Experimental and theoretical investigation of milling tool selection towards energy-efficient process planning in discrete parts manufacturing

Recently, energy-efficient process planning has aroused numerous concerns from both industry and academia. However, as one of the most important tasks of process planning in discrete parts manufacturing system, tool selection has seldom been investigated in terms of energy efficiency. Without a good understanding of its effects on energy efficiency of machining process, energy-efficient manufacturing cannot be achieved to a satisfactory level. To bridge this research gap, this paper studies tool selection effects by analysing tools with different geometrical parameters, i.e. cutter radius, flute number and helix angle. Their effects on energy efficiency of slot milling process are analysed using experimental approach and theoretical approach, respectively. In experimental approach, Taguchi method is firstly applied to study the effects of each tool geometrical parameter. Then, analysis of variance (ANOVA) is performed to study the significance of each factor. In theoretical approach, the influence of tool selection on cutting power is analytically revealed. Subsequently, the energy efficiency of different cutters is quantitatively analysed through numerical experiments. The results are quite promising with both approaches leading to the same conclusion. The rank of influence is cutter radius > flute number > helix angle. Both methods suggest that a milling cutter with a larger cutter radius and fewer flutes will improve the energy efficiency in milling process. Although there is a small discrepancy in helix angle, it does not affect the good agreement of these two methods since the influence of the helix angle is nearly negligible. Compared to experimental approach, theoretical approach may have more application potential since it may help reduce time and waste of materials. To the best knowledge of the authors, the effects of tool selection on energy efficiency in milling process are systematically analysed for the first time, which significantly advances the state of the art in energy-efficient process planning.