Agricultural Micro-Tiller Detachability Research and Multi-Module Design Development

Abstract

To improve the detachability performance and remanufacturing capability of existing agricultural micro-tillers, a modular design concept is introduced into the product design and development process, with Axiomatic Design (AD) and the Design Structure Matrix (DSM) serving as the methodological guidance and theoretical framework. In the design process, “Z-mapping” is used to reconstruct the demand/function/structure of the agricultural micro-tiller, decompose the total functions step by step, and establish a design matrix to transform the initial user requirements into specific functional indicators and design parameters. Geometric correlation is used as the design evaluation index to assign values to the matrix to establish a DSM for the correlations between agricultural micro-tiller design parameters. Using system clustering to optimize the distribution of matrix values, a total of five functional modules were identified to achieve a modular design scheme and design a prototype for agricultural micro-tillers. This design solution has significantly better disassembly performance than existing products, effectively enhancing the remanufacturing capability of existing equipment, proving the effectiveness of the Kano-AD-DSM-based design strategy, and providing a new theoretical reference for the innovative design of other small agricultural equipment.

1. Introduction

The rapid development of agricultural modernization has made small agricultural equipment the first choice for farmers’ daily farming operations. Many civil micro-tillers were introduced in Hungary, the United States, Japan, and other developed countries during the 1950s and 1960s. Agricultural micro-tillers are small, family-type agricultural machines, mainly relying on power-driven rotating parts for plowing and soil breaking. They are widely used in mountainous and hilly areas, and are characterized by their light weight, small size, ease of use, and simple structure. Yan G.Q. et al. pointed out that existing micro-tillers lack supporting apparatuses, have limited applications, and incur high maintenance costs [1]. Yang W. et al. identified poor operational performance, low efficiency, and high power consumption in paddy field operations using micro-tillers [2], and Chen G. et al. highlighted issues with handling and comfort in traditional micro-tillers [3]. In summary, existing micro-tiller equipment has numerous structural, intelligence-level, and maintenance-related problems that require reanalysis and redesign based on user needs.

Agricultural micro-tillers consist of many parts and have a complex structure. They are usually composed of a working system, frame, power unit, control system, and driving device, and are capable of performing tasks such as plowing, seeding, rototilling, and furrowing. These machines are suitable for challenging environments such as hilly and arid areas. Based on the Kano model, Chen G. et al. qualitatively and quantitatively addressed user requirements and used Quality Function Deployment (QFD) theory to map these onto engineering design requirements, making the design of agricultural machinery more user-friendly [4]. Lin Y. et al. designed an Ultra-Wideband (UWB) electric micro-tiller positioning system for complex greenhouse environments, solving the issue of frequent safety accidents with existing micro-tillers. This innovation met the needs of greenhouse rototilling operations and laid the foundation for unmanned micro-tillers [5]. Yang Y. et al. applied fuzzy mathematical comprehensive evaluation theory to improve and optimize existing equipment based on the characteristics of self-propelled micro-tillers [6]. Deng H.J. et al. used the five-step method to determine user requirements and optimized the styling, functionality, and mechanical structure of existing micro-tillers, guided by TRIZ theory [7]. Niu P. et al. developed a new agricultural micro-tiller powered by lithium batteries and brushless DC motors, which delayed the onset of white finger disease caused by vibration by 3.7 to 10.20 years through the detection and analysis of transmission vibration signals [8].

Detachable design aims to improve the disassembly performance of products, fully considering the linkage between parts. As disassembly is a crucial aspect of maintenance, enhancing the product’s disassembly capabilities also increases maintenance efficiency. Lu Zhong et al. developed a product disassembly modeling method focused on maintainability design, presenting a disassembly sequence plan for civil aircraft. Through optimizing the disassembly sequence, they improved the performance and, consequently, maintenance efficiency [9].

Modular design involves dividing a product’s components into separate modules based on specific criteria. Many scholars have integrated detachable design with modularity. Qin Yue et al. applied modular and detachable design to ship manufacturing, significantly improving production efficiency and hull assembly efficiency [10]. Zhu Y. et al. combined detachable and modular design in the structural design of panel wood furniture, enhancing the disassembly performance of existing products [11].

