Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation

In the modern era of manufacturing, it is important to optimize every design parameter in product development stage to reduce cost, material usage and to achieve the desired efficacy level. There are various models which serve those purposes, for instance, Design of Experiment (DoE) is used to check the parameters after adopting optimization tactics which results in reduced cost or saving operating time. In this regard, this research aims to construct a DoE model on a portable workstation to optimize its design parameters. The methodology of DOE would be a 2 level 3 factors full factorial DOE which is conducted to determine the optimal value for three design parameters (fac-tors) which are material density, the length of the table and the length of the table stand in terms of the response which is the required time of fold ability function of the portable workstation. Based upon the evaluated interactions between the parameters, the optimized parameters are chosen for responses. Here, the resultant design parameters are at their lowest level, so the goal of time efficiency in fold ability function is achieved. This similar sort of DoE can be implemented in the furniture and other manufacturing industries who wish to optimize their material usage as well as increase efficiency and reduce cycle time.


Introduction
In the design and development phase of a new product, selection of less time-consuming functionality is essential along with serving the intended pur- 26 Engineering pose of that product. To select less time-consuming functionality, the design parameters must be optimized in such a way that it can function properly within least time. This optimization of design parameters should be implemented in the design stage of the product development. The purpose of the design phase is to implement all items that have been discussed in the define, measure, and analyze phases into a detailed product design [1]. Product development is an iterative process in order to cope up with the fast-changing market situations, immense price pressure and shortening of product life cycles [2]. As a result, during the design phase of the product development, the designs must be evaluated and revised extensively to bring out more coherence from that product. There are two wheels and a handle in the back of the chair which helps the workstation to move after folding. To make the product time effective, the time requirement to fold the workstation will be needed to be reduced. To solve this issue, Design of Experiments (DoE) is conducted at the design stage of product development to optimize the design parameters which affect the required folding time. DoE is a systematized approach of performing the experimentation by utilizing the principles of science and statistics, which helps in establishing relationships between the input factors and output responses [3]. It is a very useful as well as the most crucial tool for the identification to optimize the respective process conditions [4]. DoE can be applied to any system in which output information and its quality depend on many input parameters. It is an iterative procedure based on previous measurements and is able to predict better settings resulting in an improvement in the quality of the output information [5].
Among different types of design of experiments, in a full factorial design (FFD), the effect of all the factors and their interactions on the outcome(s) is investigated [6]. As this research intends to evaluate the effects and interactions of all factors, full factorial design of experiment is conducted here to obtain the optimum process parameters. Using this method, this paper gathers statistical data, analyze and evaluate them with full factorial design of experiments in order to determine the optimal design parameters.
This paper is organized as follows. Section 2 describes the research methodology and process factors. Section 3 presents the DOE model analysis and Section 4 interprets the results. Conclusions are presented in Section 5.

Methodology
Any input to the process is a factor which can be set to a desired value on the machine controller or can be selected from the available options. On the other hand, any output from a process is a response [7]. A response is the result ob-  Table 1.
All two factor interactions are evaluated in these experiments that might affect the average process. The full factorial design is conducted to determine optimal level of factors.

Experimental Layout
The time is measured at different levels of factors and timings are recorded three times. The experimental layout is illustrated by Table 2.

Main and Interaction Effects Affecting the Variability in Response Time
Screening designs provide an effective way to consider many process or design parameters (or factors) in a minimum number of experimental runs or trials (i.e. with minimum sources and budget) [10]. In our research article, a coded design matrix is established for screening and determining the notable main and interaction effects that affect process variability where standard deviation (SD) is the response. The coded design matrix is presented in Table 3. The Pareto effect in terms of SD is illustrated in Figure 4 and interaction plot is illustrated in Figure 5.

Results and Discussion
From the Pareto plot of standard deviation (Figure 4) shows none of the factors has a significant effect on variability. On the other hand, the interaction plot represents that the interaction between material density (L) and the length of the     The above design parameter will allow "maximum foldability" which in turn means "minimum folding time" will be achieved by using this combination of design parameters in the product design and development phase.

Conclusion
In this research article, a portable workstation is considered whose foldability among the quality characteristics) and monotonic (i.e. the effect of each factor on robustness should be in a consistent direction, even when the settings of factors are changed), but it is often seen in practical situation that even though the main factors have no or little impact on the variability of a response, the interaction between those factors has a significant impact on that [11]. Therefore, this research article considers the interactions between the factors in order to detect any impact on the variability of the folding time of the workstation. Main and interaction plots in terms of standard deviation are also determined. After analyzing the variability and mean folding time using full factorial design of experiment, the optimal design parameters are determined. However, as an early stage design, only two-level factorial design has conducted here. In future as a further work, 3 or 4 level factorial design can be evaluated to determine more efficient parameters and increase the competence level of the design.