Sivaraos, Dimin M.F., Faris N.M.F., A. Hambali, S. Dhar Malingam and Sapuan S.M. Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM). Faculty of Mechanical Engineering, UniversitiTeknikal Malaysia Melaka (UTeM). Department of Mechanical and Manufacturing Engineering, University Putra Malaysia (UPM).

Veracious concept selection process is crucial in design engineering where, a concept with concise description will fulfill customer requirements. Failure in concept selection can lead to inaccurate design which will result in unnecessary process repetition of the initial stage.

One of the best tools that can be used in determining the best design concept is Analytical Hierarchy Process (AHP). Micro Hot-Marking Tool (HMT) is a super finished tool with micro tip which is to be used for alphabetical marking process using CNC milling machine. In this research, AHP was successfully employed in selecting design concept for HMT.

Four significant and robust concepts were analyzed, namely C1, C2, C3 & C4. Concept 2 (C2) has been chosen as the best concept with the highest score of 27% among all the evaluated concepts which will be taken into next design stage.

The early stage of design selection is considered to be the most difficult, sensitive and critical process in product development [1]. Selecting the right design concepts at the conceptual design stage in product development process is a crucial decision [2,3].

In both academic research and industrial practice, AHP has been widely used to solve multi-criteria decision making. AHP has been implemented in almost all applications related to decision-making and is now predominantly used in the theme of selection and evaluation especially in the area of engineering, personal and social categories [4].

Implementing appropriate evaluation and decision tool should be considered at the conceptual design stage that involves many complex decision-making tasks [5]. AHP is based on experience and knowledge of the experts or users to determine the factors affecting the decision making process [6]. Majority of product cost and quality is fixed by selecting particular concepts [7].

AHP is one of the decision making tools that can be employed to assist decision makers to determine the right decision.

In general AHP can be divided into three main phases [8] :

1. Hierarchy structure

2. Priority analysis

3. Consistency verification

Factor Influence Selecting Teaching and Learning Tools

Easy to use (EU)

Easy to

installation (EI)

Easy to store (ES)

Light weight

(LW)

Simplicity of

design (SD)

Easy to

manufacturing

(EM)

Easy to assemble (EA)

Performance (P)

Manufacturing (M)

Temperature (T)

Cost

(C) Flexible type of

Word (FW)

Selection of the Best HMT Conceptual

Design GOAL

CRITERIA

Manufacturing cost (MFC)

Material cost (MTC)

SUB-CRITERIA

(S-C)

C1 C2 C3 C4 ALTERNATIVES

Pairwise comparison is a fundamental of AHP steps. The decision makers have to compare each element by

using the relative scale pairwise comparison and the signed value is made based on the decision makers or users experience and knowledge [10].

Pairwise comparison of criteria with respect to overall goal

Pairwise Comparison of Criteria with Respect to Overall Goal

Goal P M T C FW

Performance (P) 1 3 3 3 1

Manufacturing (M) 1/3 1 1 1 1

Temperature (T) 1/3 1 1 3 1

Cost (C) 1/3 1 1/3 1 1

Flexible type of word (FW) 1 1 1 1 1

Total Column ( ∑ ) 3.000 7.000 6.333 9.000 5.000

Since the comparisons are carried out through personal or subjective judgments, some degree of inconsistency may be arising.

To ensure the judgments are consistent, the final operation called consistency verification must be performed.

Consistency verification is considered as one of the most advantages of the AHP which is incorporated in order to measure the degree of consistency among the pairwise comparisons by computing the consistency ratio [10].

The consistency is determined by the consistency ratio (CR). Consistency ratio (CR) is the ratio of consistency index (CI) to random index (RI) for the same order matrices. Table 2 shows the consistency ratio for the main factors with respect to the goal in this case study. If CR is less that 0.1 and the judgments are acceptable.

