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Research Papers

A Systematic Approach for Online Minimizing Volume Difference of Multiple Chambers in Machining Processes Based on High-Definition Metrology

[+] Author and Article Information
De-Lin Huang

State Key Lab of Mechanical System
and Vibration,
Shanghai Jiaotong University,
Shanghai 200240, China;
School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: cjwanan@sjtu.edu.cn

Shi-Chang Du

State Key Lab of Mechanical System
and Vibration,
Shanghai Jiaotong University,
Shanghai 200240, China;
School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: lovbin@sjtu.edu.cn

Gui-Long Li

School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: lgllg68629315@qq.com

Zhuo-Qi Wu

School of Mechanical Engineering,
Shanghai Jiaotong University,
Shanghai 200240, China
e-mail: zqwu@sjtu.edu.cn

1Corresponding author.

Manuscript received May 31, 2016; final manuscript received January 26, 2017; published online May 8, 2017. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 139(8), 081003 (May 08, 2017) (17 pages) Paper No: MANU-16-1306; doi: 10.1115/1.4035897 History: Received May 31, 2016; Revised January 26, 2017

The volume variation of multiple chambers of a workpiece is one of the most important factors that can directly influence the performance of the final product. This paper presents a novel systematic approach for online minimizing the volume difference of multiple chambers of a workpiece based on high-definition metrology (HDM). First, the datum of high-density points is transformed by a random sample consensus (RANSAC) algorithm due to its good robustness in fitting. Second, a procedure containing reconstruction of interior curved surfaces of chambers, boundary extraction, and projection is developed to calculate the accurate volumes of the multiple chambers. Third, a model for obtaining an optimized machining parameter for depth of chambers is explored to minimize the volume difference of any two ones of all the chambers. The model is formulated as a multi-objective optimization (MOO) problem, and a new procedure of multi-objective particle swarm optimization (MOPSO) algorithm is developed to solve this problem. Finally, a milling depth is output as the optimal milling parameter for controlling the volume variation of multiple chambers. The results of a case study show that the proposed approach can minimize the volume difference of four combustion chambers of a cylinder head and it can be well applied online in volume variation control of multiple chambers in machining processes.

