Development of a Modular Design and Detachable Mechanism for Safety Support Products in Winter Ice Fishing

Abstract

To enhance the adaptability and disassemblability of winter ice fishing safety auxiliary products, a modular design approach was introduced during the design process. Axiomatic design (AD) and design structure matrix (DSM) were employed as the theoretical guidance and methodological framework. In the design process, the “Z-mapping” method was used to reanalyze the product’s requirements, functions, and structure, progressively decomposing the overall function and constructing a corresponding design matrix. This approach converted initial user requirements into detailed functional specifications and design parameters. Geometric correlation was used as the evaluation criterion, with values assigned to the design matrix, leading to the development of a correlation matrix for the design parameters of the winter ice fishing safety auxiliary product. System clustering techniques were then applied to optimize the distribution of matrix values, allowing for the identification of functional module areas. Based on these results, a modular design scheme was proposed. The findings indicate that the Kano-AD-DSM-based design strategy significantly improved the disassemblability of the winter ice fishing safety auxiliary product, which is crucial for protecting the safety of ice fishers, reducing physical exertion, and enhancing the ice fishing experience. Moreover, the multi-module design allows the product to be flexibly configured and upgraded based on varying operational needs and personalized user requirements, significantly improving its adaptability and practicality. This research not only provides new theoretical insights for the innovative design of winter ice fishing safety auxiliary products but also offers valuable references for the modular design of similar products.

1. Introduction

Ice fishing is a popular winter activity in cold-climate regions and has evolved into a multi-billion-dollar industry, making a significant contribution to local economies. Additionally, ice fishing holds cultural significance for many residents in these regions [1]. This activity requires participants not only to master advanced fishing techniques but also to possess solid expertise and emergency response skills to ensure safety while operating in challenging conditions, including low temperatures, slippery surfaces, and potentially unstable ice layers. Given the unique challenges of ice fishing, equipping participants with professional and fully functional safety equipment is essential to safeguarding their lives. However, current market offerings for ice fishing safety products are limited in quantity and exhibit significant deficiencies in both design concepts and practical effectiveness, making them insufficient to address the diverse safety challenges encountered during ice fishing. Particularly, existing products have substantial room for improvement in preventing accidental falls into icy waters and enabling efficient rescues. This underscores the need to analyze the demands for ice fishing safety products, identify risk points in real-world usage scenarios, and redesign and develop products better suited to the safety needs of ice fishing.

Detachable design focuses on enhancing the disassembly performance of products by thoroughly considering the interconnectivity of components. Easier disassembly increases the number of recyclable parts, thus improving product utilization and remanufacturing potential. In existing research, many scholars have combined detachable design with modularization. For instance, Huang Yongjia et al. applied modular design theory to air conditioner module partitioning and combined it with a detachable design to facilitate easy cleaning [2]. Harivardhini et al. proposed a new model for estimating workload during the early stages of the product design process by integrating Das’s Disassembly Effort Index (DEI) with the disassembly evaluation model developed by Kroll and Hanft. This model derives the disassembly difficulty score from the workload of routine disassembly tasks and reallocates tasks based on these scores [3]. Smith et al. applied the concept of modular design to the grouping of product components. By employing a recursive principle for module selection, they reduced the number of redirections required for component removal. This approach facilitates the parallel disassembly of interlocked or obstructed components, thereby reducing disassembly steps, time, and resource waste [4]. Axiomatic design is a systematic design methodology aimed at optimizing the design process and enhancing design quality and efficiency through scientific principles. This approach establishes a mapping relationship between functional requirements and design parameters, making the design process more systematic and quantifiable while also contributing to the optimization of complex system structures. The design structure matrix (DSM) is a matrix-based representation used to analyze and manage relationships between elements in complex systems. It provides a compact and intuitive means of visualizing dependencies between components, tasks, or parameters within a system, thereby facilitating the optimization of the design process and minimizing unnecessary iterations. In modular design research, axiomatic design (AD) and design structure matrix (DSM) have been widely applied in product and process modularization. For example, Park et al. integrated axiomatic design (AD) and DSM to optimize functional decomposition and modularization. This approach was validated in automotive applications, enhancing the efficiency of the design and manufacturing processes [5]. Kong Peipei et al. proposed an axiomatic design-based product framework model to decompose and map CNC micro-grinders and create structural diagrams. Tian Kunxiao et al., using an air-defense missile as an example, employed Quality Function Deployment, AD, and DSM to construct an integrated relational matrix, achieving traceability in modular design processes [6]. Bekdik et al. integrated dependencies in the design structure matrix (DSM) with modularization and applied them to the design process of Danish façades. By visualizing the design and development process, they created more possibilities with limited resources [7]. These studies demonstrate that axiomatic design aids designers in clearly defining research objectives, decomposing product functions step by step, determining corresponding design parameters, and providing a scientific basis for design decisions.

This study focuses on ice fishing safety auxiliary products, aiming to ensure user safety while enhancing the enjoyment of the ice fishing experience. At the early design stage, axiomatic design and the design structure matrix (DSM) are introduced as core methods and theoretical frameworks to balance safety and practicality. By integrating axiomatic design with DSM, an innovative modular design approach is proposed that ensures both safety and practicality from the outset. Through a thorough analysis of the interrelationships among design parameters, the product is decomposed into modular components. The research leverages multi-module design and detachable studies to enhance product modularity and flexibility, allowing users to easily adjust or replace components according to different ice fishing scenarios and requirements. This not only ensures convenience and maintainability but also extends product lifespan and reduces resource waste. The highly modular and flexibly configurable safety products provide reliable protection during ice fishing, promoting the sustainable development of this activity.

