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Allowablevariationfromthenominaldimensionthat’stoleranceofacomponentplaysavitalroleinselectingmanufacturing process and functioning of the product while mating with other sub components and its manufacturing cost. Closer tolerance required secondary process which increases manufacturing cost in considerable amount. Selective assembly is a method where components are manufactured with wider tolerance, measured and partitioned into groups and the components in their corresponding groups are assembled together to form precision assemblies. This method reduces the cost involved in secondary operation but in the mean time the cost of measuring the components in additions with the existing random assembly process. A trade off between measuring cost of each components and secondary operation cost is the deciding factor in implementing the selective assembly techniques. Existing method mostly focuses on equal group numbers and equal group width either surplus parts or reducing the clearance variation or both. A new technique of variable group numbers according to their tolerance is suggested in this work and the precision assemblies are produced using the best bin combinations obtained using teaching-and learning-based optimization algorithm. The proposed method has been implemented on the existing problem and can able to produce close precision assemblies without any surplus parts with less manufacturing cost. It is established that the TLBO algorithm minimizes the clearance variation from 17.5 ym to 15ym in a linear assembly that consists of three gears in a gear box and from 17 m to 16 m in a ball bearing assembly in a single stage with zero surplus parts.

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