In the study of modular design, many scholars have applied Axiomatic Design (AD) and the Design Structure Matrix (DSM) in the modular design phase of products and design processes. Cheng X.F. et al. utilized AD to divide product functional areas and combined it with DSM to construct a product design association matrix. They used a topological clustering algorithm to obtain product module divisions [12]. Li J.F. et al. developed an innovative design method based on AD and Extenics, analyzing the total layer structure of a distributed hybrid electric tractor using AD. They established a design matrix and resolved contradictions in the innovative design of the assembly architecture using Extenics, verifying the method through simulation and a design prototype [13]. Fazeli et al. proposed a design innovation method based on AD-QFD-DSM to generate feasible design concepts by matrix mapping across different design domains, and they validated this method with a hand rehabilitation device design case [14]. Awasthi A. et al. proposed a methodology based on fuzzy AD for prioritizing sustainable transport projects and applied it to rank three sustainable transport projects in Luxembourg City [15]. Du et al. proposed a reuse design method based on AD theory and QFD, applying it to machine tool remanufacturing to verify its feasibility and effectiveness [16]. He et al., considering the functional, physical, and process domains of AD, studied the decomposition method of product infant failure relationship trees. They combined rough set and fuzzy TOPSIS methods, and demonstrated the method through a case study involving car infant failure, body noise, vibration, and roughness complaints [17]. Based on AD theory, designers can more easily conduct comprehensive analyses of user requirements, design solutions, processes, product structures, and design analysis, providing a theoretical foundation for pre-product design development.

Cheng et al. proposed an adaptive platform identification method based on AD and DSM, using an AD approach to model non-adaptive platforms and employing the DSM to identify adaptive and non-adaptive parameters in platforms [18]. Cheng Q. et al. hierarchically decomposed the product into the functional, physical, and process domains, transforming the design matrix into a DSM to achieve modular optimization, which they validated using indoor air conditioners [19]. Z.K. Li et al. introduced a hierarchical modular architecture design method that combines DSM with modularity, demonstrating its effectiveness in a case study involving a concrete sprayer [20]. Loureiro et al. proposed a method to combine DSM interdependencies with modularity and applied it to the design process of mass-customization products [21]. Bekdik et al. applied the combination of dependencies in the DSM and modularity to the design of a Danish facade, visualizing the design development process and creating more possibilities with limited resources [22]. Algeddawy et al. used hierarchical clustering to construct a product mechanism model for DSM, clustering product components into modules, and validated the approach with a 38-component body-in-white case [23].

In these studies, AD and DSM help designers to clarify the study’s purpose, decompose product functions in a step-by-step manner, determine the corresponding design parameters, and provide a scientific basis for product design decisions.

In summary, existing research on mini-tillers suffers from a narrow focus, approaching the subject from singular perspectives. This results in product designs that lack systematization and applicability, making it difficult to clearly express the dependencies and connections between various components.

The innovation of this study lies first in addressing issues such as installation difficulty and maintenance complexity through modularization. It also analyzes the product structure from the perspective of user needs, aiming to meet diverse user demands and improve the disassembly performance. In the early design stage, the disassembly and recyclability of product components are emphasized. Through considering the connections, similarities, and correlations between modules and parts, a DSM is constructed, dividing the product components into modules to achieve modular design for the micro-tiller.

This study focuses on the field of equipment disassembly and maintenance, utilizing industrial design techniques to reduce the maintenance time and extend the lifespan of agricultural mini-tillers. By enhancing the sustainability of these machines, this research contributes to the sustainable development of the industrial production sector.

2. AD-DSM-Based Lawn Mower Solution Model

In this study, AD and DSM serve as methodological guides to analyze the requirements and decompose the functions of agricultural micro-tillers. AD is used to establish an AD matrix. However, the existing AD matrix cannot adequately describe the inter-influential relationships within the same domain. DSM, on the other hand, can intuitively represent these relationships. Therefore, the AD matrix can be transformed into a DSM matrix to fully account for the influence of DPS on FRS, as well as the strength of relationships between design parameters.