Consistency test for the Main Factors

Goal P M T MT FW PV NV NV /PV Consistency index (CI)

= 𝐂𝐈 = 𝝀𝒎𝒂𝒙−𝒏

𝒏−𝟏

= 0.075

P 1 3 3 3 1 0.354 1.914 5.411

M 1/3 1 1 1 1 0.145 0.764 5.285

T 1/3 1 1 3 1 0.189 1.011 5.349 Consistency ratio (CR)

= 𝐂𝐑 = 𝑪𝑰

𝑹𝑰

= 0.07

MT 1/3 1 1/3 1 1 0.124 0.638 5.165

FW 1 1 1 1 1 0.189 1.000 5.290

Total ( ∑ ) 26.500 Note: A the value of

CR is less than 0.1,

the judgments are

acceptable because

CR < 0.1

Maximum eigenvalue ( ) 5.300

Table below represents the overall priority vector for four decision options with respect to the sub-factors.

The overall priority vector can be obtained by multiplying the priority vector for the decision options by the vector of priority of the sub-factors.

Overall Priority Vectors for Sub-Factors with respect to the Main Factors

Overall Priority Vector ∑ %

C1 0.244 0.079 0.313 0.079 0.380 0.238 24%

C2 0.238 0.201 0.313 0.201 0.380 0.269 27%

C3 0.281 0.519 0.063 0.519 0.062 0.262 26%

C4 0.238 0.201 0.313 0.201 0.179 0.231 23%

The overall priority vector can be obtained by multiplying the priority vector for the design alternatives by the priority vector of the criteria.

The C-2 is the preferred choice since it has the highest value (0.269 or 26.9% ≈ 27%) among four decision options. the overall priority calculation is as follow; 0.238(0.354) + 0.201(0.145) + 0.313(0.189) + 0.201(0.124) + 0.38(0.189) = 0.269.

The second highest is the C-3 with a value of 0.262 (26.2%), and the lowest value or last choice is the email approach with a value of only 0.231 (23.1%).

Best Selection

Alternative ∑ %

C-1 0.238 24%

C-2 0.269 27%

C-3 0.262 26%

C-4 0.231 23%

AHP was effectively applied in selecting the best micro Hot-Marking Tool concept among the four significant and robust alternatives. HMT design concept 2 was most appropriate as per all the analyzed criteria.

Since the Consistency ratio (CR) were less than 0.1, concept C2 stood at the top ranking with score of 0.269 (27%) followed by C3, C1 and C4 with their scores of 0.262, 0.238 and 0.231 respectively. Hence, based on requirements, C2 is selected as the best concept for the intended design and development of HMT.

Application of AHP for selecting conceptual design at conceptual design stage can drastically improve the product quality while shortening the product development stages and processes.

[1] K.T. Ulrich, and S.D. Eppinger, Product Design and Development 2nd ed., McGraw-Hill, New York, 2000.

[2] O. S. Vaidya, and S. Kumar. Analytical Hierarchy Process: An Overview of Applications. European Journal of Operational Research, (2006), 169: 1-29.

[3]A. Hambali, S.M. Sapuan, I. Napsiah, and Y.Nukman, Use of analytical hierarchy process (AHP) for selecting the best design concept, Journal Teknologi, (2008), pp.1-18(1)

[4] V. Laemlaksakul, S.Bangsarantrip, Analytic Hierarchy Process for design selection of Laminated Bamboo Chair, Proceedings of the international multi conference of engineers and computer Scientists Volume II, (2008), pp.1-6(7)

[5] T.L. Saaty, How to make a decision: The analytical hierarchy process, European journal of operation research 48, 1990, pp.9-26.

[6] F. Dweiri, and F.M. Al-Oqla, Material Selection Using Analytical Hierarchy Process. International Journal of Computer Applications in Technology, (2006), 26(4): 82-189. [7] F.Rehman, and X.T. Yan, Product design elements as means to realise functions in mechanical conceptual design. Proceedings of the International Conference on Engineering design ICED 03, (2003), pp. 1-10. [8] T. L. SaatyThe Analytic Hierarchy Process. New York: McGraw Hill, 1980. [9] A. Perego, and A. Rangone, On integrating tangible and intangible measures in AHP application a reference framework, IEEE international conference on systems, man and cybermetics, (1996), pp. 1836-1841(14) [10] Y. Jun, M. Xin-sheng, L. Yang, Design and realization of AHP Toolbox in MATLAB, IEEE international conference on granular computing, (2008), pp.740-745 [11] S.W. Hsiao, Concurrent Design Method for Developing a New Product. International Journal of Industrial Ergonomics, (2002), 29: 41-55.