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 (factors) 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.
In the design and development phase of a new product, selection of less time-consuming functionality is essential along with serving the intended purpose 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 [
This research article considers a portable workstation which has a fold ability function. The workstation consists of a desk and a chair which are joined by a hinge and can be folded. The table is supported by square stainless-steel pipe, an outer box and plywood. The table and the outer box can be folded over the chair. 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 [
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.
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 [
Full factorial designed experiment consists of all possible combinations of levels for all factors. The total number of experiments for studying k factors at 2-levels is 2^k [
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.
The time is measured at different levels of factors and timings are recorded three times. The experimental layout is illustrated by
A main effects plot or Pareto plot is a plot of the mean response values at each level of a design parameter or process variable. One can use this plot to compare the relative strength of the effects of various factors [
| Factors | Labels | High Level | Low Level |
|---|---|---|---|
| Material Density | D | 680 kg/m3 | 2000 kg/m3 |
| Length of the Table | L | 30 cm | 33 cm |
| Length of the | S | 60 cm | 64 cm |
| No. of Trials | D (kg/m3) | L (cm) | S (cm) | Response 1 (Time in Seconds) | Response 2 (Time in Seconds) | Response 3 (Time in Seconds) |
|---|---|---|---|---|---|---|
| 1 | 680 | 30 | 60 | 30.66 | 31.20 | 30.23 |
| 2 | 2000 | 30 | 60 | 26.79 | 26.57 | 26.69 |
| 3 | 680 | 33 | 60 | 29.71 | 29.82 | 30.33 |
| 4 | 2000 | 33 | 60 | 25.58 | 25.21 | 25.81 |
| 5 | 680 | 30 | 64 | 37.75 | 37.01 | 37.32 |
| 6 | 2000 | 30 | 64 | 33.24 | 32.30 | 34.01 |
| 7 | 680 | 33 | 64 | 37.11 | 36.30 | 37.23 |
| 8 | 2000 | 33 | 64 | 32.56 | 32.84 | 33.90 |
An interactions plot is a powerful graphical tool which plots the mean response of two factors at all possible combinations of their settings. If the lines are parallel, this indicates that there is an interaction between the factors. Non-parallel lines are an indication of the presence of interaction between the factors [
Interpretation of Pareto and interaction plot
From Pareto plots, it can be interpreted that material density(D) and the length of the table stand(S) has a significant effect on the foldability of the workstation. Other factors including the interactions of the factors do not show noticeable impact on the response. On the other hand, interaction plot illustrates that there is little interaction between the material density and the length of the table. But this interaction might not have much influence on the folding time of workstation which can be concluded from the Pareto chart.
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) [
From the Pareto plot of standard deviation (
| No. of Trials | D (kg/m3) | L (cm) | S (cm) | SD | NR | Mean |
|---|---|---|---|---|---|---|
| 1 | −1 | 1 | 1 | 30.66 | 31.20 | 30.23 |
| 2 | 1 | 1 | 1 | 26.79 | 26.57 | 26.69 |
| 3 | −1 | 1 | 1 | 29.71 | 29.82 | 30.33 |
| 4 | 1 | 1 | 1 | 25.58 | 25.21 | 25.81 |
| 5 | −1 | 1 | 1 | 37.75 | 37.01 | 37.32 |
| 6 | 1 | 1 | 1 | 33.24 | 32.30 | 34.01 |
| 7 | −1 | 1 | 1 | 37.11 | 36.30 | 37.23 |
| 8 | 1 | 1 | 1 | 32.56 | 32.84 | 33.90 |
for the workstation can be determined. It is observed that variability is lowest when both material density and the length of the table are at a low level. So, the optimized design parameters for the workstation are:
Material Density (D): Low Level (2000 kg/m3)
Length of the Table(L): Low Level (30 cm)
Length of the Stand (S): Low Level (60 cm)
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.
In this research article, a portable workstation is considered whose foldability process is subjected to improvement using DoE method. The main goal of this research was to determine the optimal time needed to fold the workstation. This process depends on three factors which significantly affect folding time. These factors are varied into two levels—high level and low level to observe the impact on the folding time. The interactions of the factors are also evaluated because, quality characteristics should preferably be additive (i.e. no interaction exists 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 [
The authors declare no conflicts of interest regarding the publication of this paper.
Anika, N.A., Tanzeem, N. and Gupta, H.S. (2020) Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation. Engineering, 12, 25-32. https://doi.org/10.4236/eng.2020.121002