Taguchi-EM-AI Design Optimization Environment for SynRM Drives in Traction Applications

Multi-objective design optimization environments are used for electric vehicles and other traction applications to arrive at efficient motor drives. Typically, the environment includes characterization modules that involve the use of Electromagnetic Finite Element and State-Space models that require large number of iterations and computational time. This work proposes the utilization of a Taguchi orthogonal arrays method in conjunction with a Particle Swarm Optimization search algorithm to reduce computational time needed in the design optimization of electric motors for traction applications. The effectiveness of the Taguchi method in conjunction with the optimization environment is demonstrated in a case study involving a prototype of a Synchronous Reluctance Motor drive system.

Prediction on Compressive and Split Tensile Strengths of GGBFS/FA Based GPC

Based on rate constant concept, empirical models were presented for the predictions of age-dependent development of compressive and split tensile strengths of geopolymer concrete composite (GPCC) with fly ash (FA) blended with ground granulated blast furnace slag (GGBFS). The models were empirically developed based on a total of 180 cylindrical test results of GPCC. Six different independent factors comprising of curing temperature, the weight ratios of GGBFS/binder, the aggregate/binder, the alkali solution/binder, the Na2SiO3/NaOH, and the NaOH concentration were considered as the variables. The ANOVA analyses performed on Taguchi orthogonal arrays with six factors in three levels showed that the curing temperature and ratio of GGBFS to binder were the main contributing factors to the development of compressive strength. The models, functionalized with these contributing factors and equivalent age, reflect the level of activation energy of GPCC similar to that of ordinary Portland cement concrete (OPC) and a higher frequency of molecular collisions during the curing period at elevated temperature. The model predictions for compressive and split tensile strength showed good agreements with tested results.

Integration of Taguchi and Shainin DOE for Six Sigma improvement: an Indian case

Purpose The purpose of this paper is to demonstrate the application of Six Sigma/design of experiments (DOE) hybrid framework for improving damping force (DF) generation process in a shock absorber assembly unit. Design/methodology/approach The study adopted a case study research method with single case (holistic) design. This research design was found to be appropriate for testing the projected framework for integrating DOE approaches within Six Sigma define-measure-analyze-improve-control (DMAIC) cycle. In the proposed framework, Shainin’s component search technique (CST) was deployed at the “analysis” phase of DMAIC for the first stage filtering of process parameters, followed by the use of Taguchi orthogonal arrays (OA) at the “improve” phase for identifying the optimal setting of the parameters. Findings The application of Shanin CST facilitated in ascertaining that assembly component (piston with rebound stopper) was causing the variation and not the assembly process. Further, the use of Taguchi OA at the improve phase allowed the collection of necessary data to determine the significant piston parameters with minimum experimentation (eight experimental runs in this case as opposed to the expected 64) and analysis of variance on the collected data facilitated the selection of parameter settings to optimize the “critical to quality”, i.e. rebound DF. Originality/value This study provided a stimulus for wider application of integrated DOE approaches by the engineering community in the problem solving and the identification of parameters responsible for poor performance of the process.

A Bee Colony Optimization Approach for Mixed Blocking Constraints Flow Shop Scheduling Problems

The flow shop scheduling problems with mixed blocking constraints with minimization of makespan are investigated. The Taguchi orthogonal arrays and path relinking along with some efficient local search methods are used to develop a metaheuristic algorithm based on bee colony optimization. In order to compare the performance of the proposed algorithm, two well-known test problems are considered. Computational results show that the presented algorithm has comparative performance with well-known algorithms of the literature, especially for the large sized problems.

Identify Generators for 2k - p Experiments Using Taguchi Orthogonal Arrays

Two-level fractional factorial design is an efficient technique for experiments considering a large number of factors. To evaluate the efficiency and analyze the data for such a design, we need to know the generators for the design, so that, using the generators, we can generate its defining relation and alias structure. Although knowing the generators is important for a two-level fractional factorial design, it is not unusual in actual industrial situations for the generators used in the design to be lost or overlooked while the design is performed. Since Taguchi methods has been widely applied in industry, in this research, an efficient algorithm based on Taguchi orthogonal arrays (OA's) and interaction tables is developed to identify the generators for given designs. Furthermore, with the investigation of the insights of Taguchi OA's and interaction tables, this research may provide ideas for making Taguchi methods a simple tool for developing optimal designs for 2k - p experiments.