Next Article in Journal
Distributed Remote E-Voting System Based on Shamir’s Secret Sharing Scheme
Next Article in Special Issue
A Machine Learning Method for Classification of Cervical Cancer
Previous Article in Journal
SDSWSN—A Secure Approach for a Hop-Based Localization Algorithm Using a Digital Signature in the Wireless Sensor Network
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Cam Mechanisms Reverse Engineering Based on Evolutionary Algorithms

Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, 25123 Brescia, Italy
*
Author to whom correspondence should be addressed.
Electronics 2021, 10(24), 3073; https://doi.org/10.3390/electronics10243073
Submission received: 5 November 2021 / Revised: 4 December 2021 / Accepted: 7 December 2021 / Published: 9 December 2021
(This article belongs to the Special Issue Machine Learning Applications to Signal Processing)

Abstract

Cam follower mechanisms are widely used in automated manufacturing machinery to transform a rotary stationary motion into a more general required movement. Reverse engineering of cams has been studied, and some solutions based on different approaches have been identified in the literature. This article proposes an innovative method based on the use of an evolutionary algorithm for the identification of a law of motion that allows for approximating in the best way the motion or the sampled profile on the physical device. Starting from the acquired data, through a genetic algorithm, a representation of the movement (and therefore of the cam profile) is identified based on a type of motion law traditionally used for this purpose, i.e., the modified trapezoidal (better known as modified seven segments). With this method it is possible to estimate the coefficients of the parametric motion law, thus allowing the designer to further manipulate them according to the usual motion planning techniques. In a first phase, a study of the method based on simulations is carried out, considering sets of simulated experimental measures, obtained starting from different laws of motion, and verifying whether the developed genetic algorithm allows for identifying the original law or approximating one. For the computation of the objective function, the Euclidean norm and the Dynamic Time Warping (DTW) algorithm are compared. The performed analysis establishes in which situations each of them is more appropriate. Implementation of the method on experimental data validates its effectiveness.
Keywords: evolutionary algorithms; reverse engineering; cam mechanisms; law of motion; genetic algorithms evolutionary algorithms; reverse engineering; cam mechanisms; law of motion; genetic algorithms
Graphical Abstract

Share and Cite

MDPI and ACS Style

Tiboni, M.; Amici, C.; Bussola, R. Cam Mechanisms Reverse Engineering Based on Evolutionary Algorithms. Electronics 2021, 10, 3073. https://doi.org/10.3390/electronics10243073

AMA Style

Tiboni M, Amici C, Bussola R. Cam Mechanisms Reverse Engineering Based on Evolutionary Algorithms. Electronics. 2021; 10(24):3073. https://doi.org/10.3390/electronics10243073

Chicago/Turabian Style

Tiboni, Monica, Cinzia Amici, and Roberto Bussola. 2021. "Cam Mechanisms Reverse Engineering Based on Evolutionary Algorithms" Electronics 10, no. 24: 3073. https://doi.org/10.3390/electronics10243073

APA Style

Tiboni, M., Amici, C., & Bussola, R. (2021). Cam Mechanisms Reverse Engineering Based on Evolutionary Algorithms. Electronics, 10(24), 3073. https://doi.org/10.3390/electronics10243073

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop