Multi-dimensional Evaluation Methods for Enhancing the Teaching Effectiveness of Mechanical Courses

DOI

10.2991/978-94-6463-502-7_88How to use a DOI?

Keywords

Mechanical drawing; Machining errors; Evaluation parameters; Roughness; Fourier transforms

Abstract

Processing error is a key factor affecting the accuracy and quality of machined components. However, in the current course of Mechanical Drawing, the teaching of processing error and surface roughness is relatively weak, which means students cannot combine practical problems with theoretical knowledge. To solve this problem, novel multi-dimensional processing error assessment methods based on spectral analysis are introduced. This method not only overcomes the limitations of the traditional surface roughness evaluation but also reveals more in-depth and extensive error distribution characteristics and information. Introducing this method into teaching will provide more effective analytical tools for the teaching field of mechanical specialties, thus deepening students’ understanding and application of advanced analytical techniques.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lin Chang
AU  - Fangxiang Zhuang
AU  - Jiehua Gao
AU  - Jiamiao Wei
AU  - Chenxin Xie
PY  - 2024
DA  - 2024/08/31
TI  - Multi-dimensional Evaluation Methods for Enhancing the Teaching Effectiveness of Mechanical Courses
BT  - Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）
PB  - Atlantis Press
SP  - 832
EP  - 838
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-502-7_88
DO  - 10.2991/978-94-6463-502-7_88
ID  - Chang2024
ER  -