2. Design Model Based on Kano-AD-DSM

This study adopts axiomatic design (AD) and DSM as guiding methods and theoretical frameworks for optimizing the design of ice fishing safety auxiliary products. Axiomatic design provides a systematic approach to optimize the design process and improve design quality and efficiency [8]. The design structure matrix can effectively help identify the coupling relationships within a system, optimize the design process, reduce design changes and iterations, and enhance decision-making efficiency [9]. The specific design process is as follows: First, the Kano model is used to qualitatively classify user requirements for ice fishing safety auxiliary products, which are then incorporated into the AD model. Next, the “Z-mapping” method is applied to decompose the overall functionality of the product into sub-functions, specifying the design parameters corresponding to each sub-function and establishing the axiomatic design matrix.

However, the existing AD matrix has limitations in describing the interrelationships among design parameters within the same domain. DSM, by contrast, provides a more intuitive representation of these interdependencies. Therefore, the AD matrix is converted into a DSM, with geometric correlation as the evaluation criterion, to quantitatively populate the matrix and construct a detailed DSM based on the relationships among design parameters. Subsequently, system clustering is applied to the DSM for analysis, identifying the module assignments of each design parameter and laying the foundation for modularization. Finally, based on the modular partitioning results, a modular design scheme for ice fishing safety auxiliary products is developed. The modular design process for winter ice fishing safety assistance products is shown in Figure 1.

3. Design Solution for Ice Fishing Safety Auxiliary Product

3.1. Kano Model Solution

3.1.1. User Requirement Collection

Common methods for collecting user requirements include surveys, observations, user interviews, scenario-based approaches, and literature reviews [10]. To address the current safety needs of ice fishing activities, we conducted field observations and interviews with ice fishing enthusiasts. Observations were carried out randomly near frozen lakes and rivers, recording their use of safety auxiliary products, operational processes, actions under varying ice conditions, and interactions with companions. After observing 20 ice fishers, we compiled the recorded information and conducted in-depth interviews with 10 representative participants.

The collected user requirements were categorized and organized using the KJ method (Affinity Diagram). This method grouped vague and non-specialized language based on correlations, transforming ambiguous raw requirements into concrete design needs. After applying the KJ method, we integrated the analysis of user behavioral patterns and constructed a hierarchical list of user requirements for ice fishing safety auxiliary products, as shown in

Table 1. Hierarchical list of user requirements for ice fishing safety auxiliary product.

Coding	Requirements	Coding	Requirements	Coding	Requirements
B1	Safety assurance	B6	Quick installation	B11	Easy cleaning
B2	Lighting	B7	Anti-slip	B12	Easy operation
B3	Ice fishing functionality	B8	Low-temperature adaptability	B13	Aesthetic design
B4	Easy maintenance	B9	Reasonable pricing
B5	Portability	B10	Stability

.

3.1.2. User Requirement Analysis

The KANO model, proposed by Professor Noriaki Kano of the University of Tokyo in the late 20th century, is an effective tool for analyzing the nonlinear relationship between product performance and user needs based on user satisfaction. It provides both qualitative and quantitative analysis of user requirements [11]. By analyzing users’ satisfaction with a product, the model helps guide product feature upgrades. The KANO model categorizes user needs into five types based on satisfaction and quality characteristics: Must-be Quality (M), One-dimensional Quality (O), Attractive Quality (A), Indifferent Quality (I), and Reverse Quality (R) [12]. The KANO model accurately captures user needs, adjusts product development direction, and ensures successful product positioning. Using KANO questionnaires, we conducted a deeper analysis of the importance of user needs for ice fishing safety assistance products. Given the unique and diverse requirements of ice fishing, which takes place in cold outdoor environments, the survey process is inherently complex. To ensure the accuracy of the survey results, we segmented the user groups into ice fishing enthusiasts, safety supervisors (or companions), and product maintenance personnel, distributing the questionnaires in a 6:3:1 ratio. A total of 200 questionnaires were distributed, and by combining the results with the KANO evaluation table, we conducted a detailed analysis of the attributes of user needs and clearly distinguished the KANO attributes for each requirement.

To determine the attributes and weights of requirement elements, the Better–Worse coefficient analysis method was used [13]. Using Formulas (1) and (2), the user satisfaction improvement coefficient T′ and the satisfaction decline coefficient W’ were calculated. The survey results were processed according to the scale and computed, and the classification results of functional attributes are presented in

Table 2. Analysis results of functional attributes for ice fishing safety auxiliary product.

