الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Nowadays vertical and horizontal machining centers are used in many industriesranging from made to order companies, where complex one-off jobs are the norm, to multi-component manufacturing companies where a range of up to typically 200 smaller components are manufactured. Due to the high running cost of CNC
machines, many researches are introduced to optimize the total resident time by optimizing machining parameters, machining time, or non-productive time. Many typical factors that influence the optimum residence time of machine tool use, especially in the multi component situation where a long machine tables (or a pallet)
are composed of up to sixty different components and requires a tool magazine of over 100 tools. These typical factors are the location of components on the machine
table or on the pallet, the sequence in which the tools are selected, and either the order in which the individual component part programs are called or the manner in which the multicomponent part program is handwritten for the entire pallet. These factors influence the residence time and in particular the non-machining time (e.g. Tool changes, movements between different components and different machining features, pallet rotation (in the case of using the pallet faces)). The current work presents a novel methodology to improve the optimization of
multi-component, multi-tool and multi-axis non-productive tool path by optimizing the location of components according to the commonly used tools using hybrid optimization techniques followed by optimization of the non-productive time for the optimized workpiece location points. The presented work of general optimization of the non-productive tool path has been carried out in three different phases, data extraction and feature recognition, optimization of workpiece location, and optimization of non-productive time. Machining features are recognized using the data extracted from CAD file in DXF,
IGES, or STEP format. The reordering of workpieces (optimization of workpiece 3 location) is done using hybrid optimization techniques Rank order/ simulated annealing (ROSA) or Rank order/ Genetic algorithm (ROGA) or Rank order/ Simulated/ Genetic (ROSG) algorithms applied to a CNC milling machine to optimize the location of workpieces (reordering workpieces) according to the
commonly used tools. Finally, the optimization of non-productive time is done using simulated annealing (SA), Genetic algorithm (GA), Hybrid Simulated annealing/Genetic algorithm (SA/GA), Hybrid Genetic algorithm/ Simulated annealing (GA/SA), or Hybrid Genetic algorithm (HGA).
The usage of workpiece reordering methodology (which consumes about three minutes for the very complex cases) before doing the optimization of tool path method introduces more reduction of the non-productive time and solves the tradeoff
approaching global optimization and computational time. The purpose of using a simulated annealing algorithm was that it does not need a omplicated mathematical model of the problem under study and it does not need large computer memory, even at the expense of the optimization percent reached. However, the present study found
that using the workpiece reordering method with simulated annealing algorithm is yielding both benefits: the simple mathematical model and a considerable optimization percent to be very close of the optimization percent obtained when using Genetic algorithm which needs complex mathematical model. This suggests
that the introduction of workpiece reordering method to Genetic algorithm would lead to a greater optimization almost equal to the optimization obtained when using hybrid ptimization techniques with a high reduction in the run time. It can noticed the effect of workpiece location on the optimized non-productive time
using different optimization echniques as ROSA gives improvement from 7.88 %
to 28.313 % in the case of using machine table and gives 10.12 % to 51 % improvement in case of using machine pallet.