China
Africa
Explore chapters and articles related to this topic
Combinatorial Chemistry Approach and the Taguchi Method for Phosphors
Published in Ru-Shi Liu, Xiao-Jun Wang, Phosphor Handbook, 2022
Orthogonal arrays are a set of tables containing information on how to determine the least number of experiments required and their conditions. The fixed orthogonal tables, called standard orthogonal arrays, are available to design simple experiments in which factors can vary among a fixed number of levels. Experimental designs with mixed levels of multifactors require knowledge on statistics to modification of the standard arrays. In orthogonal array tables, columns present factors that can be accommodated, while rows present trial conditions. The standard notation for orthogonal arrays is Ln(XY), where n represents the number of experiments (rows in the orthogonal table), Y represents the number of factors (columns in the orthogonal table), and X represents the levels of a factor. The commonly used orthogonal tables include L4(23), L8(27), L9(34), L16(215), L16(45), L25(56), L27(313), and the mixed-levels L18(21 × 37). Tables 8.1 and 8.2 show two examples on the design of orthogonal tables of L9(34) and L16(45).
Optimization of Laser-Based Additive Manufacturing
Published in Linkan Bian, Nima Shamsaei, John M. Usher, Laser-Based Additive Manufacturing of Metal Parts, 2017
Amir M. Aboutaleb, Linkan Bian
Taguchi design provides a balanced design of experiments that lays out the factors’ levels in an equally weighted way. Taguchi design is an efficient method because it provides enough information by designing just a few design setups, and may be used a robust alternative to two- or three-level fractional factorial designs. The Taguchi design uses three sequential steps: (1) system design, which incorporate domain knowledge; (2) parameter design, which optimizes the settings of process parameters; and (3) tolerance design, which determines and analyzes tolerances around the optimal parameters. This subsection focuses on parameter design, which is a key step that incorporates statistical design of experimentation. Taguchi (parameter) design is developed based on the idea of orthogonal arrays. For a system with f factors, each with l levels, an orthogonal array is an N by k matrix denoted by LN such that each possible combination of levels are repeated by the same number of times across the columns of this matrix.
Science-Based Test Case Design: Better Coverage, Fewer Tests
Published in Matthew Heusser, Govind Kulkarni, How to Reduce the Cost of Software Testing, 2018
Pairwise, combinatorial, and orthogonal are often used interchangeably. Pairwise implies only two-way combinations, whereas combinatorial suggests n-way combinations. An orthogonal array has the balancing property that for each pair of columns all parameter-level combinations occur an equal number of times. This is a fine distinction vis-à-vis two-way and will in practice generate slightly more test cases.
An artificial intelligence model for ballistic performance of thin plates
Published in Mechanics Based Design of Structures and Machines, 2023
Ravindranadh Bobbili, B. Ramakrishna, Vemuri Madhu
Orthogonal arrays are special standard experimental design that requires only a small number of experimental trials to find the main factors effects on output. Before selecting an orthogonal array, the minimum number of experiments to be conducted is to be fixed based on the formula below N Taguchi = 1+ NV (L − 1) N Taguchi = Number of experiments to be conducted NV = Number of parameters L = Number of levels. Based on this orthogonal array (OA) is to be selected which has at least 9 rows i.e.,9 experimental runs. The following standard orthogonal arrays are commonly used to design experiments: 2-Level Arrays: L4, L8, L12, L16, L32 3-Level Arrays: L9, L18, 4-Level Arrays: L16, L32 In this work L18 is sufficient. It would require a total of 18 experiments to optimize the parameters. Taguchi experimental design of experiments suggests L18 orthogonal array, where 18 experiments are sufficient to optimize the parameters.
Biodiesel production using heterogeneous catalyst, application of Taguchi robust design and response surface methodology to optimise diesel engine performance fuelled with Jatropha biodiesel blends
Published in International Journal of Ambient Energy, 2022
Aparna Singh, Shailendra Sinha, Akhilesh Kumar Choudhary, H. Chelladurai
Taguchi method which is widely used in engineering analysis because of extensive range of applications such as automobile, electronics and process industry is used in the present study. It is an offline analytical quality control approach in which the selection of level of input variable is done in such a manner to nullify the variation in response because of noise factors. Orthogonal array selection depends on the number of input variables and their levels (Phadke 1989). In the present study, focus is to maximise quality characteristics, i.e. BP and BTHE, therefore quality loss function for higher the better has been used. Mathematical equation is given below (Antony 2001). where is the quality characteristics or observed data and n is the number of experimental run (Antony 2001). S/N ratio with a higher the better characteristics can be expressed as The contribution of each input variables that affects output response has been determined by using ANOVA technique. Percentage contribution is usually represented by F-ratio. Higher the F-ratio more significant will be the input factor.
Grey-Taguchi and ANN based optimization of a better performing low-emission diesel engine fueled with biodiesel
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022
M. Gul, Asad Naeem Shah, Umair Aziz, Naveed Husnain, M. A. Mujtaba, Tasmiya Kousar, Rauf Ahmad, Muhammad Farhan Hanif
This methodology involved four steps: in first step Design of Experiments (DOE) is carried out with the help of L9 OA, in second and third steps experimental data are collected, and Grey-Taguchi method was to evaluate the most valuable input factors by using grey-relational coefficients & overall average grey relational grade. In the end, ANOVA analysis is employed to find the most significant factor then the output results are validated by ANN. In the present study, three input factors involving the nature of fuel, speed, and load have been studies. Three levels of variation for each factor are revealed in Table 3. The design of experiments is developed on the premise of the orthogonal array. The structural design of OA depends on the number of input factors & variation of levels. Maximum number of OA relies on the number of experiments conducted for every level of every input factor and it comes out as L9 (33) OA. Table 4 introduces the orthogonal array’s arrangement for the nature of fuel, speed, and load.