AD, introduced by Professor Suh in 1990, is a design decision method that categorizes the design process into four domains: customer domain, functional domain, physical domain, and process domain. Each domain corresponds to a specific element: Customer Needs (CAS), Functional Requirements (FRS), Design Parameters (DPS), and Process Variables (PVS). The functional, physical, and process domains are decomposed hierarchically using “Z-mapping” from higher to lower levels to ensure the independence of functions and structures at each layer. The mapping relationships between these domains are shown in Figure 1, while the mapping relationship between the functional and physical domains is illustrated in Figure 2.

The design expression of the relationship between FRS and DPS is given by Equation (1):

FR = ADP

(1)

FR is the set of functional requirements, with its elements denoted FR_i; DP is the set of design parameters; and A is the design matrix that expresses the mapping relationship between functional requirements FR_S and design parameters DP_S. The design expression is represented by Equation (2), where n and m represent the number of FR_S and DP_S, respectively. The elements of matrix A are determined with Equation (3).

$$\left[\begin{array}{c}{FR}_1\\ \dots \\ {FR}_n\end{array}\right]=\left[\mathit{A}\right]\left[\begin{array}{c}{DP}_1\\ \dots \\ {DP}_m\end{array}\right]=\left[\begin{array}{ccc}{\mathit{A}}_{11}& \dots & {\mathit{A}}_{1m}\\ \dots & \dots & \dots \\ {\mathit{A}}_{n1}& \dots & {\mathit{A}}_{nm}\end{array}\right]\left[\begin{array}{c}{DP}_1\\ \dots \\ {DP}_m\end{array}\right]$$

(2)

$${\mathit{A}}_{ij}={\displaystyle \frac{\partial {FR}_i}{\partial {DP}_j}}$$

(3)

DSM is an Nth-order matrix used for analyzing information flow, introduced by Donald Steward in 1981. N represents the number of design elements, with each row or column indicating that a particular design element requires a specific association with other design elements, such as information exchange or material interaction. Agricultural micro-tillers are complex systems with many elements, but DSM allows for a direct and intuitive analysis of dependency links between these elements, reducing the complexity of the design process. In the DSM matrix example shown in

Table 1. Example of DSM matrix.

	A	B	C	D	E	F
A	A		X	X
B		B
C			C		X
D	X			D
E		X			E
F	X					F

, assume that "X" represents a specific correlation between design elements. design element A is influenced by design elements D and F, and receives input from design elements C and D.

In summary, AD and DSM served as the methodological guidance and theoretical framework for optimizing the design of agricultural micro-tillers. First, the Kano model was used to qualitatively classify user requirements, which were then imported into the AD design model. The total functions of the micro-tiller were decomposed step-by-step using “Z-mapping” in order to determine the design parameters corresponding to the user requirements, and the design matrix was established. Using geometric correlation as an evaluation criterion, values were assigned to the matrix to form a DSM based on the correlation among design parameters. System clustering analysis was then performed to determine the modular division of design parameters and form the initial design solution. The disassembly performance of this design scheme was verified through virtual reality technology, completing the modular design output for the agricultural micro-tiller. The modular design process for the agricultural micro-tiller is illustrated in Figure 3.

3. Agricultural Micro-Tiller Design Solution

3.1. Solution with Kano Model

Agricultural micro-tillers are primarily used in mountainous and hilly areas, where the environment is more complex, making the research relatively intricate. First, desktop research was conducted to identify current issues with agricultural micro-tillers, followed by the collection of user needs through field interviews, hands-on experience, and questionnaires [24,25,26,27,28,29,30]. A total of 200 questionnaires were distributed, with the user groups divided into farmers, agricultural machinery maintenance personnel, and agricultural machinery sales personnel in a ratio of 6:3:1. Of the distributed questionnaires, 103, 55, and 17 were recovered from the respective groups, resulting in a total of 175 valid questionnaires and a sample pass rate of 89.5%. The results of the survey were calculated based on a rating scale, and the attribution of each functional attribute is presented in

Table 2. Results of the functional attribute analysis for agricultural micro-tillers.

