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Frontiers of Mechanical Engineering

Front. Mech. Eng.    2018, Vol. 13 Issue (2) : 232-242     https://doi.org/10.1007/s11465-018-0469-y
RESEARCH ARTICLE |
Tool path strategy and cutting process monitoring in intelligent machining
Ming CHEN1(), Chengdong WANG2, Qinglong AN1, Weiwei MING1
1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. School of Mechanical and Electric Engineering, Soochow University, Suzhou 215021, China
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Abstract

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

Keywords intelligent machining      tool path strategy      process optimization      online monitoring     
Corresponding Authors: Ming CHEN   
Just Accepted Date: 25 September 2017   Online First Date: 03 November 2017    Issue Date: 16 March 2018
 Cite this article:   
Ming CHEN,Chengdong WANG,Qinglong AN, et al. Tool path strategy and cutting process monitoring in intelligent machining[J]. Front. Mech. Eng., 2018, 13(2): 232-242.
 URL:  
http://journal.hep.com.cn/fme/EN/10.1007/s11465-018-0469-y
http://journal.hep.com.cn/fme/EN/Y2018/V13/I2/232
Fig.1  Three-dimensional model of the impeller
Fig.2  Geometrical features of the impeller blades
Fig.3  Surface projection machining strategy
Fig.4  Safe height clearance
Fig.5  Leads and links
Fig.6  Tool axis strategy (forward/roll)
Fig.7  Schematic of milling for tapered cutter
Fig.8  Tool-path commissioning for blade machining
Fig.9  Results of tool-path commissioning
Fig.10  Integrated impeller part
Fig.11  Anatomical tissue deformation: real-time simulation experiment photos and screenshots
Fig.12  Porcine jaw bone
Fig.13  Carbide micro-drilling
Fig.14  Experimental set-up of jaw micro-drilling
Fig.15  Change in the axial force during the drilling process
Fig.16  Change curve of the cutting temperature during the drilling process
Fig.17  Schematic diagram of the processing of the slide valve working edge
Fig.18  Method of burr removal. (a) Pressing with metallic sandpaper; (b) pressing of the outer ring with an alloy bar
Fig.19  Microscopic images before and after deburring
Fig.20  Main components of the automatic deburring device system
Fig.21  Schematic diagram of grinding machining and online deburring device
Fig.22  Effect of deburring the working edge
Fig.23  Fillet radius and edge angle of the working edge
1 Li X H, Li W Y. The research on intelligent monitoring technology of NC machining process. Procedia CIRP, 2016, 56: 556–560
https://doi.org/10.1016/j.procir.2016.10.109
2 Grzesik W. Chapter nineteen—Virtual/digital and internet-based machining. In: Grzesik W, ed. Advanced Machining Processes of Metallic Materials . 2nd ed. Amsterdam: Elsevier, 2017, 505–531
https://doi.org/10.1016/B978-008044534-2.50038-2
3 Zhou B, Zhao J, Li L. CNC double spiral tool-path generation based on parametric surface mapping. Computer Aided Design, 2015, 67–68: 87–106
https://doi.org/10.1016/j.cad.2015.06.005
4 Zhang R, Hu P, Tang K. Five-axis finishing tool path generation for a mesh blade based on linear morphing cone. Journal of Computational Design and Engineering, 2015, 2(4): 268–275
https://doi.org/10.1016/j.jcde.2015.06.013
5 W X, Cai Y. The tool path planning of composed surface of big-twisted blisk. Procedia Engineering, 2017, 174: 392–401
https://doi.org/10.1016/j.proeng.2017.01.158
6 Sun Y, Xu J, Jin C, et al. Smooth tool path generation for 5-axis machining of triangular mesh surface with nonzero genus. Computer Aided Design, 2016, 79: 60–74
https://doi.org/10.1016/j.cad.2016.06.001
7 Gowd G H, Goud M V, Theja K D, et al. Optimal selection of machining parameters in CNC turning process of EN-31 using intelligent hybrid decision making tools. Procedia Engineering, 2014, 97: 125–133
https://doi.org/10.1016/j.proeng.2014.12.233
8 Du X, Huang J, Zhu L. An analytical transition algorithm for real-time CNC machining of linear tool path. Procedia CIRP, 2016, 56: 344–348
https://doi.org/10.1016/j.procir.2016.10.037
9 Xu K, Wang J, Chu C H, et al. Cutting force and machine kinematics constrained cutter location planning for five-axis flank milling of ruled surfaces. Journal of Computational Design and Engineering (in press)
https://doi.org/10.1016/j.jcde.2017.02.003
10 Hammad A W A, Rey D, Akbarnezhad A. A cutting plane algorithm for the site layout planning problem with travel barriers. Computers & Operations Research, 2017, 82: 36–51
