Validation of a Manual Methodology for Measuring Roundness and Cylindricity Tolerances

Abstract

This paper describes a practice-based methodology applied to roundness and cylindricity concepts. Traditionally, technicians encounter difficulties in understanding this topic, especially when they analyze the tolerances involved due to the complexity of their graphic interpretation. Additionally, it is mandatory for industrial engineers to acquire competencies that allow them to validate form and make decisions in this field. With the goal of enhancing the handling of measurement techniques involved in this topic and analyzing the results obtained, a methodology has been designed to address the roundness and cylindricity tolerance evaluation from different perspectives. Firstly, based on a conceptual analysis, an analytical procedure is introduced. Secondly, an engaging manual practice is implemented by using simple measuring instruments that allow the user to be involved in the development of the procedure applied. The conditions that tried to optimize the manual procedure to get good results were analyzed. Moreover, the results obtained under this perspective allow us to ensure that better skills can be acquired regarding the typical method based on the coordinate measuring machines. This experience has been validated based on the practice with ongoing users under a training period.

Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques

“…Diametral measurements are made with two-point measuring tools such as calipers or micrometers, whereas radial measurements require precision spindle instruments that are both expensive and time-consuming. These studies introduced a method for evaluating roundness and cylindricity tolerances, addressing difficulties encountered by technicians and engineers, focusing on optimizing manual procedures, and improving skills [34]. Furthermore, roundness profiles with pseudo and actual diameters were evaluated, and relationship models with neural network regression based on coordinate measuring machines were developed [35].…”