Testing of the Prototype Model of the Proposed End Effector Design

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Chapter Seven

Testing of the Prototype Model of the Proposed End Effector Design

7.1 Introduction

The performance of the proposed “Dolly & Baseboard” assembly operation needs to be monitored and analyzed in order to minimize failure. As baseboard and dolly are supplied from different mould cavity in Clipsal, it is important to know the parameters such as the mould cavity number where the parts are made from or inserting speed, that might affect the consistence and cycle time of the assembly process, and to be able to vary or discard them so as to achieve the desired performance of the assembly process. The approach used in this project is an experimental technique known as Design of Experiment (DOE).

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DeVor, Tsong & John (1992) has defined DOE as a statistical tool used in quality design and improvement. The purpose of DOE is to experiment with various combinations of parameters for the purpose of identifying the particular combinations that optimize certain design criteria or performance measures.

7.2 Mathematical representation of DOE

In DOE, only the final outcomes obtained by a combination of different variables are of interest. This outcome is usually known as response, which is the performance of the dolly & baseboard assembly process in this project. DeVor, Tsong & John (1992) states that the response can be represented mathematically by the equation as illustrated in equation 7.1. Assuming that a system involving a mean response that is dependent on input variables x1, x2, . . . , xn. Then could be expressed as

This mean that the mean response can be expressed as function with independent variables x1, x2, . . . , xn and a set of parameters θ1, θ2, . . . , θk. The data collected during the experiment are represented by the equation as illustrated in equation 7.2.

7.2.1 Classification of variables by transfer function model

The performance of a system can be described by a transfer function model as shown in 7.1. The transfer function illustrates the relationship between the inputs, defined control factors and the output of the process in the presence of noise.

According to Taguchi’s Methods, the factors that can affect the output performance or quality can be classified into mainly four categories which are shown in Table 7.1.

Table 7.1 Types of factors in an experiment


Types of Factors



Signal factors

These are factors that may be adjusted by user to attain target performance.


Control factors

These are the process parameters whose values can be determined during design process.

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