4. The results in Tab. 5 show that the cumulative explanatory rate of the variance of the first four factors is 90.516%, and the percentages of variance of the four factors after rotation are all greater than 10%.According to the results of the gravel map, the eigenvalues of the first four points have greater differences, and the subsequent points have smaller differences, so the image has an obvious turning point at the fourth point.
These results show that the four factors can summarize all adjective pairs well.The load matrix of each adjective pair after factor analysis is shown in Tab. 6.The semantic space formed by the 14 groups of adjective pairs is composed of four types of factors, namely, humanity factors, professional factors, personality factors, and safety factors.So far, the emotional user needs of the operators of laser additive manufacturing equipment obtained through the Kansei semantic difference experiment can be summarized as humanity, professionalism, personality, and safety.To determine the best Kansei knowledge of laser additive manufacturing equipment operators, this paper compiled a rough AHP expert evaluation questionnaire and investigated seven senior operators of laser additive manufacturing equipment.Four of these operators have rich operating experience.
Step 1. Experts evaluate the relative importance of the criteria, form an evaluation matrix A and conduct a consistency test.As an example, the evaluation matrix of the first expert is the consistency of the evaluation matrix of the first expert is less than 0.1, which meets the consistency test requirements.
Step 2. Conduct an AHP survey and construct a group of pairwise comparison matrices.According to Eq. ( 8), the evaluation matrices of the seven experts were integrated, and the integrated evaluation matrix B was constructed as follows 7 7 9 5 2 3 3 5 5 5 4 3 4 5 6 Step 3. Transform each importance set into a rough set.According to Eqs. ( 9)-( 12), the rough value conversion and normalization were performed, and the constructed rough judgment matrix M was as follows The results in Tab.7 show that the safety factor, which is 0.3958, accounts for the highest proportion.This result shows that safety is the most important Kansei factor among the operators of laser additive manufacturing.
When conducting emotional fusion design, the effect of safety factors on the design should be first considered to ensure that the design results load the psychological cognition of most people.After that, the order of proportion was the humanity factor, professional factor, and personality factor.It was shown that the operator pays more attention to safety while agreeing with the human feeling.
Through the analysis of the perceptual factors of the operators of the laser additive manufacturing equipment the related characteristics of the equipment, the Kansei design requirements are resolved from the three aspects of professional needs, safety needs, and human needs.The relative importance weights and design requirements of the Kansei factors obtained after a rough AHP analysis are introduced into the House of Quality.▲ (1 point), ◯ (3 points) and • (5 points) are used to represent the weak, medium, and strong relationships between user needs and design requirements, respectively.The Kansei factor-design requirement QFD matrix was then constructed.
According to Eq. ( 17) and Eq. ( 18), the importance value of each design requirement is calculated, as shown in Fig.According to the results in Fig. 5, intelligence, monitoring visualization, safety in use and space comfort are relatively important design requirements.The contents of the table were consolidated and simplified with lower relative importance and similar importance.The distinctive personality is relatively low in importance and partly similar to the smart and elegant in content, so the two are merged into the smart and elegant design requirements.

个性因子
The contents of responsive and intelligent design requirements are similar, and the weight of responsiveness is lower than that of intelligent, so the two are merged into intelligent design requirements.The final design requirements for laser additive manufacturing equipment are intelligent, visualized, safe to use, comfortable space, sturdy and durable, simple interface, flexible and elegant.
The results of the design requirements of laser additive manufacturing equipment are transformed into the functional requirements of axiomatic design, named FR1 through FR7: FR1=intelligence, FR2=visualization, FR3=safe use, FR4=comfortable space, FR5=sturdy and durable, FR6=simple interface, FR7=smart and elegant.
For each functional requirement, the corresponding emotional fusion design demands are proposed as the design parameters of axiomatic design.The details are shown in Tab. 8. Mapping the functional requirements and the design requirements and testing the coupling of the mapping relationship according to the independent axiom, the judgment matrix is obtained as shown below.This matrix is a noncoupling matrix, which meets the requirements of axiomatic design. 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 Next, decompose the morphological elements of current common laser additive manufacturing equipment and take the morphological elements as the evaluation object and the design requirements as the evaluation indicators to construct a matrix of morphological elements-design requirements.Six experts are invited to score with 1, 3, and 5 points and calculate the importance of each form element. they take this result as a criterion for judging to meet the emotional fusion design elements of laser additive manufacturing equipment.Tab. 9 shows the decomposition results of the morphological elements.The first expert in this paper was taken as an example for solving and calculating the morphological element score.The evaluation result of the first expert is shown in Tab.

