1. Introduction
Aluminum-steel bimetallic clads are widely used in engineering applications, such as ship building, chemical industry, commercial and military aircrafts, due to their ability to lower the weight of structural components while improving corrosion resistance [
1]. However, due to the significant variations in physical and mechanical properties, welding of aluminum-steel employing traditional fusion welding techniques is unlikely. However, solid state welding processes, such as friction welding, explosive cladding, and diffusion bonding, provide reliable options to join this combination [
2]. Of the three techniques, explosive cladding is preferred due to its process time less than 50 µs [
3].
The eminence of the explosive clad is dictated by the mechanical properties which are influenced by process parameters, such as loading ratio, standoff distance, preset angle, surface finish, collision velocity, flyer plate velocity, and thickness of flyer plate [
4]. Recently, Kumar et al. explosively cladded aluminum with magnesium at varied loading ratios and reported increase in mechanical strength with the loading ratio [
5]. The variation in microstructure and mechanical strength, subjected to varied standoff distance (1 to 10 mm), in cladding titanium-duplex steels was reported by Chen et al. [
6]. In their attempt to enhance the mechanical strength of the Al-steel clad, Li et al. [
7] machined a dovetail groove on the base plate and reported improved mechanical properties. Tamilchelvan et al. [
8] while cladding titanium-stainless steel plates varied the preset angle between 3 and15° and recommended a maximum of 10°. However, expressing the relationship between process parameters and mechanical strength is intricate as the mechanism of the explosive cladding process is complicated [
9]. In earlier studies, few researchers described the relationship between interface microstructure and mechanical strength of the dissimilar explosive clads [
10,
11].Though the metallurgical approach is effective, the complexity and time-consuming nature motivate researchers to look for a rapid and reliable solution. In recent years, the use of software in predicting the mechanical properties of weld joints has been increasing. ANN and SVM are the two main techniques employed to predict the mechanical properties due to their ability to solve complex nonlinear problems.
While predicting the peak temperature developed during dissimilar grade aluminum friction stir welding, Anandan and Manikandan employed DTR, RFR, LR, PR, and SVR machine learning techniques and concluded that the DTR and RFR models are superior owing to their tree type structure [
12]. Likewise, five machine learning techniques were successfully employed by Mishra and Morisetty to predict the impact of process parameters (tool traverse speed, tool rotational speed, and axial force) on the UTS of the friction stir welded AA6061 alloys [
13]. In this context, Feng et al. proposed a SPDTRS-CS-ANN hybrid algorithm to predict the fatigue life of EH36 grade steel friction stir weld joints with a variation below 10% [
14]. In a similar attempt, Mongan et al. implemented a hybrid GA-ANN model that predicted the lap shear strength of ultrasonically welded Al 5754 joints with a 7.55% deviation from the experimental results [
15]. In a novel attempt, Chen et al. determined the quality of the resistance spot-weld joint via online inspection [
16].
Deep learning has lately evolved into a better and more effective technique that is being adopted by many researchers in the field of materials processing due to its larger capability to handle raw data with enhanced precision, reliability, and concise analysis [
17]. Ma et al. identified the porosities formed during the laser welding of aluminum alloys using CNN [
18]. Wu et al. used a twenty-layer CNN to envisage the weld strength of the ultrasonic welded joints [
19]. To predict the tiny crack patterns in FRP laminates, Ding et al. successfully designed two DNN models based on regression and classification [
20]. Wei et al. attempted to predict the fracture patterns using an integrated neural network and discrete simulation models, and they concluded that this technique had a higher computational efficiency [
21]. In order to identify voids in friction stir welded joints, Rabe et al. used LSTM and BiLSTM approaches and found 93% successful classification [
22]. By using the LSTM-RNN approach, Wu et al. accurately forecasted the mechanical behavior of structural steel at high temperatures [
23]. In the work of Wang et al., one-dimensional CNN outperforms the LSTM and bidirectional LSTM models in detecting faults in glass-polymer-reinforced polymers [
24].