Functional Requirements		A	M	O	I	R	Categorization	T’	W’
Safety assurance	B1	30	121	22	0	0	M	0.301	−0.827
Lighting	B2	33	25	29	88	0	I	0.354	−0.309
Ice fishing functionality	B3	50	92	11	20	0	M	0.353	−0.058
Easy maintenance	B4	35	29	101	10	0	O	0.777	−0.743
Portability	B5	25	45	83	22	0	O	0.617	−0.731
Quick installation	B6	102	39	31	10	0	A	0.731	−0.385
Anti-slip	B7	40	38	98	5	0	O	0.762	−0.751
Low-temperature adaptability	B8	20	42	25	92	0	I	0.251	−0.374
Reasonable pricing	B9	45	32	10	89	0	I	0.313	−0.188
Stability	B10	22	141	18	0	0	M	0.221	−0.878
Easy cleaning	B11	80	33	20	13	0	A	0.685	−0.363
Easy operation	B12	26	17	85	15	1	O	0.776	−0.713
Aesthetic design	B13	92	31	11	10	0	A	0.363	−0.685

.

$$T^{’}={\displaystyle \frac{A+O}{A+M+O+I}}$$

(1)

$$W^{’}=-{\displaystyle \frac{M+O}{A+M+O+I}}$$

(2)

After qualitative and quantitative analysis of user requirements, the findings were as follows:

• Must-be Quality (M): Safety assurance, stability, and ice fishing functionality.
• Attractive Quality (A): Quick installation, easy cleaning, and aesthetic design.
• One-dimensional Quality (O): Easy maintenance, portability, anti-slip, and easy operation.
• Indifferent Quality (I): Lighting, low-temperature adaptability, and reasonable pricing.

Since Indifferent Quality (I) does not significantly impact user satisfaction during the design phase, this study focuses on Must-be Quality (M), One-dimensional Quality (O), and Attractive Quality (A) as the starting points for subsequent design research.

3.2. Solution Based on Axiomatic Design

Axiomatic design is a design decision-making theory based on logical and rational thinking processes, proposed by Professor Suh N.P. [14]. In axiomatic design, the design space is divided into four domains: the customer domain, functional domain, physical domain, and process domain. Each domain represents a set of data, namely customer attributes (CAs), functional requirements (FRs), design parameters (DPs), and process variables (PVs), respectively [15]. The mapping between the functional, physical, and process domains follows a hierarchical “zigzag mapping” from higher to lower levels. In adjacent domains, the left domain represents the objectives to be achieved, while the right domain represents the means to achieve these objectives, ensuring the independence of functions and structures at each level. However, the mapping in axiomatic design only reflects the relationships between domains and does not indicate the means by which the objectives in the left domain are achieved. The mapping relationships among the domains in axiomatic design are illustrated in Figure 2.

Axiomatic design is primarily based on two core axioms: the Independence Axiom and the Information Axiom. The Independence Axiom mandates that functional requirements (FRS) remain mutually independent in the design process to reduce coupling and complexity. The Information Axiom focuses on minimizing the information content in the design, ensuring simplicity and system stability [16]. The mapping relationship between the functional domain and the physical domain can be expressed as follows:

$$FR=ADP$$

(3)

where $FR$ represents the vector of functional requirements, with elements denoted as ${FR}_i$; $DP$ denotes the vector of design parameters; and $A$ is the design matrix that maps $FR$ to $DP$. The specific design expression is given in Equation (4), where $n$ and $m$ represent the number of functional requirements and design parameters, respectively.

$$\left\{\begin{array}{c}{FR}_1\\ \begin{array}{c}{FR}_2\\ \begin{array}{c}⋮\\ {FR}_n\end{array}\end{array}\end{array}\right\}=\left[\begin{array}{ccc}{A}_{11}& A_{12}& \begin{array}{cc}\cdots & A_{1m}\end{array}\\ A_{21}& A_{22}& \begin{array}{cc}\cdots & A_{2m}\end{array}\\ \begin{array}{c}⋮\\ A_{n1}\end{array}& \begin{array}{c}⋮\\ A_{n2}\end{array}& \begin{array}{cc}\begin{array}{c}\ddots \\ \cdots \end{array}& \begin{array}{c}⋮\\ A_{nm}\end{array}\end{array}\end{array}\right]\left\{\begin{array}{c}{DP}_1\\ \begin{array}{c}{DP}_2\\ \begin{array}{c}⋮\\ {DP}_m\end{array}\end{array}\end{array}\right\}$$

(4)

The design requirements obtained through qualitative and quantitative analysis using the Kano model are integrated into the axiomatic design process. Using the “Z-shaped mapping” mechanism between the functional domain and the physical domain, user requirements are progressively decomposed. For example, the requirements for ice fishing can be broken down into functional needs such as ice breaking, ice drilling, and drilling depth. Corresponding structural design parameters are then selected based on these requirements. Specifically, the ice-breaking requirement corresponds to the structural parameter of the drill bit. This process results in the functional decomposition diagram of the ice fishing safety auxiliary product, as shown in Figure 3.

3.3. Solution with DSM

The design structure matrix (DSM) is an N-order matrix introduced by Donald Steward in 1981 for analyzing information flow [17]. The core of the DSM lies in its ability to visually represent and analyze the dependencies among the components or activities of a system in matrix form, thereby optimizing processes and reducing unnecessary complexity. Ice fishing safety auxiliary products are complex systems with multiple elements, and the DSM can effectively identify and optimize the dependencies among their components, simplifying the structural layout and improving design efficiency and product performance.