https://doi.org/10.1016/j.cor.2017.01.005
11 Sokolov I L, Cherkasov V R, Tregubov AA, et al. Smart materials on the way to theranostic nanorobots: Molecular machines and nanomotors, advanced biosensors, and intelligent vehicles for drug delivery. Biochimica et Biophysica Acta (BBA)—General Subjects, 2017, 1861(6): 1530–1544
https://doi.org/10.1016/j.bbagen.2017.01.027
12 Cai Z, Geng J, Zhang C, et al. Systematic solving of machining deformation and process optimization for complex thin-walled parts. Procedia CIRP, 2016, 56: 167–172
https://doi.org/10.1016/j.procir.2016.10.048
13 Toh C K. A modified offset cutter path strategy for high-speed rough milling hardened steel. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2003, 217(8): 1161–1166
https://doi.org/10.1177/095440540321700812
14 Zhou M, Zheng G, Chen S. Identification and looping tool path generation for removing residual areas left by pocket roughing. International Journal of Advanced Manufacturing Technology, 2016, 87(1–4): 765–778
https://doi.org/10.1007/s00170-016-8474-y
15 Fan H, Xi G, Wang W, et al. An efficient five-axis machining method of centrifugal impeller based on regional milling. International Journal of Advanced Manufacturing Technology, 2016, 87(1–4): 789–799
https://doi.org/10.1007/s00170-016-8467-x
16 Gao X, Mou W, Peng Y. An intelligent process planning method based on feature-based history machining data for aircraft structural parts. Procedia CIRP, 2016, 56: 585–589
https://doi.org/10.1016/j.procir.2016.10.115
17 Chen Y, Huang Z, Chen L, et al. Parametric process planning based on feature parameters of parts. International Journal of Advanced Manufacturing Technology, 2006, 28(7–8): 727–736
https://doi.org/10.1007/s00170-004-2428-5
18 Möhring H C, Wiederkehr P. Intelligent fixtures for high performance machining. Procedia CIRP, 2016, 46: 383–390
https://doi.org/10.1016/j.procir.2016.04.042
19 Lauro C H, Brandão L C, Baldo D, et al. Monitoring and processing signal applied in machining processes—A review. Measurement, 2014, 58: 73–86
https://doi.org/10.1016/j.measurement.2014.08.035
20 Dornfeld A D. In-process recognition of cutting states. JSME International Journal. Series C, Dynamics, Control, Robotics, Design and Manufacturing, 1994, 37(4): 638–650
https://doi.org/10.1299/jsmec1993.37.638
21 Cus F, Milfelner M, Balic J. An intelligent system for monitoring and optimization of ball-end milling process. Journal of Materials Processing Technology, 2006, 175(1–3): 90–97
https://doi.org/10.1016/j.jmatprotec.2005.04.041
22 Ratava J, Lohtander M, Varis J. Tool condition monitoring in interrupted cutting with acceleration sensors. Robotics and Computer-Integrated Manufacturing, 2017, 47: 70–75
https://doi.org/10.1016/j.rcim.2016.11.008
23 Frangos M. Uncertainty quantification for cuttings transport process monitoring while drilling by ensemble Kalman filtering. Journal of Process Control, 2017, 53: 46–56
https://doi.org/10.1016/j.jprocont.2017.02.008
24 Eufinger H, Wittkampf A R M, Wehmöller M, et al. Single-step fronto-orbital resection and reconstruction with individual resection template and corresponding titanium implant: A new method of computer-aided surgery. Journal of Cranio-Maxillo-Facial Surgery, 1998, 26(6): 373–378
https://doi.org/10.1016/S1010-5182(98)80070-X
25 Heidemann W, Gerlach K L, Gröbel K H, et al. Drill free screws: A new form of osteosynthesis screw. Journal of Cranio-Maxillo-Facial Surgery, 1998, 26(3): 163–168
https://doi.org/10.1016/S1010-5182(98)80007-3
26 Wang S, Yang J, Gee J C. Cranio-maxillofacial surgery simulation based on pre-specified target face configurations. Journal of Zhejiang University-Science C: Computers & Electronics, 2010, 11(7): 504–513
https://doi.org/10.1631/jzus.C0910349
27 Kim K H, Cho C H, Jeon S Y, et al. Drilling and deburring in a single process. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2003, 217(9): 1327–1331
https://doi.org/10.1243/095440503322420250
28 Kim K H, Park N J. A new deburring tool for intersecting holes. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2005, 219(12): 865–870
https://doi.org/10.1243/095440505X32850
29 Ton T P, Park H Y, Ko S L. Experimental analysis of deburring process on inclined exit surface by new deburring tool. CIRP Annals-Manufacturing Technology, 2011, 60(1): 129–132
https://doi.org/10.1016/j.cirp.2011.03.124
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