Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: An aluminium alloy 5083 case study

Abstract

A mastering of surface quality issues during machining helps avoiding failure, enhances component integrity and reduces overall costs. Surface roughness significantly affects the quality performance of finished components. A number of parameters, both material and process oriented, influence at a different extend the surface quality of the finished product. Aluminium alloy 5083 component surface quality, achieved in side end milling, constitutes the subject of the present case study. The design of experiment method is employed: that is, 18 carbide two-flute end mill cutters -manufactured by a five-axis grinding machine -have been assigned to mill 18 pockets in finishing conditions -having different combinations of geometry and cutting parameters values, according to the L 18 (2 1 3 3 7 ) standard orthogonal array. Process performance is estimated using the statistical surface texture parameters R a , R y and R z -measured during three different passes on the side surface of the pockets. The results indicate that process parameters -such as the cutting speed, the peripheral second relief angle and the core diameter -mostly influence surface texture. The experimental values are used to train a feed forward back-propagation artificial neural network for the prediction of the yield surface roughness magnitude.

Indirect model for roughness in rough honing processes based on artificial neural networks

“…Artificial neural networks (ANN) have provided satisfactory results in machining processes like turning [2] or milling [3]. Moreover, different authors showed an improvement in results obtained when using artificial neural network models with respect to statistical models [4], [5].…”

Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

“…In recent decades, various milling methods have been investigated and analysed to increase productivity [1,[13][14][15], the subject of the investigation also included the following aspects: high-speed machining with respect to the removed material [2,13,14]; simulation methods aiming to implement different control models and the real behaviour of machines to eliminate machine failure and downtime [16,17]; adjusting tool feeds aiming to optimize the production cycle time; process monitoring to evaluate tool wear for titanium or nickel-based alloys [1,18]. The possibilities of the predictive model are control of the machining process in order to reduce vibrations, increase the stability of the cut and efficiency of the cutting process [19]; and trochoidal milling in connection with finish operations in machining superalloys based on nickel [20][21][22].…”

Hybrid subtractive–additive manufacturing processes for high value-added metal components

“…The goal of this paper is to introduce a process planning approach, which, apart from aiding the automation of operation sequencing in a hybrid manufacturing process, will provide a holistic tool for hybrid manufacturing planning. This tool, driven by the authors experience on additive [18] and subtractive [25,26] process optimization, will fill the gap of the existing process planning platforms, by accounting for part quality KPIs and introducing optimal process windows, upon which the process plan is going to be built.…”