When axiomatic design (AD) is employed as the guiding framework for designing ice fishing safety auxiliary products, requirement analysis and functional decomposition of the device are required to establish the axiomatic design matrix. However, traditional AD matrices only reflect the mapping relationships between functional requirements (FRs) and design parameters (DPs) but fail to capture the interdependencies among design parameters. The DSM offers an intuitive representation of the coupling relationships between design parameters. Hence, the AD-DSM can more comprehensively reflect the mapping and coupling relationships between design parameters for ice fishing safety auxiliary products.

The DSM facilitates modular division based on the coupling relationships within the product structure [18,19,20], but it does not explicitly detail the specific correlations among design parameters. By incorporating geometric correlations, the design matrix can be transformed into a DSM to more accurately display the specific correlations and interrelationships among design parameters, optimizing the product’s modular design [21]. The specific construction process is as follows:

(1)

Develop an axiom-based design for ice fishing safety auxiliary products based on user needs.

(2)

Replace the DP_S with the highest correlation to the FR_S in each row of the axiom design matrix with the corresponding functional items, resulting in the design transition matrix for the ice fishing safety auxiliary product.

(3)

Perform appropriate transformations on the rows and columns of the design transition matrix, so that elements corresponding to the same design parameters are placed on the diagonal. Adjust the same design parameters to the lower triangular part of the matrix, thereby converting the feedback information into feedforward information, resulting in the DSM for the ice fishing safety auxiliary product.

(4)

Set the design parameter correlation evaluation criteria and assign values to the DSM for ice fishing safety auxiliary products based on geometric correlation.

The construction process is illustrated in Figure 4.

Considering the product design requirements and recognizing that general connecting components such as bolts and nuts have minimal impact on the study of detachable design for ice fishing safety auxiliary products, these components were excluded from the analysis. As a result, 23 structural parameters were retained. This study therefore focuses on these 23 structural parameters as the research objects and constructs a design matrix to map the functional–structural requirements of ice fishing safety auxiliary products, as shown in

Table 3. Design matrix for ice fishing safety auxiliary products.

		DP1		DP2				DP3			DP5					DP7				DP8		DP9	DP10
		11	12	21	22	23	24	31	32	33	51	52	53	54	55	71	72	73	74	81	82	91	101	102
FR1	11	X	X									X		X						X			X	X
	12	X	X									X		X						X			X	X
FR2	21	X		X													X		X	X				X
	22	X	X		X												X		X	X				X
	23			X		X										X	X	X		X
	24	X		X		X	X									X	X			X
FR3	31					X	X	X	X	X												X
	32		X	X	X			X	X	X	X	X		X	X							X	X	X
	33		X						X	X	X	X	X	X	X							X	X	X
FR5	51	X	X	X					X	X	X	X				X	X				X			X
	52		X						X	X	X	X				X	X				X			X
	53									X	X	X	X	X		X	X						X	X
	54		X	X	X							X		X	X								X	X
	55				X				X				X		X								X	X
FR7	71	X	X		X	X	X		X	X	X	X			X	X	X	X					X	X
	72				X	X	X			X	X	X	X	X		X	X	X					X	X
	73		X											X		X	X	X
	74		X					X	X					X					X
FR8	81				X										X				X	X			X	X
	82										X				X				X	X	X
FR9	91		X			X			X						X	X						X
FR10	101	X				X			X	X								X					X	X
	102			X	X	X			X		X	X	X								X		X	X

.

According to design step (2), the design parameters in the design matrix are adjusted to obtain the design transition matrix, as shown in

Table 4. Design transition matrix for ice fishing safety auxiliary product.

		DP1		DP2				DP3			DP5					DP7				DP8		DP9	DP10
		11	12	21	22	23	24	31	32	33	51	52	53	54	55	71	72	73	74	81	82	91	101	102
DP11	FR11	X	X									X		X						X			X	X
DP12	FR12	X	X									X		X						X			X	X
DP21	FR21	X		X													X		X	X				X
DP22	FR22	X	X		X												X		X	X				X
DP23	FR23			X		X										X	X	X		X
DP24	FR24	X		X		X	X									X	X			X
DP31	FR31					X	X	X	X	X												X
DP32	FR32		X	X	X			X	X	X	X	X		X	X							X	X	X
DP33	FR33		X						X	X	X	X	X	X	X							X	X	X
DP51	FR51	X	X	X					X	X	X	X				X	X				X			X
DP52	FR52		X						X	X	X	X				X	X				X			X
DP53	FR53								X	X	X	X	X	X		X	X						X	X
DP54	FR54		X	X	X							X		X	X								X	X
DP55	FR55				X								X	X	X								X	X
DP71	FR71	X	X		X	X	X		X	X	X	X			X	X	X	X					X	X
DP72	FR72				X	X	X		X	X	X	X	X		X	X	X	X					X	X
DP73	FR73		X													X	X	X
DP74	FR74		X					X						X					X
DP81	FR81				X				X					X					X	X			X	X
DP82	FR82										X			X					X	X	X
DP91	FR91		X			X										X						X
DP101	FR101	X				X			X	X					X			X					X	X
DP102	FR102			X	X	X			X		X	X	X								X		X	X

.

