Archives > Volume 11 | Number 2 | June 2016 > pp 93–104
Advances in Production Engineering & Management
Volume 11 | Number 2 | June 2016 | pp 93–104
Surface roughness assessing based on digital image features
Simunovic, G.; Svalina, I.; Simunovic, K.; Saric, T.; Havrlisan, S.; Vukelic, D.
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A B S T R A C T
The paper gives an account of the machined surface roughness investigation based on the features of a digital image taken subsequent to the technological operation of milling of aluminium alloy Al6060. The data used for investigation were obtained by mixed-level factorial design with two replicates. Input variables (factors) are represented by the face milling basic machining parameters: spindle speed (at five levels: 2000; 3500; 5000; 6500; 8000 rev/min, respectively), feed per tooth (at six levels: 0.025; 0.1; 0.175; 0.25; 0.325; 0.4 mm/tooth, respectively) and depth of cut (at two levels: 1; 2 mm, respectively). Output variable or response is the most frequently used surface roughness parameter – arithmetic average of the roughness profile, Ra. Digital image of the machined surface is provided for every test sample. Based on experimental design and obtained results of roughness measuring, a base has been created of input data (features) extracted from digital images of the samples' machined surfaces. This base was later used for generating the fuzzy inference system for prediction of the surface roughness using the adaptive neuro-fuzzy inference system (ANFIS). Assessing error, i.e. comparison of the assessed value Ra provided by the system with real Ra values, is expressed with the normalized root mean square error (NRMSE) and it is 0.0698 (6.98 %).
A R T I C L E I N F O
Keywords • Surface roughness, Face milling, Digital image, Adaptive neuro-fuzzy inference system
Corresponding author • Simunovic, G.
Article history • Received 7 March 2016, Revised 15 May 2016, Accepted 16 May 2016
Published on-line • 31 May 2016
E X P O R T C I T A T I O N
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