Functional Requirements		A	M	O	I	R	Categorization	Better	Worse
Adjustment of tillage depth	R1	21	123	31	0	0	M	0.297	−0.880
Environmental adaptation	R2	83	45	25	22	0	A	0.617	−0.400
Easy to move	R3	41	92	18	24	0	M	0.337	−0.629
Easy to repair	R4	30	33	99	13	0	O	0.737	−0.754
Guaranteed safety	R5	18	132	25	0	0	M	0.246	−0.897
Beautiful shape	R6	25	31	23	96	0	I	0.274	−0.309
Reasonable size	R7	97	42	25	11	0	A	0.697	−0.383
Easy to use	R8	35	29	101	10	0	O	0.777	−0.743
Working at night	R9	33	25	29	88	0	I	0.354	−0.309
Long-lasting power	R10	38	39	95	3	0	O	0.760	−0.766

.

The Kano model was used to map Customer Needs (CAS) to Functional Requirements (FRS). The “better” and “worse” qualities were calculated using Equations (4) and (5):

Better = (A + O)/(A + O + M + I)

(4)

Worse = −1(O + M)/(A + O + M + I)

(5)

After performing a qualitative and quantitative analysis of user needs, the following classifications were identified: Must-be Quality (M): adjustment of tillage depth, guaranteed safety. Attractive Quality (A): reasonable size, working at night. One-dimensional Quality (O): easy to repair, easy to use, long-lasting power. Indifferent Quality (I): beautiful shape, working at night. According to the Kano model’s demand ranking principle, Must-be Quality (M) > One-dimensional Quality (O) > Attractive Quality (A) > Indifferent Quality (I).

3.2. Solution with AD

Based on the preliminary desktop research, the design parameters of agricultural micro-tillers can be divided into two categories: common parts, such as washers and bolts, and non-common parts, such as the resistance tiller and rotary tiller. A breakdown diagram of key components of the agricultural mini-tiller is shown in Figure 4, and a detailed parts list is provided in

Table 3. Detailed parts list of agricultural micro-tiller.

Agricultural Micro-Tiller Parts Breakdown
1	Emergency stop handle	9	Wind guide cover	17	Resistance tiller	25	Rubber washers
2	Clutch handle	10	Equipment shell	18	Transmission	26	Handle sleeve
3	Handle connecting rod	11	Start handle	19	Brackets	27	Wheel hubs
4	Standpipe holder	12	Lamp	20	Control lever	28	Tires
5	Spindle	13	Anti-collision frame	21	Driver	29	Support bracket
6	Fuel tank	14	Station stand	22	Rotary tiller	30	Fenders
7	Air filter	15	Engine	23	Screws	31	Telescopic rods
8	Muffler	16	Engine pallets	24	Nuts

.

The qualitative and quantitative design requirements were imported into the AD process. After performing the “Z-mapping” between FRS and DPS, the relevant design parameters were selected according to the design requirements. A functional breakdown of agricultural micro-tillers is presented in Figure 5.

As seen in Figure 5, there are 31 design parameters corresponding to the functional requirements of the agricultural micro-tiller. After excluding generic design parameters such as rubber washers and screws, 27 structural parameters remained for further analysis. These 27 structural parameters are the focus of this study, and a structural matrix was constructed based on them.

In the FRS-DPS matrix, element A_ij represents the relationship between FRS and DPS in binary form: “X” indicates a direct relationship between functional requirements and physical components, while a blank space signifies no direct connection. The design matrix of the FRS-DPS mapping for agricultural micro-tillers is shown in

Table 4. Breakdown of agricultural micro-tiller parts and corresponding FRS-DPS mapping.

