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In this study, an attempt was made to assess the genetic variability of kernel micronutrients and to enrich the micronutrientcontent in groundnut. Twenty one different genotypes and three cultivars were evaluated for micronutrient response underalfisol condition. To enhance the soil micronutrient availability recommended dose of Fe and Zn were applied as basal. Theexperiment was conducted at Dryland Agricultural Research Station, Chettinad under rainfed condition during August toDecember 2011. Yield parameters and micronutrients content in kernel were assessed. In alfisol, basal application of Zn andFe enhanced the soil available Zn and Fe content. Basal application of micronutrients increased the kernel Zn content to anaverage of 28.7 per cent and pod yield of 12.6 per cent in groundnut cultivars. Significant genetic variability was observedamong genotypes for Fe, Zn, Cu and Mn enrichment in kernel. The accessions ICGV 07225, ICGV 07222, ICGV 07247and ICGV 07220 were having inherent ability to load high zinc content in kernel with high pod yield per plant. Fordeveloping high yielding zinc dense cultivars, the genotype identified in this study are to be tested in diverse environmentsfor their stability

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many other's. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques. [DOI: 10.1115/1.2037085] Keywords: Optimization, Surface Grinding, Multi-Objective, Particle Swarm Optimization

Process planning, as a critical stage integrating the design and manufacturing phase in a manufacturing environment, has been automated to meet the needs for higher productivity and lower production cost. Being an input to various systems such as scheduling and routing, process planning results are of great importance in the manufacturing stage. Though feature extraction and sequence optimization have been given much attention, the process parameters are rarely dealt with. This paper focuses on the development of a new generative computer aided process planning (CAPP) framework for rotational components. The developed framework includes modules for feature extraction based on CAD application programming interfaces, determination of the optimum sequence and generation of optimum process parameters. The optimization of the machining operations is achieved using the evolutionary technique. The approach resulted in the reduction and prediction of machining time and cost. The framework is demonstrated with a case study. [ABSTRACT FROM AUTHOR]

In the design of a machining fixture configuration (layout), the stability of the fixture, which influences the dimensional and form accuracy of the workpiece, is an important factor. The optimal placement of the fixturing elements minimizes the dynamic response of the workpiece (elastic deformation of the workpiece under dynamic conditions), which in turn improves the stability of the fixture. In this paper, an Ant Colony Algorithm (ACA) and Genetic Algorithm (GA) are employed to optimize the fixture layout with an objective of minimizing the dynamic response of the workpiece. The different node systems are used on the same workpiece geometry, and the fixture layout is optimized so that the elastic deformation of the workpiece satisfies the accuracy needed on the workpiece. Two different examples are used to compare the performance of ACA- and GA-based fixture-layout optimization methods. [ABSTRACT FROM AUTHOR]

Tolerance allocation to individual parts in any assembly should be a vital design function with which both the design and manufacturing engineers are concerned. Generally design engineers prefer to have tighter tolerances to ensure the quality of their design, whereas manufacturing engineers prefer loose tolerances for ease of production and the need to be economical. This paper introduces a concurrent tolerance approach, which determines optimal product tolerances and minimizes combined manufacturing and quality related costs in the early stages of design. A non-linear multivariable optimization model is formulated here for assembly. A combinatorial optimization problem by treating cost minimization as the objective function and stack-up conditions as the constraints are solved using scatter search algorithm. In order to further explore the influence of geometric tolerances in quality as well as in the manufacturing cost, position control is included in the model. The results show how position control enhances quality and reduces cost. [ABSTRACT FROM AUTHOR]