0
Research Papers

Integrating Operator Information for Manual Grinding and Characterization of Process Performance Based on Operator Profile

[+] Author and Article Information
Jayanti Das

Mechanical & Aerospace Engineering,
University of California,
Davis, CA 95616
e-mail: jydas@ucdavis.edu

Gregory L. Bales

Mem. ASME
Mechanical & Aerospace Engineering,
University of California,
Davis, CA 95616
e-mail: glbales@ucdavis.edu

Zhaodan Kong

Mem. ASME
Mechanical & Aerospace Engineering,
University of California,
Davis, CA 95616
e-mail: zdkong@ucdavis.edu

Barbara Linke

Mem. ASME
Mechanical & Aerospace Engineering,
University of California,
Davis, CA 95616
e-mail: bslinke@ucdavis.edu

Manuscript received November 9, 2017; final manuscript received May 9, 2018; published online June 4, 2018. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 140(8), 081011 (Jun 04, 2018) (10 pages) Paper No: MANU-17-1697; doi: 10.1115/1.4040266 History: Received November 09, 2017; Revised May 09, 2018

Due to its high versatility and scalability, manual grinding is an important and widely used technology in production for rework, repair, deburring, and finishing of large or unique parts. To make the process more interactive and reliable, manual grinding needs to incorporate “skill-based design,” which models a person-based system and can go significantly beyond the considerations of traditional human factors and ergonomics to encompass both processing parameters (e.g., feed rate, tool path, applied forces, material removal rate (MRR)), and machined surface quality (e.g., surface roughness). This study quantitatively analyzes the characteristics of complex techniques involved in manual operations. A series of experiments have been conducted using subjects of different levels of skill, while analyzing their visual gaze, cutting force, tool path, and workpiece quality. Analysis of variance (ANOVA) and multivariate regression analysis were performed and showed that the unique behavior of the operator affects the process performance measures of specific energy consumption and MRR. In the future, these findings can be used to predict product quality and instruct new practitioners.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Lee, J. , Lapira, E. , Bagheri, B. , and Kao, H-A. , 2013, “ Recent Advances and Trends in Predictive Manufacturing Systems in Big Data Environment,” Manuf. Lett., 1(1), pp. 38–41. [CrossRef]
Gillespie, L. K. , 1999, Deburring and Edge Finishing Handbook, Society of Manufacturing Engineers, Dearborn, MI.
Bales, G. , Das, J. , Linke, B. , and Kong, Z. , 2016, “ Recognizing Gaze-Motor Behavioral Patterns in Manual Grinding Tasks,” Procedia Manuf., 5, pp. 106–121. [CrossRef]
Bales, G. L. , Das, J. , Tsugawa, J. , Linke, B. , and Kong, Z. , 2017, “ Digitalization of Human Operations in the Age of Cyber Manufacturing: Sensorimotor Analysis of Manual Grinding Performance,” ASME J. Manuf. Sci. Eng., 139(10), p. 101011. [CrossRef]
Francalanza, E. , Borg, J. , and Constantinescu, C. , 2017, “ Development and Evaluation of a Knowledge-Based Decision-Making Approach for Designing Changeable Manufacturing Systems,” CIRP J. Manuf. Sci. Technol., 16, pp. 81–101. [CrossRef]
Lee, J. , Bagheri, B. , and Kao, H.-A. , 2015, “ A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems,” Manuf. Lett., 3, pp. 18–23. [CrossRef]
Davim, J. P. , 2010, Surface Integrity in Machining, Springer, New York. [CrossRef]
Das, J. , and Linke, B. , 2017, “ Evaluation and Systematic Selection of Significant Multi-Scale Surface Roughness Parameters (SRPs) as Process Monitoring Index,” J. Mater. Process. Technol., 244, pp. 157–165.
Das, J. , and Linke, B. , 2016, “ Effect of Manual Grinding Operations on Surface Integrity,” Procedia CIRP, 45, pp. 95–98. [CrossRef]
Aurich, J. C. , Linke, B. , Hauschild, M. , Carrella, M. , and Kirsch, B. , 2013, “ Sustainability of Abrasive Processes,” CIRP Ann.-Manuf. Technol., 62(2), pp. 653–672. [CrossRef]