The need for quick and accurate error detection and prediction algorithms is warranted in order to predict the mechanical properties of various explosive clads. In this context, though the deep learning approaches, e.g., recurrent neural networks (RNN), convolutional neural networks (CNN), and deep neural network (DNN), have proven their capabilities, they have not been implemented for the prediction of the mechanical strength of explosive cladding so far. Hence, single, multiple convolutional layer, deep neural network, and recurrent neural network learning models are developed to predict the mechanical properties of Al 6061-SS 304 explosive clad and the deviation with the experimental results is reported.
2. Materials and Methods
In an inclined explosive cladding configuration (
Figure 1a) detailed elsewhere [
25], aluminum 6061 (wt.% Cr-0.23, Si-0.5, Cu-0.28, Fe-0.45, Mg-1.1, Mn-0.15, Zn-0.25, Al-Bal) sheets and stainless steel 304 (Cr-18.9, Ni-8.4, C-0.015, Si-0.48, Cu-0.043, Mn-1.8, Fe-Bal) plates of uniform dimensions (110 mm × 50 mm) were employed as flyer (3 mm thick) and base (8 mm thick) plates, respectively. Prior to cladding, the mating surface of the base plates (SS 304) was machined along the transverse direction to create a dovetail (2 mm wide, 1 mm deep) and V-groove (2 mm wide, 1 mm deep), as illustrated in
Figure 1b. The standoff distance, S, between the flyer and base plates, was varied from 5 mm to 9 mm, and the preset angle, A, between participant alloys, was varied from 0° to 10°. The chemical explosive (density: 1.2 g/cm
3, detonation velocity: 4200 m/s) was packed above the flyer plate and initiated by an electrical detonator, for an explosive loading ratio, R (mass of the explosive/mass of the flyer plate), varying from 0.6 to 1.0. The range of parameters for the experimental conditions attempted (
Table 1) are determined based on trial experiments.
The explosive clad specimens are shown in
Figure 1c, and the characteristic undulating interface microstructures are shown in
Figure 1d. When the preset angle, A is set at 10°, for the loading ratio, R, of 0.6 and a standoff distance, D, of 5 mm the Al 6061-grooveless SS 304 clad exhibits wavy morphology with a streak of molten layer (10 µm thick) at the interface. The formation of molten layer reduces the strength of the clad (
Table 1), consistent with the previous study [
4]. For the similar condition, the Al 6061-‘V’grooved SS 304 interface microstructure (
Figure 1e) shows an undulated continuous bonding at the interface.
Three tensile test specimens were prepared for each condition in the detonation direction (
Figure 1g: ASTM E8-16 sub-size standard) and tested in an automated UNITEK-94100 UTM. In a similar way, three shear test specimens (
Figure 1f; ASTM B 898 standard) were prepared for each condition and tested by applying a compressive force.
The proposed deep learning models have four inputs (R, D, A, and G) and three outputs (TS, Sh.S, and IS), and were trained by the standardized data obtained from the experimental and trial experiments. Data processing, modeling, and validation are the three essential phases of deep learning [
17]. The data acquired from the mechanical tests, described above, are utilized for the first phase i.e., data processing. Post processing, a model is constructed to analyze the data. The selection of algorithms, training, and developing predictions are the phases involved in modeling. Supervised deep learning models, such as CNN, DNN, and RNN, are chosen for modeling, owing to their superiority over competing algorithms. Since the demand is to predict the mechanical strength of the explosive clads, regression algorithms of the above techniques are chosen to build, train and test the proposed models. The prediction performance and accuracy of the developed models are evaluated in the final stage of the deep learning, i.e., validation. The systematic steps in the analysis are schematically illustrated in
Figure 2.
Training and testing sets are performed usingthe original data for deep learning. The training is computed by utilizing 80% of the experimental data (3 specimens for each of the 48 conditions; 48 × 3 = 144 conditions), trial experiments, and previous results. The model is trained, in a python environment, using the training set (800) of data, and then validated using the test set (200), followed by validation with data not utilized for training and testing. During training, the values of the process parameters (R, A, and D) were fed in the existing form while the groove (G) wasmapped into numerical numbers (No-grove: 1, V-groove: 2, Dovetail-groove: 3). The deep learning models attempted are described below.