	DP	DP1		DP2				DP3			DP5					DP7			DP8					DP9	DP10
FR		11	12	21	22	23	24	31	32	33	51	52	53	54	55	71	72	73	81	82	83	84	85	91	101	102	103	104
FR1	11	X	X																				X		X			X
	12	X	X													X	X
FR2	21			X																						X	X	X
	22				X
	23					X																			X
	24						X									X	X
FR3	31							X	X
	32							X	X	X	X	X	X			X	X					X			X	X	X	X
	33								X	X	X	X	X			X	X
FR5	51									X	X	X	X			X	X				X					X	X	X
	52								X	X	X	X	X			X	X					X				X	X	X
	53											X	X	X		X	X
	54											X		X					X	X	X		X
	55				X		X							X	X	X	X								X	X	X	X
FR7	71		X		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	X	X
	73															X	X	X										X
FR8	81													X					X	X	X		X
	82								X										X	X	X	X	X
	83																		X	X	X	X	X
	84											X							X	X		X
	85											X									X		X		X			X
FR9	91															X								X
FR10	101																								X		X	X
	102																				X				X	X	X	X
	103																									X	X	X
	104																								X	X	X	X

.

3.3. Solution with DSM

When using AD as a design guideline for agricultural micro-tillers, it is necessary to conduct a thorough analysis of the requirements and functional decomposition to create an AD matrix. However, this matrix only reflects the mapping relationship between functional requirements (FRS) and design parameters (DPS), without illustrating the connections between design parameters themselves. The advantage of using a DSM is its ability to visually analyze the coupling relationships between design elements within the matrix. Therefore, combining AD with DSM (AD-DSM) fully captures the influence of DPS on FRS and highlights the strong and weak relationships between design parameters, addressing AD’s limitation of not reflecting homogeneous relationships between design elements.

DSM is used for modular division based on the coupling and relevance between product structures. A traditional DSM matrix describes the coupling relationship between design parameters, but lacks detail about the degree of interdependence and correlation between them. To address this, the matrix transformation approach proposed in this article (see reference [9]) combines geometric correlation between design parameters, transforming the design matrix into a DSM. This method better reflects the interrelationships and dependencies between design parameters, providing a basis for product disassembly and modularization. The build method is shown in Figure 6, and the process is outlined as follows:

(1)

Construct the design matrix (FRS-DPS): The design parameters of the agricultural micro-tiller are deconstructed according to the independent axioms in AD, and the FRS-DPS matrix is built.

(2)

Create the transition matrix: The design parameters in the AD matrix of the agricultural micro-tiller are adjusted. The DPS with the greatest impact on the FRS in each row is replaced with the corresponding functional term to obtain the agricultural micro-tiller design transition matrix.

(3)

Rearrange the transition matrix: The rows and columns of the transition matrix are adjusted so that the elements corresponding to the same design parameters are placed in diagonal positions, while the design parameters with coupling relationships are moved to the upper or lower triangle of the matrix, resulting in the DSM matrix for the agricultural micro-tiller.

(4)

Assign values to the DSM: The DSM is assigned values based on the geometric correlation between design parameters, reflecting the degree of interaction and modularization.

After transforming the design matrix into a DSM using the matrix transformation process, values are assigned based on geometric correlations. Geometric correlations refer to the physical relationships between design parameters in terms of spatial and geometric relationships, including physical connections, perpendicularity, assembly processes, and dimensions. As a mechanical product, the agricultural micro-tiller integrates various design parameters into a whole through connecting elements. These connections directly impact the product’s disassembly process. The evaluation criteria for the correlation of design parameters are based on four dimensions: connection form, shape relationship, assembly relationship, and functional correlation. The grading of each dimension is shown in

Table 5. Guidelines for connection form scores.

Connection Form	Score α1
Welding, gluing, and other non-removable methods	10
Pressure, thermal expansion, cold shrinkage fits	8
Difficult-to-disassemble connections (e.g., riveting)	6
Easier-to-disassemble connections (e.g., threaded)	4
Easy-to-remove connections (e.g., general contact)	2
No contact relationship	0

,

Table 6. Guidelines for shape relationship scores.

