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Taguchi Method. ONG SIE MENG NADIRAH BINTI MOHD IZAN NOR SAZRIN BINTI NOR AZMAN MOHD FARIDDUDIN BIN ADNAN. Overview. Introduction How it works? Advantages & Disadvantages of using Taguchi Method Application to industry. Introduction.
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Taguchi Method ONG SIE MENG NADIRAH BINTI MOHD IZAN NOR SAZRIN BINTI NOR AZMANMOHD FARIDDUDIN BIN ADNAN
Overview • Introduction • How it works? • Advantages & Disadvantages of using Taguchi Method • Application to industry
Introduction • Taguchi Method is a new engineering design methodology • Improves the quality of existing products and processes and simultaneously reducing the costs, with minimum engineering resources and development man-hours
Introduction • Taguchi method has 2 types of active engineering design • To find the levels of the parameters that will introduce the least variation into product, process or service (controlling quality) • To determine which factors contribute most to the end product’s variation (controlling cost of product)
Principle Contribution • Taguchi's principle contributions to statistics are • Taguchi loss-function • The philosophy of off-line quality control
Taguchi loss-function • To improve mean outcome of process • To maximize an appropriate signal to noise ratio representing the magnitude of the mean of a process as compared to its variation Figure 1 Conventional Interpretation Specification Figure 2 Taguchi Interpretation Specification
Off-line Quality Control • System design • To design at the conceptual level involving creativity and innovation • Parameter design • The nominal values of the various dimensions and design parameters need to be set • Tolerance design • To understand of the effect that the various parameters have on performance, resources can be focused on reducing and controlling variation in the critical few dimensions
Taguchi specified three situations: • Larger the better • agricultural yield • n = -10 [ mean of sum of squares of (measured – ideal) data ] • Smaller the better • carbon dioxide emissions • n = -10 [mean of sum squares of reciprocal of measured data] • On-target, minimum-variation • mating part in an assembly • n = 10 (square of mean / variance)
Steps approach to Taguchi Method • Step-1: Identify the main function, side effects, and failure mode • Problem lead to a low production rate • Step-2: Identify the noise factors, testing conditions, and quality characteristics • Ambient temperature • Step-3: Identify the objective function to be optimized • Improve the production rate • Step-4: Identify the control factors and their levels • Variables that affect the production rate
Step-5: Select the Orthogonal Array Experiment • A type of general fractional factorial design • Technique of laying out the conditions of experiments involving multiple factors • Taguchi Orthogonal arrays are balanced to ensure that all levels of all factors are considered equally , where = Number of level = Number of parameter n = Number of experiments carried out
Example of Orthogonal Array • Orthogonal array = 7 factors in 2 level, • Known as Taguchi’s 2 level design
Step-6: Conduct the Matrix ExperimentStep-7: Analyze the Data, Predict the Optimum Levels and Performance • Signal-to-noise ratio needs to be calculated for each experiment conducted to determine the effect of each variable has on the output. • , where, • is the mean value of the performance for a given experiment • is the variance of the performance • Average range value for each factor
Factor E is the critical factor and followed by Factor D • Factor A, D and F tends to perform better at Level 2 • Factor B,C and E can perform efficiently at Level 1 Figure 3 Range Value for Each Parameter
Step-8: Perform the Verification Experiment and Future Plan • ANOVA • Sum of square error • P-value
Taguchi Experimental Design vs Traditional Design of Experiments • Higher-order interactions are assumed to be non-existent. • Experimenters are asked to identify which interactions might be significant before conducting the experiment • Taguchi’s orthogonal arrays are not randomly generated • Traditional DOE’s treat noise as a nuisance (blocking) but Taguchi makes it the focal point of his analysis.
What are the advantages of robust design? • The effect of robustness on quality is great. Robustness reduces variation in parts by reducing the effects of uncontrollable variation • Lower quality parts or parts with higher tolerances can be used and a quality product can still be made • The product will have more appeal to the customer. Customers demand a robust product that won't be as vulnerable to deterioration
What are the disadvantages of robust design? • To effectively deal with the noise, the designer must be aware of the noise. If there is a noise factor that is affecting the product and the experiments run do not address • Larger computational time as much variables have been considered. • By using orthogonal arrays, it assumes the noise factors are independent, which may be helpful in setting up the experiment, but is not necessarily a good assumption
Application in Industry ( Plastic Injection Moulding) • Plastic Injection Molding is a high precision tool that is being used for fabricating thermoplastic materials. • It is a high rate production process, with good dimensional control. Thus, highly complex components can be produced in the finished state
Problem : Warpage • Warpage is the dimensional distortion developed in a molded product after its ejection from the mold during injection molding process.
