Introduction
Over the 20th century, quality had many meanings and there were many different approaches towards achieving quality. This paper presents an overview of Taguchi’s view on quality and what quality meant to him. Taguchi proposes a design approach known as quality by design to reduce cost and provide optimum quality to customers. He emphasizes the importance of three design steps to achieve quality: System design, Parameter design, and Tolerance design. The paper further explains in detail the steps for parameter design which is the key to reduce variability in process output.
Taguchi’s View on Quality
Taguchi believed that cost and quality go hand in hand and he defined quality as the loss incurred by the society from the production …show more content…
controllable system parameters to get a quality output which is insensitive to noise factors and thus reduced variability. Noise factors contribute to variability. Therefore, the system parameters should be insensitive to it. Usually, there are multiple parameters for a system and optimum levels are obtained by trial and error. But this is a time-consuming and expensive design step and to reduce cost in most cases very few experiments are conducted and an optimum design is not achieved.
To tackle this problem, Taguchi came up with his own method for parameter design. This method aims at reducing the number of experiments to achieve the optimum parameters by using orthogonal arrays from the field of DOE.
Taguchi found a simpler way to use orthogonal arrays by providing standard orthogonal arrays and linear graph fits. The columns in the array are mutually orthogonal such that all combinations of levels are possible. So, for a system with 4 factors and 3 levels, we will have 9 rows and 4 columns and is denoted as L9 design. This means 9 experiments must be conducted to get the optimum levels of parameters which is significantly lower when compared to the traditional method which would need 81 experiments for the same design. This helps in reducing the time and cost significantly and thus getting the best quality products at lower costs from the …show more content…
Also, the number of levels for these control factors must be selected in this step.
• After identifying the control factors and their levels, matrix experiment is designed and data analysis procedures are defined. Appropriate orthogonal arrays are selected for control factors and noise factors. These arrays are crossed and results are denoted by Y i, j. Then the mean and variance of product response are obtained by varying the noise factors.
• Next, a matrix experiment is conducted and the results are recorded.
• Analysis of the results will give, the best test parameter configuration for optimum quality. The results are analyzed by utilizing signal-to-noise ratio which is a statistical measure of performance. S/N is basically the ratio of mean to standard deviation. Out of the many S/N ratios possible, the best is selected to optimize the quality characteristics. The larger the S/N ratio the better. So, to optimize quality characteristics factors resulting in highest S/N ratio must be selected.
• Once, these levels for factors are selected, an actual experiment is performed to confirm the
Over the 20th century, quality had many meanings and there were many different approaches towards achieving quality. This paper presents an overview of Taguchi’s view on quality and what quality meant to him. Taguchi proposes a design approach known as quality by design to reduce cost and provide optimum quality to customers. He emphasizes the importance of three design steps to achieve quality: System design, Parameter design, and Tolerance design. The paper further explains in detail the steps for parameter design which is the key to reduce variability in process output.
Taguchi’s View on Quality
Taguchi believed that cost and quality go hand in hand and he defined quality as the loss incurred by the society from the production …show more content…
controllable system parameters to get a quality output which is insensitive to noise factors and thus reduced variability. Noise factors contribute to variability. Therefore, the system parameters should be insensitive to it. Usually, there are multiple parameters for a system and optimum levels are obtained by trial and error. But this is a time-consuming and expensive design step and to reduce cost in most cases very few experiments are conducted and an optimum design is not achieved.
To tackle this problem, Taguchi came up with his own method for parameter design. This method aims at reducing the number of experiments to achieve the optimum parameters by using orthogonal arrays from the field of DOE.
Taguchi found a simpler way to use orthogonal arrays by providing standard orthogonal arrays and linear graph fits. The columns in the array are mutually orthogonal such that all combinations of levels are possible. So, for a system with 4 factors and 3 levels, we will have 9 rows and 4 columns and is denoted as L9 design. This means 9 experiments must be conducted to get the optimum levels of parameters which is significantly lower when compared to the traditional method which would need 81 experiments for the same design. This helps in reducing the time and cost significantly and thus getting the best quality products at lower costs from the …show more content…
Also, the number of levels for these control factors must be selected in this step.
• After identifying the control factors and their levels, matrix experiment is designed and data analysis procedures are defined. Appropriate orthogonal arrays are selected for control factors and noise factors. These arrays are crossed and results are denoted by Y i, j. Then the mean and variance of product response are obtained by varying the noise factors.
• Next, a matrix experiment is conducted and the results are recorded.
• Analysis of the results will give, the best test parameter configuration for optimum quality. The results are analyzed by utilizing signal-to-noise ratio which is a statistical measure of performance. S/N is basically the ratio of mean to standard deviation. Out of the many S/N ratios possible, the best is selected to optimize the quality characteristics. The larger the S/N ratio the better. So, to optimize quality characteristics factors resulting in highest S/N ratio must be selected.
• Once, these levels for factors are selected, an actual experiment is performed to confirm the