Cutting Force and Surface Roughness Optimizations in End Milling of GFRP Composites Utilizing BPNN-Firefly Method

The excessive cutting force that is generated in the end milling process of glass fiber-reinforced polymer (GFRP) composites can lower the surface quality. Hence, it is necessary to select the correct levels of end milling parameters to minimize the cutting force (CF) and surface roughness (SR). The parameters of the end milling process comprised the depth of cut (doc), spindle speed (n), and feeding speed (Vf). This study emphasized on the modeling and minimization of both CF and SR in the end milling of GFRP combo fabric by combining backpropagation neural network (BPNN) method and firefly algorithm (FA). The FA based BPNN was first performed to model the end-milling process and predict CF and SR. It was later also executed to obtain the best combination of end-milling parameter levels that would provide minimum CF and SR. The outcome of the confirmation experiments disclosed that the integration of BPNN and FA managed to accurately predict and substantially enhance the multi-objective characteristics

A study on the machinability of some metal alloys using grey TOPSIS method

The machinability of a material can be defined as the ease with which it can be machined. Materials with good machinability property require less power to cut, can be cut quickly, and easily obtain a good finish without wearing the tooling much. Therefore, to manufacture components economically, production engineers are challenged to discover ways to determine machinability of materials which mainly depends on their mechanical properties, as well as on other cutting conditions. In this paper, the machinability characteristics of alloys of three materials, i.e. aluminium, copper and steel are studied applying grey TOPSIS (technique for order preference by similarity to ideal solution) method. For each case, eight different alloys are considered whose machinability is evaluated based on different mechanical properties which are expressed in grey numbers. Using the adopted methodology, it now becomes easier for the manufacturers to select a particular alloy that can be easily machined. It is observed that A357RC, CuCr1Zr and AISI 5140 are the best machinable aluminium, copper and steel alloys respectively. It is also found that the ranking performance of grey TOPSIS method remains unaffected with the variation in greyness of the considered mechanical property values