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

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

Zhaodan Kong

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

Barbara Linke

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.

Copyright © 2018 by ASME
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Fig. 1

Schematic of cutting force generation during manual grinding operation

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Fig. 2

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

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Fig. 3

Graphical representation of UMP for manual grinding operation

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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.

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Fig. 5

Tangential and normal force for each subject

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Fig. 6

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

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Fig. 7

Example grinding tool paths for each subject

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Fig. 8

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

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Fig. 9

Normalized histograms of the α parameter for all ten trials

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Fig. 10

Specific energy consumption over MRR

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Fig. 11

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



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