Surface Finish Analysis in Single Point Incremental Sheet Forming of Rib-Stiffened 2024-T3 and 7075-T6 Alclad Aluminium Alloy Panels

Abstract

The article presents the results of the analysis of the interactions between the single point incremental forming (SPIF) process parameters and the main roughness parameters of stiffened ribs fabricated in Alclad aluminium alloy panels. EN AW-7075-T6 and EN AW-2024-T3 Alclad aluminium alloy sheets were used as the research material. Panels with longitudinal ribs were produced with different values of incremental vertical step size and tool rotational speed. Alclad is formed of high-purity aluminium surface layers metallurgically bonded to aluminium alloy core material. The quality of the surface roughness and unbroken Alclad are key problems in SPIF of Alclad sheets destined for aerospace applications. The interactions between the SPIF process parameters and the main roughness parameters of the stiffened ribs were determined. The influence of forming parameters on average roughness Sa and the 10-point peak–valley surface roughness Sz was determined using artificial neural networks. The greater the value of the incremental vertical step size, the more prominent the ridges found in the inner surface of stiffened ribs, especially in the case of both Alclad aluminium alloy sheets. The predictive models of ANNs for the Sa and the Sz were characterised by performance measures with R2 values lying between 0.657 and 0.979. A different character of change in surface roughness was found for sheets covered with and not covered with a soft layer of technically pure aluminium. In the case of Alclad sheets, increasing the value of the incremental vertical step size increases the value of the surface roughness parameters Sa and Sz. In the case of the sheets not covered by Alclad, reduction of the tool rotational speed increases the Sz parameter and decreases the Sa parameter. An obvious increase in the Sz parameter was observed with an increase in the incremental vertical step size.

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force

“…The tool is specifically mounted at an angle of 30° degrees, so that the mechanical momentum sensor STJ100 ( 7) is connected to controller BGI (8) (Mark-10 Corp., Copiague, NY, USA) and could measure both-pressing and friction momentum. The sensor controller BGI is connected to an oscilloscope PicoScope 3424 (9) (Pico Technology Ltd., St. Neots, UK, GB), which transmits the results obtained from the mechanical moment sensor on the computer (10). The parameters of the tool forming path trajectory are given in Table 2.…”

“…One of the indicators of the quality of the formed product is the surface quality [ 10 , 11 , 12 , 13 , 14 , 15 ]. The authors in [ 10 ] present the results of an analysis of the interaction between the SPIF process parameters and the stiffened ribs made of Alclad aluminum alloy plates.…”

“…One of the indicators of the quality of the formed product is the surface quality [ 10 , 11 , 12 , 13 , 14 , 15 ]. The authors in [ 10 ] present the results of an analysis of the interaction between the SPIF process parameters and the stiffened ribs made of Alclad aluminum alloy plates. The correlation between the operating variables, the maximum forming angle, and the surface roughness is determined by ensemble learning using a gradient boosting regression tree and presented in [ 11 ].…”

Recent Developments and Future Challenges in Incremental Sheet Forming of Aluminium and Aluminium Alloy Sheets

“…In the last decade, an increase has been observed in works related to the incremental forming of the following lightweight alloy sheets: titanium and titanium alloys [30,31], aluminium alloys [32,33] and magnesium and its alloys [34]. These works are mainly related to the use of these alloys in the aviation [32] and space [35] industries, where, increasing, the share of lightweight metals in the entire structure is part of the efforts to reduce carbon dioxide emissions to the atmosphere. A brief overview of the stateof-the-art methods of incremental sheet forming for lightweight materials was provided by Trzepieci ński et al [36].…”

Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets

“…Moreover, machine learning techniques with controlled manufacturing are used to develop various effective predictive models [ 25 , 26 , 27 , 28 , 29 ]. Trzepieciński et al [ 30 ] presented an analysis of the interaction between SPIF process parameters and the main roughness parameters. They found that predictive models of ANNs for Ra and Rz were characterised by performance measures of R 2 values between 0.657 and 0.979.…”