MULTI-OBJECTIVE OPTIMIZATION OF ENERGY CONSUMPTION AND SURFACE ROUGHNESS IN 5-AXIS CNC MACHINING USING THE NSGA-II METHOD
Optimization in CNC machining processes is essential for enhancing performance and efficiency. Energy consumption during the cutting process is a key optimization target, as researchers and manufacturers aim to achieve minimal energy use in CNC machining to reduce operational costs. On the other...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/84711 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Optimization in CNC machining processes is essential for enhancing performance
and efficiency. Energy consumption during the cutting process is a key optimization
target, as researchers and manufacturers aim to achieve minimal energy use in
CNC machining to reduce operational costs. On the other hand, consumers demand
high product quality, and one measure of this quality is surface roughness, with
lower values indicating better quality. The use of coolant in the cutting process can
reduce surface roughness, but it also increases energy consumption due to the
pump required to circulate the coolant. This study addresses the challenge of
minimizing energy consumption while simultaneously minimizing surface
roughness in products cut using a Hartford 5A-25R 5-Axis CNC machine. Five
parameters, each with three levels—spindle speed, feed rate, depth of cut, width of
cut, and coolant mode—were investigated. A Taguchi Orthogonal array L27
Design of Experiment was employed to derive regression equations for the multi-
objective optimization process using the Non-dominated Sorting Genetic Algorithm
II (NSGA-II). The NSGA-II process yielded a Pareto optimal front, representing
optimal values for both objectives without dominance, indicating a trade-off
between energy consumption and surface roughness. Experimental verification of
the optimal front solutions showed errors below 10% for each objective, which is
within acceptable limits.
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