Gibson, K. , and Tierney, J. M. , 2011, “ The Evolution of Environmental Management Systems: Back to Basics,” Environ. Qual. Manage., 21(1), pp. 23–37. [CrossRef]
Bernstein, W. Z. , Mani, M. , Lyons, K. , Morris, K. , and Johansson, B. , 2016, “ An Open Web-Based Repository for Capturing Manufacturing Process Information,” ASME Paper No. DETC2016-59265.
Khellouki, A. , Rech, J. , and Zahouani, H. , 2007, “ The Effect of Abrasive Grain's Wear and Contact Conditions on Surface Texture in Belt Finishing,” Wear, 263(1–6), pp. 81–87. [CrossRef]
Klocke, F. , and Linke, B. , 2008, “ Mechanisms in the Generation of Grinding Wheel Topography by Dressing,” Prod. Eng., 2(2), pp. 157–163. [CrossRef]
Tönshoff, H. K. , Peters, J. , Inasaki, I. , and Paul, T. , 1992, “ Modelling and Simulation of Grinding Processes,” CIRP Ann.-Manuf. Technol., 41(2), pp. 677–688. [CrossRef]
Linke, B. , Duscha, M. , Klocke, F. , and Dornfeld, D. , 2011, “ Combination of Speed Stroke Grinding and High Speed Grinding With Regard to Sustainability,” 44th CIRP International Conference on Manufacturing Systems, Madison, WI, June 1–3. https://escholarship.org/uc/item/5qs5k8pv
Malkin, S. , and Guo, C. , 2008, Grinding Technology: Theory and Application of Machining With Abrasives, Industrial Press, New York.
Marinescu, I. D. , Hitchiner, M. P. , Uhlmann, E. , Rowe, W. B. , and Inasaki, I. , 2006, Handbook of Machining With Grinding Wheels, CRC Press, Boca Raton, FL. [CrossRef]
Linke, B. S. , Corman, G. J. , Dornfeld, D. A. , and Tönissen, S. , 2013, “ Sustainability Indicators for Discrete Manufacturing Processes Applied to Grinding Technology,” J. Manuf. Syst., 32(4), pp. 556–563. [CrossRef]
Zein, A. , Li, W. , Herrmann, C. , and Kara, S. , 2011, “ Energy efficiency Measures for the Design and Operation of Machine Tools: An Axiomatic Approach,” Glocalized Solutions for Sustainability in Manufacturing, Springer, Berlin, pp. 274–279. [CrossRef]
Rowe, W. B. , 2013, Principles of Modern Grinding Technology, William Andrew, Waltham, MA.
Mohan, N. , Ramachandra, A. , and Kulkarni, S. , 2005, “ Influence of Process Parameters on Cutting Force and Torque During Drilling of Glass–Fiber Polyester Reinforced Composites,” Compos. Struct., 71(3–4), pp. 407–413. [CrossRef]
Chakraborty, S. , Kar, S. , Dey, V. , and Ghosh, S. K. , 2017, “ Optimization and Surface Modification of Al-6351 Alloy Using SiC–Cu Green Compact Electrode by Electro Discharge Coating Process,” Surf. Rev. Lett., 24(1), p. 1750007. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Schematic of cutting force generation during manual grinding operation

Grahic Jump Location
Fig. 2

Input–output diagram of manual grinding process. Reprinted with permission from Das and Linke [9]. Copyright 2016 by Elsevier B.V.

Grahic Jump Location
Fig. 3

Graphical representation of UMP for manual grinding operation

Grahic Jump Location
Fig. 4

Experimental setup consists of eye tracking glasses, kinematic sensors, camera, and piezoelectric force sensor. Reprinted with permission from Bales et al. [4]. Copyright 2017 by The American Society of Mechanical Engineers.

Grahic Jump Location
Fig. 5

Tangential and normal force for each subject

Grahic Jump Location
Fig. 6

Changes of average surface roughness and MRR with tangential and normalized normal force variation

Grahic Jump Location
Fig. 7

Example grinding tool paths for each subject

Grahic Jump Location
Fig. 8

Autoregressive parameters α and β for each subject for all ten trials

Grahic Jump Location
Fig. 9

Normalized histograms of the α parameter for all ten trials

Grahic Jump Location
Fig. 10

Specific energy consumption over MRR

Grahic Jump Location
Fig. 11

Mean signal-to-noise ratio graph for average surface roughness

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In