According to design step (3), the design transition matrix is converted into the design structure matrix. Since the design structure matrix is a symmetric matrix, the dependency values of the design parameters on the diagonal are the same as those of the corresponding parameter pairs. Therefore, either the upper or lower triangular part of the matrix can be used to represent the entire matrix, resulting in an upgraded and simplified design structure matrix, as shown in

Table 5. Simplified design matrix for ice fishing safety auxiliary product.

		DP1		DP2				DP3			DP5					DP7				DP8		DP9	DP10
		11	12	21	22	23	24	31	32	33	51	52	53	54	55	71	72	73	74	81	82	91	101	102
DP1	11	X
	12	X	X
DP2	21	X		X
	22	X	X		X
	23			X		X
	24	X		X		X	X
DP3	31					X	X	X
	32		X	X	X			X	X
	33		X						X	X
DP5	51	X	X	X					X	X	X
	52		X						X	X	X	X
	53								X	X	X	X	X
	54		X	X	X							X		X
	55				X								X	X	X
DP7	71	X	X		X	X	X		X	X	X	X			X	X
	72				X	X	X		X	X	X	X	X		X	X	X
	73		X													X	X	X
	74		X					X						X					X
DP8	81				X				X					X					X	X
	82										X			X					X	X	X
DP9	91		X			X										X						X
DP10	101	X				X			X	X					X			X					X
	102			X	X	X			X		X	X	X								X		X	X

.

After decoupling the design matrix for ice fishing safety auxiliary products and establishing the DSM following the process in Figure 4, values are assigned based on geometric correlation [22]. The physical relationships between design parameters in terms of spatial and geometric connections are referred to as geometric correlation, which includes physical connections, perpendicularity, assembly processes, dimensions, and other physical relationships. Based on reference [23], the correlation evaluation criteria for the design parameters of the ice fishing safety auxiliary product are established from the perspectives of structural correlation, geometric relationship, functional relevance, and evolutionary correlation degree. Quantitative constraints are applied to the correlated design parameters, and the evaluation criteria are shown in

Table 6. Structural tightness evaluation criteria.

Structural Correlation	Score α1
Tightly connected by welding or similar methods, non-dismountable	10
Connected by interference fit or similar methods, difficult to disassemble	8
Connected by threading or similar methods, relatively difficult to disassemble	6
Quick-release structure connection, relatively easy to disassemble	4
Connected by ordinary contact methods, easy to disassemble	2
No connection relationship	0

,

Table 7. Geometric correlation evaluation criteria.

Geometric Relationship	Score α2
Direct geometric relationship: for example, parallelism or other strict geometric relationships	8–10
Geometric relationship: an indirect geometric relationship exists	1–7
No direct geometric relationship	0

,

Table 8. Functional correlation evaluation criteria.

Functional Relevance	Score α3
Strong relationship in jointly achieving certain functions	10
Relatively strong relationship in jointly achieving certain functions	8
Average relationship in jointly achieving certain functions	6
Relatively weak relationship in jointly achieving certain functions	4
Very weak relationship in jointly achieving certain functions	2
No functional relationship	0

and

Table 9. Evolutionary correlation degree evaluation criteria.

Evolutionary Relevance	Score α4
If one component is upgraded, the other must be upgraded	10
If one component is upgraded, the other is recommended to be upgraded	6
If one component is upgraded, the other can be upgraded	3
If one component is upgraded, the other is not recommended to be upgraded	0

. Specifically, the score α1 in Table 6 reflects the connection strength between components, ranging from non-dismountable connections (highest score) to no connection (lowest score). These scores are obtained through a qualitative assessment of connection methods such as welding, interference fit, or threading, considering their impact on the product’s performance. The score α2 in Table 7 represents the geometric relationship between components, with a score of 10 indicating a strict geometric relationship (e.g., parallelism), and lower scores indicating weaker or no geometric relationship. These scores are determined through a geometric analysis of the relative positions and orientations of the components. The score α3 in Table 8 is used to evaluate the contribution of each component to achieving the overall function of the product. The scores are determined by analyzing the system’s functions and considering how the performance of each component affects the overall functionality, with higher scores indicating a stronger functional relationship. The score α4 in Table 9 is used to evaluate the impact of upgrading one component on the potential for upgrading other components. A score of 10 indicates that upgrading one component requires upgrading another, while lower scores reflect weaker dependencies between components. The specific scores are determined through an analysis of the component lifecycle and the interdependencies during the upgrade process.

According to the above evaluation criteria, the comprehensive correlation among design parameters of the ice fishing safety auxiliary products can be calculated as follows:

(1)

Let C represent the comprehensive correlation among the product components in the four aforementioned aspects. The formula for calculating the comprehensive correlation of the design parameters of the ice fishing safety auxiliary products is as follows:

$$C=C_1+C_2+C_3+C_4$$

(5)

(2)

