A mechanics based prediction model for tool wear and power consumption in drilling operations and its applications

Abstract We present a mechanics based model for predicting the power consumption of drilling operations. Different from existing power models in machining that ignore the tool wear, our model takes into full consideration the tool wear which is particularly pronounced in drilling and causes extra power consumption. For any given spindle speed n and feed rate f, our model establishes the relationship between the length of drill and the total power consumption as well as the amount of tool wear. With this prediction model established, we can then optimize the drilling parameters (n, f) towards different objectives, such as the two applications reported in this paper – to minimize the average power consumption per unit length of drill and to maximize the tool usage before its replacement. Physical drilling experiments of the proposed power prediction model and its two optimization applications are also reported in this paper which have validated the accuracy of the model and convincingly demonstrated its efficacy in deciding optimal drilling parameters (n, f) for energy minimization and other objectives.

[1]  Yusuf Altintas,et al.  Prediction of Milling Force Coefficients From Orthogonal Cutting Data , 1996 .

[2]  Carmita Camposeco-Negrete,et al.  Sustainable machining as a mean of reducing the environmental impacts related to the energy consumption of the machine tool: a case study of AISI 1045 steel machining , 2019, The International Journal of Advanced Manufacturing Technology.

[3]  G. Rutelli,et al.  Tool wear monitoring based on cutting power measurement , 1990 .

[4]  Ming Luo,et al.  Machine Based Energy-Saving Tool Path Generation for Five-Axis End Milling of Freeform Surfaces , 2016 .

[5]  Hua Zhang,et al.  An energy consumption optimization strategy for CNC milling , 2017 .

[6]  Chris Yuan,et al.  A General Empirical Energy Consumption Model for Computer Numerical Control Milling Machine , 2019 .

[7]  Tao Peng,et al.  Decision rules for energy consumption minimization during material removal process in turning , 2017 .

[8]  Junxue Ren,et al.  A novel energy consumption model for milling process considering tool wear progression , 2018 .

[9]  Ming Luo,et al.  Milling Force Modeling of Worn Tool and Tool Flank Wear Recognition in End Milling , 2015, IEEE/ASME Transactions on Mechatronics.

[10]  Li Li,et al.  Impact of surface machining complexity on energy consumption and efficiency in CNC milling , 2019, The International Journal of Advanced Manufacturing Technology.

[11]  María Henar Miguélez,et al.  Influence of cutting parameters on tool wear and hole quality in composite aerospace components drilling , 2017 .

[12]  Itziar Cabanes,et al.  A computer assistant for monitoring tool performance during the drilling process , 2012, Int. J. Comput. Integr. Manuf..

[13]  Wen Feng Lu,et al.  A hybrid approach to energy consumption modelling based on cutting power: a milling case , 2015 .

[14]  Chao Zhang,et al.  Optimization of cutting parameters considering tool wear conditions in low-carbon manufacturing environment , 2019, Journal of Cleaner Production.

[15]  Jie Sun,et al.  Modeling of cutting force under the tool flank wear effect in end milling Ti6Al4V with solid carbide tool , 2013 .

[16]  Sung-Hoon Ahn,et al.  Control of machining parameters for energy and cost savings in micro-scale drilling of PCBs , 2013 .

[17]  S. Palanisamy,et al.  Effect of cryogenic compressed air on the evolution of cutting force and tool wear during machining of Ti-6Al-4V alloy , 2015 .

[18]  Jeffrey B Dahmus,et al.  Thermodynamic analysis of resources used in manufacturing processes. , 2009, Environmental science & technology.

[19]  Shun Jia,et al.  Energy modeling method of machine-operator system for sustainable machining , 2018, Energy Conversion and Management.

[20]  Michael P Sealy,et al.  Energy consumption and process sustainability of hard milling with tool wear progression , 2016 .

[21]  Sami Kara,et al.  Unit process energy consumption models for material removal processes , 2011 .

[22]  H. Shao,et al.  A cutting power model for tool wear monitoring in milling , 2004 .

[23]  Yan He,et al.  An on-line approach for energy efficiency monitoring of machine tools , 2012 .

[24]  Dinghua Zhang,et al.  Improving tool life in multi-axis milling of Ni-based superalloy with ball-end cutter based on the active cutting edge shift strategy , 2018 .

[25]  Sami Kara,et al.  An empirical model for predicting energy consumption of manufacturing processes: a case of turning process , 2011 .

[26]  Yan He,et al.  Characteristics of Additional Load Losses of Spindle System of Machine Tools , 2010 .

[27]  Sung-Hoon Ahn,et al.  Empirical power-consumption model for material removal in three-axis milling , 2014 .

[28]  Dinghua Zhang,et al.  Chip evacuation force modelling for deep hole drilling with twist drills , 2018, The International Journal of Advanced Manufacturing Technology.

[29]  Yue Meng,et al.  Cutting energy consumption modelling for prismatic machining features , 2019, The International Journal of Advanced Manufacturing Technology.

[30]  Franck Girot,et al.  Modeling and tool wear in drilling of CFRP , 2010 .

[31]  Xingtao Wang,et al.  Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear , 2018 .

[32]  Lin Li,et al.  Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling , 2013 .

[33]  Marc Thomas,et al.  Specific cutting energy: a physical measurement for representing tool wear , 2019 .

[34]  J. K. Watson,et al.  A decision-support model for selecting additive manufacturing versus subtractive manufacturing based on energy consumption. , 2018, Journal of cleaner production.

[35]  Yusuf Altintas,et al.  Prediction of ball-end milling forces from orthogonal cutting data , 1996 .