Shape Relationship	Score α2
Direct form relationship (e.g., parallelism, etc.)	8–10
Indirect shape relationship (related to other parts)	2–7
No direct form or position relationship	0

,

Table 7. Guidelines for fitting relationship scores.

Fitting Relationship	Score α3
Excellent	10
Good	8
General	6
Rather poor	4
Very poor	2
No fitting	0

and

Table 8. Guidelines for functional relevance scores.

Functional Relevance	Score α4
Excellent	10
Good	8
General	6
Rather poor	4
Very poor	2
No relevance	0

, with the tightness of each parameter divided into 11 levels, scored from 0 to 10, where higher values indicate stronger relationships [31]. Connection form evaluates the tightness of design parameters, ease of disassembly, and other connection methods. Shape relationship refers to the geometric relationship between parameters, such as parallelism and perpendicularity. Assembly strength assesses the ease of assembly, tightness, and process rigidity. Functional relevance evaluates how the clustering of design parameters facilitates specific functions, such as equipment operation or control devices.

According to the scoring criteria in Table 5, Table 6, Table 7 and Table 8, the correlation of each design parameter of the agricultural micro-tiller is calculated, and the specific steps are as follows:

(1)

Z is used to represent the integrated correlation between the design parameters of the agricultural micro-tiller, and is calculated as shown in Equation (6):

$$Z=Z_1+Z_2+Z_3+Z_4$$

(6)

Z₁, Z₂, Z₃, and Z₄ represent the correlation magnitudes for connection method, shape relationship, assembly relationship, and functional relevance, respectively.

(2)

Set the evaluation levels and corresponding weights Q = {Q₁, Q₂, Q₃, Q₄}, where Q₁, Q₂, Q₃, and Q₄ represent the weight values of connection method, form relationship, assembly relationship, and functional relevance, respectively. When two design parameters are connected by welding, gluing, or other non-removable methods, they are closely related and should belong to the same module. Therefore, the weight for connection method Q₁ is set to 0.4. As the influence of shape relationship, assembly relationship, and functional relevance on the disassembly of the product decreases, the weights are set as follows: Q₂ = 0.3, Q₃ = 0.2, and Q₄ = 0.1. Therefore, the set of weights Q = {Q₁, Q₂, Q₃, Q₄} = {0.4,0.3,0.2,0.1}

(3)

Set the score vector set A = {A₁, A₂, A₃, A₄} for each evaluation criterion of the agricultural micro-tiller, where A₁, A₂, A₃, and A₄ represent the evaluation scores of connection mode, form or position relationship, assembly relationship and functional relevance, respectively. The equation for calculating the correlation of a single criterion is shown in Equation (7):

$$Z_x=Q_x\times A_x{Z}_x=Q_x\times A_x$$

(7)

(4)

The final integrated correlation Z of the agricultural micro-tiller is calculated using Equation (8):

$$Z=Q_1\times A_1+Q_2\times A_2+Q_3\times A_3+Q_4\times A_4$$

(8)

According to the score criteria set in Table 5, Table 6, Table 7 and Table 8, the theoretical maximum score for a single judging criterion is 10, and the minimum score is 0. Therefore, the threshold value for correlation of agricultural micro-tiller design parameters is set at 5. If the sum of the correlation values of two design parameters is greater than 5, it indicates a high correlation between them; otherwise, it indicates a low correlation.

The agricultural micro-tiller DSM is assigned based on Equation (4). For example, for DP11 and DP12 (the rotary tiller and the telescopic rod), the connection is made via an easily removable threaded connection, so the value for Q₁ is 4. There is a strict direct relationship between the two and, so, Q₂ is given a value of 10. The assembly strength shows good contact stiffness, with Q₃ valued at 8. In terms of functional relevance, the two components are strongly specialized in completing the plowing operation depth and, so, Q₄ is also assigned a value of 10. According to Equation (4), the correlation between design parameters DP11 and DP12 is calculated as Z (11,12) = (4 × 0.4) + (10 × 0.3) +(8 × 0.2) +(10 × 0.1) = 7.2. By analogy, the DSM for the correlation of agricultural micro-tiller design parameters is shown in

Table 9. Agricultural micro-tiller DSM (note: no coupling between identical design parameters).

