Tool Quality Life during Ball End Milling of Titanium Alloy Based on Tool Wear and Surface Roughness Models

The prediction and control of milling tool service performance is critical for milling tool design and machining. However, the existing prediction model can hardly quantify tool performance, or precisely describe the relationship between the tool performance and the design or milling parameters. This study redefines the tool lifetime as a function of surface roughness and proposes a new geometric analysis method based on a time-varying wear model. The proposed method can be utilized to evaluate the relationship between tool wear and lifetime. The surface roughness, with respect to tool service performance, is expressed as a time-varying model of the tool and processing parameters. After experimental validation, the influence factors were analyzed through simulation. A generalized method for milling tool design was proposed and successfully applied to a tool performance design case, on a theoretical level. Additionally, the research results prove that basing the tool milling quality life on the surface roughness is extremely feasible and necessary.

[1]  Wit Grzesik,et al.  Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools , 2008 .

[2]  Steven Y. Liang,et al.  Time-varying analytical model of ball-end milling tool wear in surface milling , 2020 .

[3]  Konstantinos-Dionysios Bouzakis,et al.  Determination of the chip geometry, cutting force and roughness in free form surfaces finishing milling, with ball end tools , 2003 .

[4]  计伟,et al.  基于特征的刀具“形-性-用”一体化设计方法 , 2016 .

[5]  Ki-Yong Lee,et al.  Simulation of surface roughness and profile in high-speed end milling , 2001 .

[6]  E. Abele,et al.  Modeling of process forces with respect to technology parameters and tool wear in milling Ti6Al4V , 2017, Prod. Eng..

[7]  Anselmo Eduardo Diniz,et al.  Correlating surface roughness, tool wear and tool vibration in the milling process of hardened steel using long slender tools , 2013 .

[8]  Yongsheng Su,et al.  An experimental investigation of effects of cooling/lubrication conditions on tool wear in high-speed end milling of Ti-6Al-4V , 2006 .

[9]  Kamlakar P Rajurkar,et al.  Tool path optimization based on wear prediction in Electric Arc Sweep Machining , 2020 .

[10]  Szymon Wojciechowski,et al.  Mechanical and technological aspects of micro ball end milling with various tool inclinations , 2017 .

[11]  Steven Y. Liang,et al.  A review of chatter vibration research in milling , 2019, Chinese Journal of Aeronautics.

[12]  Szymon Wojciechowski,et al.  Tool Life and Process Dynamics in High Speed Ball End Milling of Hardened Steel , 2012 .

[13]  Steven Y. Liang,et al.  Part Functionality Alterations Induced by Changes of Surface Integrity in Metal Milling Process: A Review , 2018, Applied Sciences.

[14]  Xiaowen Wang,et al.  Development of Empirical Models for Surface Roughness Prediction in Finish Turning , 2002 .

[15]  Laishui Zhou,et al.  Modeling and on-line simulation of surface topography considering tool wear in multi-axis milling process , 2015 .

[16]  Steven Y. Liang,et al.  Modeling of CBN Tool Flank Wear Progression in Finish Hard Turning , 2004 .

[17]  Luis Norberto López de Lacalle,et al.  Modelling of surface roughness in inclined milling operations with circle-segment end mills , 2018, Simul. Model. Pract. Theory.

[18]  Pingfa Feng,et al.  Temperature Modeling Analysis for Milling of Titanium Alloy , 2016 .

[19]  Amir H. Roohi,et al.  Evaluation of tool wear in high-speed face milling of Al/SiC metal matrix composites , 2019, Journal of the Brazilian Society of Mechanical Sciences and Engineering.

[20]  M. A. El Baradie,et al.  Prediction of tool life in end milling by response surface methodology , 1997 .

[21]  Zhiyong Chang,et al.  Evaluation method of product–service performance , 2012, Int. J. Comput. Integr. Manuf..

[22]  Giuliano Bissacco,et al.  Effect of cutting edge micro geometry on surface generation in ball end milling , 2019, CIRP Annals.

[23]  Z. M. Wang,et al.  Titanium alloys and their machinability—a review , 1997 .

[24]  Xianli Liu,et al.  A GA-based Optimization Algorithm for Cutting Tool “Shape-performance-application” Integrated Design Approach☆ , 2016 .

[25]  A. Mansour,et al.  Surface roughness model for end milling: a semi-free cutting carbon casehardening steel (EN32) in dry condition , 2002 .

[26]  J. Paulo Davim.  Book Review: Tribology of Metal Cutting by Viktor P. Astakhov , 2009 .

[27]  L. Tunc.  Smart tool path generation for 5-axis ball-end milling of sculptured surfaces using process models , 2019, Robotics and Computer-Integrated Manufacturing.

[28]  Konstantinos-Dionysios Bouzakis,et al.  Predictive model of tool wear in milling with coated tools integrated into a CAM system , 2013 .

[29]  Xianli Liu,et al.  Tool shape-performance-application integrated design approach: a development and a numerical validation , 2018 .

[30]  Turgay Kıvak,et al.  Evaluation of tool wear, surface roughness/topography and chip morphology when machining of Ni-based alloy 625 under MQL, cryogenic cooling and CryoMQL , 2020 .

[31]  A. Klik,et al.  Soil surface roughness measurement—methods, applicability, and surface representation , 2005 .

[32]  Abdel E. Bayoumi,et al.  Tool wear modeling through an analytic mechanistic model of milling processes , 1992 .

[33]  Che Hassan Che Haron,et al.  Investigation of surface topology in ball nose end milling process of Inconel 718 , 2019, Wear.