Define the weights of structural correlation, geometrical relationships, functional relevance, and evolutionary correlation for ice fishing safety auxiliary products as $Q_1$, $Q_2$, $Q_3$, and $Q_4$, respectively. In the modularization of ice fishing safety auxiliary products, structural correlation has the greatest impact on module division. When two components are permanently connected (such as welding or interference fit), they cannot be separated and must belong to the same module. Therefore, Q₁ is assigned the highest weight. Geometrical relationships reflect the geometric constraints between components (such as parallelism or precise alignment), which significantly influence the overall integrity of the module. However, compared to structural connections, geometrical constraints allow some flexibility. Hence, Q₂ is assigned a weight lower than Q₁. Functional relevance evaluates the synergistic effect between components, but not all functionally related components must be tightly connected structurally. Therefore, Q₃ is assigned a moderate weight. Evolutionary correlation measures whether the upgrade of one component requires synchronous adjustments of other components. While important for long-term adaptability, it has less impact on the initial module division. Therefore, Q₄ is assigned the lowest weight. Based on the above, it can be concluded that Q₁ > Q₂ > Q₃ > Q₄. Considering expert consultations and engineering practice, the final weight set is determined as follows:

$$Q=\left\{Q_1,Q_2,Q_3,Q_4\right\}=\left\{0.4, 0.3, 0.2, 0.1\right\}$$

(3)

Define the rating set for ice fishing safety auxiliary products as $\alpha =\left\{{\alpha }_1,{\alpha }_2,{\alpha }_3,{\alpha }_4\right\}$, where ${\alpha }_1,{\alpha }_2,{\alpha }_3,{\alpha }_4$ represent the evaluation values of the four criteria, such as structural tightness. The correlation under each individual criterion can thus be calculated using the following formula:

$$C_n=Q_n\times {\alpha }_n$$

(6)

(4)

In summary, the formula for calculating the comprehensive correlation C among the components of ice fishing safety auxiliary products is as follows:

$$C={C_1+C_2+C_3+C_4=Q}_1\times {\alpha }_1+Q_2\times {\alpha }_2+Q_3\times {\alpha }_3+Q_4\times {\alpha }_4$$

(7)

According to the scoring criteria outlined in Table 6, Table 7, Table 8 and Table 9, the theoretical maximum score for a single evaluation criterion is 10, while the minimum score is 0. Based on this scoring range, a threshold of 5 is established as the benchmark for determining the strength of the correlation between design parameters. When the total correlation value between two design parameters exceeds 5, it indicates a high degree of correlation between them. Such a high level of correlation suggests that the two parameters exhibit strong interdependence in the design process and require synchronized adjustments or optimizations. For instance, when two parameters demonstrate high correlation, they can be considered for inclusion in the same module, or their interdependent effects can be prioritized during design optimization. Conversely, when the total correlation value is less than or equal to 5, it reflects a low degree of correlation, implying minimal mutual influence between the two parameters. In such cases, the parameters can be optimized independently or assigned to different modules for management. Therefore, setting the threshold of the design parameter correlation value at 5 ensures a more scientific and systematic evaluation of the correlation between the design parameters of ice fishing safety auxiliary products.

Based on the transformation method outlined in Figure 4, the matrix decoupling is performed to obtain the design structure matrix (DSM) for ice fishing safety auxiliary products. The design parameters in the DSM are then assigned values using the correlation evaluation criteria from Table 6, Table 7, Table 8 and Table 9. For example, for DP11 and DP12, which involve the use of buckles and safety belts, the structural relationship is a threaded connection, which is relatively difficult to disassemble. Therefore, the value of Q₁ is 6. In terms of geometric and positional relationships, the two parameters exhibit a direct relationship, so Q₂ is set to 10. The functional relationship between them is strong, as both parameters jointly ensure safety, so Q₃ is set to 10. Regarding evolutionary correlation, when the safety belt is upgraded, it is recommended that the buckle also be upgraded, so Q₄ is set to 6. Therefore, according to Equation (7), the correlation C(11,12) between design parameters DP11 and DP12 is calculated as follows:

C(11,12) = (6 × 0.4) + (10 × 0.3) + (10 × 0.2) + (6 × 0.1) = 8

This process is repeated for all parameter pairs, resulting in the design structure matrix for the ice fishing safety auxiliary product with correlation values assigned, as shown in

Table 10. Design structure matrix of the ice fishing safety auxiliary product.

DP		DP1		DP2				DP3			DP5					DP7				DP8		DP9	DP10
		11	12	21	22	23	24	31	32	33	51	52	53	54	55	71	72	73	74	81	82	91	101	102
DP1	11
	12	8
DP2	21	6.4
	22	2	7.3
	23			6.2
	24	2.3		4.8		8
DP3	31					7	4.3
	32		6.5	5.2	2.8			8.4
	33		1.2						1
FR5	51	6.4	5.4	4.2					1	2.4
	52		5.6						1	0.2
	53								1	6.4	2.2	0.8
	54		6.2	5.6	7.6							7
	55				2.3								4.8	0.8
FR7	71	4.8	6.2		5.6	7	5.2		6	2.4	5	1.8			6.4
	72				4.3	5.4	6.2		6.6	2.4	7	1.8	5.4		6.6	7.6
	73		4.2													7.6	7.6
	74		5.8					5.8						6.6
FR8	81				1.9				1					3.6					4.6
	82										1.2			2.8					6.6	5.4
FR9	91		1.2			4.8										5.2
FR10	101	0.8				6.4			1.2	1.2					4.9			7.2
	102			1.2	1.2	1.2			1		1	1	1.2								4.3		3.2

.