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	81	82	83	84	85	91	101	102	103	104
DP1	11
	12	7.2
DP2	21
	22
	23
	24
DP3	31
	32							8.4
	33								1
FR5	51								1	2.4
	52								1	0.2	1
	53								1	6.4	2.2	0.8
	54											7
	55				1.8	1.8	2						4.8	0.8
FR7	71		6.2		5.6	5.6	7		6	2.4	5	1.8	7		6.4
	72				5.6	5.4	6.2		6.6	2.4	7	1.8	5.4		6.6	7.6
	73															7.6	7.6
FR8	81													6.6
	82								1					3.6					4.6
	83										0.6			2.8					6.6	5.4
	84								0.8			0.6				2.4	2.4		2.4	6.6	1
	85											7		2.8					6.6	5.4	1
FR9	91															5.2
FR10	101	0.8				6.4			1						5.4	4.4	4.4						1
	102			4.6					1		1	1	1.4		5.4	6.2	6.2				0.8				3.2
	103			4.6					1		0.8	0.4	1.4		5.4	5.2	5.2								3.2	5.2
	104	0.8		5.8					1		0.8	0.8	1.4		5.4	7	6.2	7					1		3.6	4.4	4.8

.

4. Design Examples and Verification

4.1. Types of Graphics

The systematic clustering method was selected to optimize the distribution of the association values in the DSM for agricultural micro-tiller design parameters, as shown in

Table 10. Clustering results for each design parameter of the agricultural micro-tiller (note: no coupling between identical design parameters).

		1		2															3	4		5
		11	12	21	55	102	103	71	72	73	104	23	101	22	24	33	53	51	91	31	32	52	54	85	81	83	82	84
1	11
	12	7.2
2	21
	55
	102			4.6	5.4
	103			4.6	5.4	5.2
	71		6.2		6.4	6.2	5.2
	72				6.6	6.2	5.2	7.6
	73							7.6	7.6
	104	0.8		5.8	5.4	4.4	4.8	7	6.2	7
	23				1.8			5.6	5.4
	101	0.8			5.4	3.2	3.2	4.4	4.4		3.6	6.4
	22				1.8			5.6	5.6
	24				2			7	6.2
	33							2.4	2.4
	53				4.8	1.4	1.4	7	5.4		1.4					6.4
	51					1	0.8	5	7		0.8					2.4	2.2
3	91							5.2
4	31
	32					1	1	6	6.6		1		1			1	1	1		8.4
5	52					1	0.4	1.8	1.8		0.8					0.2	0.8	1			1
	54				0.8																	7
	85										1		1									7	2.8
	81					0.8																	6.6	6.6
	83																	0.6					2.8	1	6.6
	82																				1	0.6	3.6		4.6	5.4
	84							2.4	2.4												0.8			5.4	2.4	1	6.6

. This optimization was based on the association distance, clustering design parameters with high correlations into one group. The resulting graph reflects a hierarchical clustering analysis of each design parameter for the agricultural micro-tiller, as shown in Figure 7. The DSM clustering results are presented in Table 10.

According to expert opinion, each design parameter was categorized into five functional modules.

(1)

Tillage module: DP₁₁ Rotary tiller, DP₁₂ Telescopic rods.

(2)

Support and power module: DP₂₁ Muffler, DP₅₅ Equipment shell, DP₁₀₂ Transmission, DP₁₀₃ Driver, DP₂₂ Wind guide cover, DP₂₄ Fenders, DP₇₁ Brackets, DP₁₀₄ Engine pallets, DP₂₃ Air filter, DP₁₀₁ Fuel tank, DP₃₃ Station stand, DP₅₃ Anti-collision frame, DP₅₁ Resistance tiller.

(3)

Lamp Module: DP₉₁ Lamp.

(4)

Mobile Module: DP₃₁ Wheel hubs, DP₃₂ Tires.

