Pramod Kumar Jain

TL;DR: In this paper, a novel approach based on the module interactions and machine capability is proposed to measure the machine reconfigurability and operational capability of an RMT and the developed performance measures along with cost are considered as the multiple objectives for the optimal machine assignment for a single part flow line allowing paralleling of similar machines.

TL;DR: In this paper, an attempt has been made to use vibration signals for in-process prediction of surface roughness during turning of Ti-6Al-4V alloy, and an artificial neural network model was developed as it can be readily integrated into a computer integrated manufacturing environment.

TL;DR: Genetic algorithm, a non-traditional global optimization technique has been proposed as the solution methodology for its inherent advantages and application of the methodology has been demonstrated with the help of suitable examples.

TL;DR: In this paper, a methodology is developed to assess the responsiveness of an RMT through developing the operational capability and machine reconfigurability metrics, which has been numerically illustrated and it has been observed that with a marginal increase in the cost, the RMTs offering higher responsiveness are available for the required operations.

TL;DR: In this paper, the authors have developed a novel operation sequence based BMIM (bypassing moves and idle machines) similarity coefficient using longest common subsequence (LCS) and the minimum number of bypassing move and the quantity of idle machine.