Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/69855
Title: Hybrid online surface roughness measurement using a robotic arm
Authors: Qin, Qin
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 2017
Abstract: Surface roughness is an important property in the engineering field. It is often used to determine the availability and function of finished parts in both assembly and machinery. However, commercial machines available currently for roughness distinguishing require high maintenance cost and are usually not precise enough. The objective of this project is to create a durable, effective and precise sensor useable in roughness discrimination. With the development of technology and science in the recent decades roughness testing has been brought to a whole new degree, especially the application concerning robots in the area of high technology such as medical, automobiles and semiconductor industries. Robot is known to use a technique called tactile sensing to distinguish the shape, texture or roughness of an object. Recent researches indicate that tactile sensors are used to simulate functions of a human finger in robotic finger. Tactile sensors are used to imitate the human mechanoreceptors which are divided into the Fast Adapting (FA) and Slow Adapting (SA). In this project, an artificial finger with piezoresistive sensors used to represent the SA mechanoreceptors and piezoelectric sensors used to represent the FA mechanoreceptors is used. Besides, a commercial optical sensor for roughness testing is also used in this project. The sensors adapted are able to produce signals and the signals generated will be processed and analyzed to evaluate the effectiveness of the two sensors in surface roughness testing.
URI: http://hdl.handle.net/10356/69855
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Theses

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