(5)

Handrail module: DP₅₂ Emergency stop handle, DP₅₄ Handle sleeve, DP₈₅ Start handle, DP₈₁ Handle connecting rod, DP₈₃ Clutch handle, DP₈₂ Control lever, DP₈₄ Spindle.

4.2. Design Examples

The relevant design parameters of the tillage module are responsible for completing the tillage and depth adjustments. The support and power module is divided into two main parts: the support part, which handles the installation and bearing of the agricultural micro-tiller, and the power part, which is closely related to the support part in geometry and provides the power for the equipment. The design parameters of the mobile module ensure easy movement of the equipment, while the lighting module (a lamp set) is responsible for illumination during night work. The handrail module controls the operational status of the equipment and maintains its balance.

(1)

Design Considerations

Structural layout: The overall assembly of the equipment relies on the bracket and support frame. The work module, mobile module, lighting module, and handrail module depend on the support and power module for their construction. Thus, the support and power modules serve as the primary design parameters, and any changes to these parameters will influence the other modules.

User requirements: Based on the analysis of each attribute derived from the Kano model, the functional design takes into account essential (M), attractive (A), and desired (O) requirements. This approach helps identify the hidden design needs of users and select appropriate design innovations.

Design methodology: Ergonomics and color psychology are applied in the design of the equipment’s appearance to enhance user experience, reduce operational errors, and minimize discomfort during use.

(2)

Design constraint

Based on the modular division of the product’s parts and the design considerations derived from the AD-DSM matrix, the design output for the agricultural micro-tiller is as shown in Figure 8. This design plan was created using 3D computer-aided design software (Auto CAD 2023, keyshot 11), considering the structural layout, user requirements, and design methods discussed earlier.

The modular structural layout of the design plan is illustrated in Figure 9. This diagram, based on a 2D exploded view of the mini-tiller’s structure, was created using 2D drafting software (Adobe photoshop 2022, Adobe Illustrator 2022). Different colored boxes highlight the distinct modules.

5. Conclusions

In this study, a modular design solution model based on a Kano-AD-DSM framework was established to address the current issues of tedious maintenance and high failure rates associated with agricultural micro-tillers. AD and DSM were introduced during the module division stage to better demonstrate the relationships and correlations between design parameters. Correlations between design parameters, combined with system clustering, were used to determine the functional module division of the product, resulting in a comprehensive multi-module design for the agricultural micro-tiller.

The final design was evaluated through a user satisfaction assessment, targeting three groups: farmers, agricultural machinery maintenance personnel, and agricultural machinery sales personnel. A Likert scale was employed, with a score of 5 indicating very satisfied, 4 fairly satisfied, 3 acceptable, 2 slightly dissatisfied, and 1 very dissatisfied. The arithmetic mean method was used to calculate overall user satisfaction. Survey questions focused on comfort of use, ease of disassembly, aesthetic appeal, and layout rationality. The average satisfaction scores were 4.46, 4.35, and 4.16 for the farmers, maintenance personnel, and sales personnel, respectively, indicating that the design meets market demands and has been proven to be feasible.

The strengths of this study are twofold: first, by starting with user needs and employing the Kano method, a comprehensive user demand analysis was conducted, leading to a systematic redesign of the mini-tiller’s components and effectively addressing design challenges. Second, a more targeted approach was used to quantify and re-evaluate the connections between components, resulting in a multi-module division that improves disassembly performance and enhances the convenience of maintenance.

In future research, further clustering analyses of the design parameters will be conducted to refine the module division scheme and enhance the product’s disassembly performance. Additionally, gathering timely feedback from users and experts will be crucial to improving the product’s user experience and human–computer interaction from the perspective of user needs. Finally, the systematic modular division strategy proposed in this article provides a solid foundation for future research on design for disassembly. Although the current study did not conduct quantitative experiments on the maintenance performance, from a theoretical perspective, enhancing the disassembly capability is expected to improve the ease of maintenance of the mini-tiller, to a certain extent, thereby positively impacting its maintenance performance. Future research can further explore the specific quantitative relationship between disassembly capability and maintenance performance.