4. Design Practice of Ice Fishing Safety Auxiliary Product

4.1. Clustering of Design Parameters

Using the systematic clustering method, the association distance was utilized to optimize the correlation matrix of the design parameters for the ice fishing safety auxiliary product. Design parameters with high correlation were grouped into the same category, ultimately generating the hierarchical clustering diagram of the design parameters, as shown in Figure 5.

Based on Figure 5, functional modules of the ice fishing safety auxiliary product were identified, including the following:

• Ice fishing module: DP₂₁ (Drill bit), DP₂₂ (Drill rod), and DP₂₃ (Blade);
• Support and stabilization module: DP₃₁ (support frame), DP₃₂ (fixing block), and DP₃₃ (base reinforcement);
• Safety module: DP₁₁ (buckle) and DP₁₂ (safety belt);
• Operation module: DP₈₁ (operation handle), DP₈₂ (main body shell), and DP₅₄ (accessory placement).

4.2. Design Example

4.2.1. Structure Module Display of Ice Fishing Safety Auxiliary Product

In the modular division of the ice fishing safety auxiliary product, the ice-breaking module is responsible for creating holes of specific diameter and depth in the ice surface, allowing anglers to place their fishing gear into the water for fishing. The support and fixation module is used to support and secure the device, ensuring its stability on the ice surface. The safety module is responsible for providing safety measures during the ice fishing process. The operation module controls the various functions and operations of the ice fishing safety auxiliary product, ensuring that anglers can easily use the product and fish effectively. These modules work together to provide ice fishing enthusiasts with a safe and efficient fishing experience.

In the design considerations for the ice fishing safety auxiliary product, it is essential to ensure the efficient stability of core functional modules, such as the ice-breaking module, ice fragment handling module, and support fixation module, to meet the basic needs of ice fishing. Additionally, the design of the safety module must be prioritized, incorporating safety measures like harnesses to ensure user safety. Furthermore, the convenience and ease of use of the operation module should also be considered. In terms of user needs, based on Kano’s model, an in-depth analysis was conducted to uncover potential user demands through must-have (M), attractive (A), and expected (O) requirements, identifying suitable design innovation points. Finally, in the design approach, ergonomics and color psychology were combined to optimize the appearance design of the device, improving user experience and reducing operational errors and discomfort caused by appearance design.

Based on the product component module division results and design considerations derived from the AD-DSM, the modeling was carried out using Rhino 7 software, and rendering was performed with KeyShot 10 software. The modeling process is shown in Figure 6, while the rendering produced with KeyShot is shown in Figure 7. The final design of the ice fishing safety assistance product is presented in Figure 8, and the structural module layout is shown in Figure 9.

4.2.2. Design Scheme and Validation

Based on the modular layout of ice fishing safety auxiliary products, the design practice was carried out by considering design elements such as the presentation of the product’s color, material, and finish (CMF) effects. By integrating modular structural innovations and undergoing multiple iterations of design and optimization, a final innovative design solution was developed, combining efficiency, safety, and convenience.

Compared to traditional ice fishing products, the ice fishing module design of the ice fishing safety auxiliary product offers higher efficiency and enhanced safety. This module is designed to break through the ice surface, creating a fishing hole for anglers, with rotating blades that effectively remove ice fragments to form a clean opening. Equipped with high-strength drill bits and blades, this module efficiently handles ice layers of varying thickness and hardness, ensuring both effectiveness and safety during the ice fishing process. In terms of stability, traditional ice fishing equipment often lacks proper fixation measures, making it prone to tilting or slipping due to uneven ice surfaces or external forces. To address this issue, the product’s support and stabilization module acts as a stabilizer, ensuring that the device remains steady and secure during operation. It incorporates a support frame, fixing blocks, and a reinforced base to prevent wobbling or tilting on the ice surface. The design prioritizes both stability and portability, allowing use in diverse locations. Additionally, the safety module, an essential component of the ice fishing safety auxiliary product, provides comprehensive protection by mitigating the risks associated with cold and slippery environments, where traditional ice fishing equipment often lacks protective measures. Featuring safety belts and buckles, this module immediately secures users in case of a fall, preventing them from plunging into the water. Moreover, during rescue operations, it provides rescuers with a secure anchor point, reducing the risk of falling into the water and enhancing overall rescue safety. Compared to traditional products, this safety module significantly improves safety during the ice fishing process. Furthermore, the operation module controls the functionality and operation of the entire device, comprising an operation handle and an accessory placement section. The operation handle allows users to control the ice-breaking and ice fragment removal modules, while the accessory placement section provides a designated space for fixing blocks, facilitating convenient usage. The design scheme of the ice fishing safety auxiliary product is shown in Figure 10.

4.2.3. Design Validation

The final design scheme was validated using the Likert five-point scale to obtain more precise and quantitative user feedback. In this scale, a score of −2 indicates “strongly dislike,” reflecting users’ extremely negative attitude towards the design scheme; a score of −1 signifies “difficult to accept,” indicating significant resistance to the scheme; a score of 0 represents “acceptable,” showing that users have no strong preference but are willing to adopt the design scheme; a score of 1 means “somewhat like,” reflecting a positive evaluation and some level of attraction; and a score of 2 denotes “strongly like,” demonstrating users’ high recognition and satisfaction with the design scheme.