Discussion
According to Tab. 12, to meet the psychological and emotional needs of personnel, the Kansei engineering  The four Kansei factors obtained through Kansei semantic experiments are all important starting points for the design process of laser additive manufacturing equipment.Additionally, the combination of human emotion design is well realized in determining the needs of the user domain.In the design of the functional domain and the physical domain, the mapping relationship between human emotion-design and requirements-design elements is accurately realized through QFD and independent axioms.The design elements of the equipment are accurately integrated into emotional factors and realize the close integration of personnel emotional needs and equipment.
Therefore, the optimal design program (the first program) could well integrate the human emotion factors into the equipment design.It is of great significance for improving the design level of laser additive manufacturing equipment and improving the pleasantness of the use process.

Conclusion
This paper studies the fusion design of laser additive manufacturing equipment human-machine system cognitive interaction and human emotion, and it proposes a Kansei-AD emotion fusion design optimization method.Based on the analysis of the operator's emotional cognitive process and the representation of psychological and emotional needs, the emotional fusion design process of laser additive manufacturing equipment is divided into four parts: user domain, functional domain, physical domain, and process domain through axiomatic design.Based on the application of the Kansei semantic difference experiment to obtain the psychological and needs of personnel, the Kansei design requirements are calculated via the rough AHP and QFD methods.
Aiming at the problem of inaccurate mapping between emotional factors and design elements in traditional Kansei engineering design, the design needs are transformed into axiomatic design functional requirements, and the design requirements are transformed into axiomatic design parameters.Independent axioms are used to judge the relationship between the two coupling relationships.The GRA-TOPSIS method is used to construct a morphological elements-design requirements matrix to solve the optimal morphological elements.The optimal design plan with information axioms is screened to determine the final design plan.This paper takes LDM4030 coaxial powder feeding laser additive manufacturing equipment as an example to verify the effectiveness of the proposed method.The results show that the optimized design scheme has a larger amount of feasible demand design and less design information, which can fully meet the emotional needs of users of laser additive manufacturing equipment.It is of great significance for improving the emotional fusion design level and human-computer interaction efficiency of laser additive manufacturing equipment.
Concerning future research, this paper primarily takes the morphological interaction process as the research object to design emotional fusion.The interaction process of personnel will also have a certain effect on psychological and emotional factors.Therefore, the design of operational interaction emotion fusion will be the focus of future research.
[  Factor analysis lithograph results Figure 5 QFD matrix of Kansei factor-design requirement Kansei optimal design program of LDM4030 based on axiomatic design Comparison of actual and feasible demand design

Fig. 1
Fig.1 Cognitive process model of the laser additive manufacturing equipment operator

Fig. 2
Fig. 2 Emotional fusion and design optimization model of laser additive manufacturing equipment gray correlation coefficients between the morphological elements and the positive and negative ideal solutions, respectively.Step 3.According to Eqs. (30)-(31), calculate the gray correlation degree and closeness between the morphological elements and the positive and negative ideal solutions.They are represented by

Fig. 6
Fig. 6 Kansei optimal design program of LDM4030 based on axiomatic designAccording to information axioms, we first compared the satisfaction degree of the emotional needs of personnel between the two design optimization programs and the original program.Tab. 13 shows the comparison results of the satisfaction degree of the emotional needs of personnel.Both programs were designed according to the morphological elements of laser additive manufacturing equipment that meet the psychological and emotional needs of personnel, and these needs were met.

Figures Figure 1 2 3 LDM4030
Figures Tab.13Comparison results of the satisfaction degree of the emotional needs of personnel.Compared with those of the first program, the second program has smoother lines, but the structural design of the protective door is more complicated, and the process would be relatively complicated in the subsequent manufacturing process.The first program has certain advantages over the second program in terms of intelligent integration requirements, simplicity, and clarity of appearance.The main color of gray and black gave people a sense of heaviness, security, and higher intelligence.The use of warm color lines is smoother and softer, which have a higher affinity for.According to Eq. (37), the actual and feasible needs of the users that are met by the first program are greater than those of the second program, as shown in Fig.7, 12 PP  .The information content required in the design of the first program, 1 I is less than that of the second 2 I .It is judged that the first program is the optimal design based on the information axioms.
P2Fig.7Comparison of actual and feasible demand design 23] LIN Chao-qun,FAN Shu hai,CHEN Peng,ZHOU Ce.Axiomatic Design of Mixed Job Shop Layout Based on Digital Factory.Modular