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research-article

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

[+] Author and Article Information
Jayanti Das

Mechanical & Aerospace Eng., University of California, Davis, California 95616
jydas@ucdavis.edu

Greg Bales

ASME Member, Mechanical & Aerospace Eng., University of California, Davis, California 95616
glbales@ucdavis.edu

Zhaodan Kong

ASME Member, Mechanical & Aerospace Eng., University of California, Davis, California 95616
zdkong@ucdavis.edu

Barbara Linke

ASME Member, Mechanical & Aerospace Eng., University of California, Davis, California 95616
bslinke@ucdavis.edu

1Corresponding author.

ASME doi:10.1115/1.4040266 History: Received November 09, 2017; Revised May 09, 2018

Abstract

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) 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 covariance (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 material removal rate. In the future, these findings can be used to predict product quality and instruct new practitioners.

Copyright (c) 2018 by ASME
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