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References

Coherix, 2010, “ Introducing the Latest in High-Definition, Non-Contact Metrology Shapix 1500 Series,” Coherix, Ann Arbor, MI, http://www.coherix.com
Du, S. , Liu, C. , and Huang, D. , 2015, “ A Shearlet-Based Separation Method of 3D Engineering Surface Using High Definition Metrology,” Precis. Eng., 40, pp. 55–73. [CrossRef]
Du, S. , Liu, C. , and Xi, L. , 2015, “ A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification Using High Definition Metrology,” ASME J. Manuf. Sci. Eng., 137(1), p. 011003. [CrossRef]
Du, S. C. , Huang, D. L. , and Wang, H. , 2015, “ An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology,” IEEE Trans. Instrum. Meas., 64(10), pp. 2590–2604. [CrossRef]
Du, S. , and Fei, L. , 2016, “ Co-Kriging Method for Form Error Estimation Incorporating Condition Variable Measurements,” ASME J. Manuf. Sci. Eng., 138(4), p. 041003. [CrossRef]
Wang, M. , Ken, T. , Du, S. , and Xi, L. , 2015, “ Tool Wear Monitoring of Wiper Inserts in Multi-Insert Face Milling Using Three-Dimensional Surface Form Indicators,” ASME J. Manuf. Sci. Eng., 137(3), p. 031006. [CrossRef]
Wang, M. , Shao, Y. P. , Du, S. C. , and Xi, L. F. , 2015, “ A Diffusion Filter for Discontinuous Surface Measured by High Definition Metrology,” Int. J. Precis. Eng. Manuf., 16(10), pp. 2057–2062. [CrossRef]
Wang, M. , Xi, L. , and Du, S. , 2014, “ 3D Surface Form Error Evaluation Using High Definition Metrology,” Precis. Eng., 38(1), pp. 230–236. [CrossRef]
Suriano, S. , Wang, H. , Shao, C. , Hu, S. J. , and Sekhar, P. , 2015, “ Progressive Measurement and Monitoring for Multi-Resolution Data in Surface Manufacturing Considering Spatial and Cross Correlations,” IIE Trans., 47(10), pp. 1033–1052. [CrossRef]
Nguyen, H. T. , Wang, H. , Tai, B. L. , Ren, J. , Hu, S. J. , and Shih, A. , 2016, “ High-Definition Metrology Enabled Surface Variation Control by Cutting Load Balancing,” ASME J. Manuf. Sci. Eng., 138(2), p. 021010. [CrossRef]
Cho, H. , Luck, R. , and Stevens, J. W. , 2015, “ An Improvement on the Standard Linear Uncertainty Quantification Using a Least-Squares Method,” J. Uncertainty Anal. Appl., 3(1), pp. 1–13. [CrossRef]
Hongn, M. , Larsen, S. F. , Gea, M. , and Altamirano, M. , 2015, “ Least Square Dased Method for the Estimation of the Optical End Loss of Linear Fresnel Concentrators,” Sol. Energy, 111, pp. 264–276. [CrossRef]
Anselone, P. , and Rall, L. , 1968, “ The Solution of Characteristic Value-Vector Problems by Newton's Method,” Numer. Math., 11(1), pp. 38–45. [CrossRef]
Fischler, M. A. , and Bolles, R. C. , 1981, “ Random Sample Consensus: A Paradigm for Model Fitting With Applications to Image Analysis and Automated Cartography,” Commun. ACM, 24(6), pp. 381–395. [CrossRef]
Kim, T. , and Im, Y. J. , 2003, “ Automatic Satellite Image Registration by Combination of Matching and Random Sample Consensus,” IEEE Trans. on Geosci. Remote Sens., 41(5), pp. 1111–1117. [CrossRef]
Yaniv, Z. , 2010, “ Random Sample Consensus (RANSAC) Algorithm: A Generic Implementation,” Insight Journal.
Raguram, R. , Chum, O. , and Pollefeys, M. , 2013, “ USAC: A Universal Framework for Random Sample Consensus,” IEEE Trans. Pattern Anal. Mach. Intell., 35(8), pp. 2022–2038. [CrossRef] [PubMed]
Leon, S. J. , Björck, Å. , and Gander, W. , 2013, “ Gram–Schmidt Orthogonalization: 100 Years and More,” Numer. Linear Algebra Appl., 20(3), pp. 492–532. [CrossRef]
Pomerleau, F. , Colas, F. , Siegwart, R. , and Magnenat, S. , 2013, “ Comparing ICP Variants on Real-World Data Sets,” Auton. Rob., 34(3), pp. 133–148. [CrossRef]
Di Maio, F. , Bandini, A. , Zio, E. , Alfonsi, A. , and Rabiti, C. , 2016, “ An Approach Based on Support Vector Machines and a K-D Tree Search Algorithm for Identification of the Failure Domain and Safest Operating Conditions in Nuclear Systems,” Prog. Nucl. Energy, 88, pp. 297–309. [CrossRef]
Schauer, J. , and Nüchter, A. , 2014, “ Efficient Point Cloud Collision Detection and Analysis in a Tunnel Environment Using Kinematic Laser Scanning and KD Tree Search,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 40(3), pp. 289–295. [CrossRef]
Besl, P. J. , and McKay, H. D. , 1992, “ A Method for Registration of 3-D Shapes,” IEEE Trans. Pattern Anal. Mach. Intell., 14(2), pp. 239–256. [CrossRef]
Du, S. , and Xi, L. , 2011, “ Fault Diagnosis in Assembly Processes Based on Engineering-Driven Rules and PSOSAEN Algorithm,” Comput. Ind. Eng., 60(1), pp. 77–88. [CrossRef]
Poli, R. , Kennedy, J. , and Blackwell, T. , 2007, “ Particle Swarm Optimization,” Swarm Intell., 1(1), pp. 33–57. [CrossRef]
Coello, C. A. C. , and Lechuga, M. S. , 2002, “ MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization,” IEEE Proceedings of the 2002 Congress on Evolutionary Computation, (CEC), Washington, DC, May 12–17, pp. 1051–1056.
Reyes-Sierra, M. , and Coello, C. C. , 2006, “ Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art,” Int. J. Comput. Intell. Res., 2(3), pp. 287–308.
Zhang, Y. , Gong, D. , and Zhang, J. , 2013, “ Robot Path Planning in Uncertain Environment Using Multi-Objective Particle Swarm Optimization,” Neurocomputing, 103, pp. 172–185. [CrossRef]
MVTec Software GmbH, 2010, “Halcon Solution Guide III-C 3D Vision,” MVTec Software GmbH, München, Germany, http://download.mvtec.com/halcon-9.0-solution-guide-iii-c-3d-vision.pdf
Dorsch, R. , Häusler, G. , and Herrmann, J. , 1994, “ Laser Triangulation: Fundamental Uncertainty in Distance Measurement,” Appl. Opt., 33(7), pp. 1306–1314. [CrossRef] [PubMed]