To comprehensively assess user satisfaction with the final design scheme, a questionnaire was developed and distributed. A total of 250 questionnaires were distributed, and, after excluding invalid responses, 235 valid questionnaires were obtained, yielding a response rate of 94% (235/250). The distribution of user evaluations for the design scheme is shown in

Table 11. Distribution of user evaluations of the design scheme.

Score	Number of Participants	Proportion
−2 (strongly dislike)	10	4.26%
−1 (difficult to accept)	25	10.64%
0 (acceptable)	80	34.04%
1 (somewhat like)	95	40.43%
2 (strongly like)	25	10.64%

.

Statistical analysis of the data from these valid questionnaires yielded a final average score of 1.67. This score lies between “somewhat like” and “strongly like,” indicating that the user group holds a positive and favorable attitude towards the design scheme.

5. Discussion

This study proposes a modular design solution model based on the integration of the Kano, axiom design (AD), and design structure matrix (DSM) methods, aimed at addressing critical issues such as design fragmentation, high maintenance complexity, and inadequate adaptability in traditional ice fishing safety auxiliary products. The Kano model delves into user needs, ensuring that product functionality is reasonably partitioned and closely aligned with real-world usage scenarios. The AD theory applies an axiomatic approach to optimize design decisions, maximizing the rationality and feasibility of the design. The DSM method, from a systems engineering perspective, optimizes the structural relationships between modules, reducing design redundancy and coupling, thereby clarifying the functional module divisions of ice fishing safety auxiliary products and completing their multi-module design, achieving a high degree of modularity and configurability. Compared to traditional linear or experience-driven design methods, the integrated Kano-AD-DSM approach in this study makes the product design more scientific and precise, and significantly enhances the reusability, scalability, and maintainability of the design. Additionally, through the application of a modular strategy, this process can quickly respond to varying demands across different usage scenarios, support product customization and flexible upgrades, and significantly reduce design iteration costs, thereby accelerating product development cycles. This method not only ensures the organic integration of ice fishing and safety assistance functions but also grants the product higher adaptability, sustainability, and market competitiveness, providing strong theoretical support for the efficient design of ice fishing safety auxiliary products. Unlike traditional modular designs, which often prioritize standardization at the expense of adaptability, our approach strikes a strategic balance between standardization and customization, ensuring that the product efficiently performs both ice fishing and safety assistance tasks while maintaining structural integrity. This method aligns with the current global trends of circular economy and sustainable development and extends the application of modular design in ice fishing safety products. Unlike traditional modular design approaches that prioritize standardization at the expense of adaptability, our study strategically balances standardization and customization. This ensures that the product maintains structural integrity while efficiently performing both ice fishing and safety assistance functions, thereby addressing critical challenges that have not been adequately tackled in existing research. Furthermore, this study extends the application of modular design methodologies to the relatively underexplored domain of ice fishing safety assistance products, expanding the scope of modular design and filling a research gap in this field.

Based on the modular process approach, a highly efficient, safe, and detachable ice fishing safety auxiliary product has been developed, innovatively combining ice fishing functions with safety assistance features. This product breaks through the limitations of existing products, which typically focus on single functionalities. Compared to traditional ice fishing equipment, this product employs a multi-module design, including an ice-breaking module, a support and fixation module, a safety protection module, and an operation control module, ensuring that the product enhances ice-breaking efficiency while also providing stable support and safety assurance. The support and fixation module enhances the product’s stability on the ice surface, reducing the risk of slippage or tilting due to uneven ice or external forces. The safety protection module provides fall protection and rescue support through safety belts and buckles, improving the safety of both the user and the rescuer. Moreover, the product adopts a detachable modular design, allowing functional units to be independently replaced and maintained, which not only reduces maintenance costs but also effectively extends the product’s lifespan while minimizing resource waste.

To evaluate the effectiveness of the design scheme and its alignment with user experience, we conducted a rigorous quantitative assessment using the Likert scale. The results demonstrate that the modular design scheme significantly improved the product’s modularity and detachability while greatly optimizing the user operation experience and ensuring safety during ice fishing activities. This design is more closely aligned with the actual usage habits and needs of ice fishing enthusiasts. Unlike previous studies that mainly emphasized structural modularity, this study introduced user-centered evaluation metrics to ensure that modular improvements translate into practical benefits for the end users. This design approach aligns closely with the practical habits and needs of ice fishing enthusiasts, validating its feasibility and practicality, and providing a solid theoretical foundation and practical guidance for the subsequent design and development of ice fishing safety auxiliary products.

From the perspective of sustainable development, this modular design strategy contributes to extending the product’s lifecycle by simplifying repair and upgrade processes, thereby reducing resource waste and environmental pollution. It adheres to the principles of green design and circular economy. Moreover, modular design facilitates product customization and rapid iteration, meeting diverse market demands and promoting the sustainable development of the ice fishing safety auxiliary product industry.

In future research, we plan to conduct multiple rounds of clustering analysis on design parameters to identify more optimal modular division schemes, further enhancing the product’s detachability and reconfigurability. Additionally, we will proceed with the development of prototype designs and closely monitor feedback from users and experts. By focusing on user needs, we aim to continuously optimize the product’s usability and human–computer interaction performance. Through ongoing improvement, we are committed to making ice fishing safety auxiliary products safer and more convenient, effectively meeting the practical needs of ice fishing enthusiasts.