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Multiple-objective optimisation of machining operations based on neural networks

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Metal cutting plays an important role in manufacturing industries. Optimisation of cutting parameters represents a key component in machining process planning. In this paper, a neural network based approach to multiple-objective optimization of cutting parameters is presented. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers' preference structures. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.

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v :

cutting speed (m/min)

f :

feed rate per revolution (mm/rev)

d :

depth of cut per pass (mm)

T p :

total operation time per part (min)

T i :

set-up time per part (min)

T c :

tool change time (min)

T i :

idle time per part (min)

C p :

cost per part ($)

C t :

cost of tool per piece ($)

C l :

labor cost per unit time ($/min)

C o :

overhead per unit time ($/min)

V :

volume to be removed per part (mm3)


metal removal rate (mm3/min)

TL :

tool life (min)

SR :

surface roughness (μm)

H p :

arithmetic centre-line average (μm)

P :

cutting power (kW)

F :

cutting force (kg)


interface temperature (°C)


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Correspondence to Dr Jun Wang.

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Wang, J. Multiple-objective optimisation of machining operations based on neural networks. Int J Adv Manuf Technol 8, 235–243 (1993). https://doi.org/10.1007/BF01748633

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  • Cutting parameter optimisation
  • Neural networks
  • Machining operations
  • Metal cutting