Figures

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Fig. 1

A cylinder head with four combustion chambers and offline volume measurement: (a) cylinder head, (b) a combustion chamber, and (c) titration methods

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Fig. 2

High-density points measured by new HDM technology: (a) point cloud of a cylinder head and (b) point cloud of a combustion chamber

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Fig. 4

The framework of the proposed approach

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Fig. 5

The structure of input data collected from a cylinder head: (a) point cloud collected from a cylinder head, (b) local distribution of the collected point cloud, and (c) the detailed form of the collected point cloud as input data

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Fig. 6

The description of RANSAC algorithm

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Fig. 7

An illustration of Gram–Schmidt transformation

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Fig. 8

Area of missing points in the collected point cloud

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Fig. 9

Large blank areas in the collected point cloud

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Fig. 10

Interpolations needed in a row

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Fig. 11

Schematic diagram of structured point cloud mesh generation: (a) sequential point cloud, (b) quadrilateral mesh, and (c) triangular mesh

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Fig. 12

Direction indications of row and column

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Fig. 13

An illustration of determining initial row and terminal row

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Fig. 14

Segmentation of convex pentahedron

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Fig. 15

Area calculation of triangle with three known vertex coordinates

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Fig. 16

The cutaway picture of the chamber with milled part

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Fig. 17

Calculation of the optimal milling parameter of a single combustion chamber

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Fig. 18

Multi-objective PSO algorithm

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Fig. 19

The procedure of MOPSO algorithm

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Fig. 20

The diagram of Pareto front

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Fig. 21

Cylinder head of B12 engine with four combustion chambers

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Fig. 22

Online measurement equipment based on HDM system: (a) measurement component and (b) industrial personal computer

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Fig. 23

The measurement process: (a) locate, (b) clamp, and (c) flip over

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Fig. 24

Actual operation of the online measurement equipment: (a) online measurement and (b) an example of measurement

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Fig. 25

Process of machining combustion chambers of cylinder head

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Fig. 26

Point cloud before and after datum transformation: (a) point cloud measured by HDM and (b) point cloud transformed by RANSAC

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Fig. 27

Point cloud before and after filtering: (a) point cloud before filtering and (b) point cloud after filtering

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Fig. 28

Cylinder head combustion chamber surfaces after reconstruction: (a) reconstruction result of a combustion chamber and (b) local amplification of the reconstructed surface of combustion chambers

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Fig. 29

Statistical averages of Z coordinates of each point cloud row

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Fig. 30

Efficient point cloud boundary extraction

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Fig. 31

Cylinder head combustion chamber boundary

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Fig. 32

Maximum volume variation of combustion chambers of each cylinder head before and after milling

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Fig. 33

Comparison of the DR of optimal strategy and nonoptimal strategy

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Fig. 34

Comparison of the MVRR of optimal strategy and nonoptimal strategy

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Fig. 37

Comparison of volume variation of combustion chambers of each cylinder head before and after milling: (a) comparison of first cylinder head, (b) comparison of second cylinder head, (c) comparison of third cylinder head, (d) comparison of fourth cylinder head, (e) comparison of fifth cylinder head, (f) comparison of sixth cylinder head, (g) comparison of seventh cylinder head, (h) comparison of eighth cylinder head, (i) comparison of ninth cylinder head, and (j) comparison of tenth cylinder head

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