@article{(Open Science Index):https://publications.waset.org/pdf/9999364,
	  title     = {Minimization of Non-Productive Time during 2.5D Milling},
	  author    = {Satish Kumar and  Arun Kumar Gupta and  Pankaj Chandna},
	  country	= {},
	  institution	= {},
	  abstract     = {In the modern manufacturing systems, the use of
thermal cutting techniques using oxyfuel, plasma and laser have
become indispensable for the shape forming of high quality complex
components; however, the conventional chip removal production
techniques still have its widespread space in the manufacturing
industry. Both these types of machining operations require the
positioning of end effector tool at the edge where the cutting process
commences. This repositioning of the cutting tool in every machining
operation is repeated several times and is termed as non-productive
time or airtime motion. Minimization of this non-productive
machining time plays an important role in mass production with high
speed machining. As, the tool moves from one region to the other by
rapid movement and visits a meticulous region once in the whole
operation, hence the non-productive time can be minimized by
synchronizing the tool movements. In this work, this problem is
being formulated as a general travelling salesman problem (TSP) and
a genetic algorithm approach has been applied to solve the same. For
improving the efficiency of the algorithm, the GA has been
hybridized with a noble special heuristic and simulating annealing
(SA). In the present work a novel heuristic in the combination of GA
has been developed for synchronization of toolpath movements
during repositioning of the tool. A comparative analysis of new Meta
heuristic techniques with simple genetic algorithm has been
performed. The proposed metaheuristic approach shows better
performance than simple genetic algorithm for minimization of nonproductive
toolpath length. Also, the results obtained with the help of
hybrid simulated annealing genetic algorithm (HSAGA) are also
found better than the results using simple genetic algorithm only.
},
	    journal   = {International Journal of Industrial and Manufacturing Engineering},
	  volume    = {8},
	  number    = {6},
	  year      = {2014},
	  pages     = {1155 - 1160},
	  ee        = {https://publications.waset.org/pdf/9999364},
	  url   	= {https://publications.waset.org/vol/90},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 90, 2014},
	}