Ultrasonic Welding of Magnesium–Titanium Dissimilar Metals: A Study on Influences of Welding Parameters on Mechanical Property by Experimentation and Artificial Neural Network

Abstract

The advancement in the application of light alloys such as magnesium and titanium is closely related to the state of the art of joining them. As a new type of solid-phase welding, ultrasonic spot welding is an effective way to achieve joints of high strength. In this paper, ultrasonic welding was carried out on magnesium–titanium dissimilar alloys to investigate the influences of welding parameters on joint strength. The analysis of variance was adopted to study the weight of each welding parameter and their interactions. The artificial neural network (ANN) was used to predict joint strength. Results show that in ultrasonic welding of magnesium and titanium alloys, clamping force is the most significant factor, followed by vibration time and vibration amplitude; the interactions between vibration time and vibration amplitude, and between vibration amplitude and clamping force also significantly impact the strength. By using the artificial neural network, test data were trained to obtain a high precision network, which was used to predict the variations of joint strength under different parameters. The analytical model predicts that with the increase in vibration time, the increase in optimal joint strength is limited, but the range of welding parameters to obtain a higher joint strength increases significantly; the minimum joint strength increases as well; and the optimal vibration amplitude expands gradually and reaches the maximum when the vibration time is 1000 ms, then shifts toward the low end gradually.

Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

“…In the research presented in this paper, the weight matrix is updated considering the RMSprop algorithm [46], reported in Equation (22). The learning rate η and the hyperparameter ρ are optimized considering the random search method [44].…”

“…In the literature, machine learning algorithms have been already applied to various manufacturing topics, such as for the prediction of joint strength of ultrasonic welding processes [22], to estimate the tool wear in milling operations [23], to diagnose the dimensional variation of additive manufactured parts [24], to classify the cutting phase of the natural fiber reinforced plastic composites [25] and to predict the tool life in the micro-milling process [26]. More recently, Wang et al [27] developed a deep learning-based algorithm for the recognition of the defects in the strip rolling process, Marques et al [28] investigated the performances of parametric and non-parametric models for the correlation of process and material variables to springback and wall thinning, Palmieri et al [29] defined a metamodel to correlate the process parameters and key-quality indicators for the optimization of the blank-holding forces in the stamping process, and Winiczenko [30] utilized a hybrid response surface methodology combined with a genetic algorithm to simulate and optimize the friction welding parameters in AISI 1020-ASTM A536 joints.…”

A review of quality monitoring for ultrasonic metal welding

“…3 The welding machines that are widely used currently can weld copper, aluminium, magnesium and other metal foils, wires and micro-pieces with good plasticity, 4 yet the range of weldable workpieces by ultrasonic metal welding technology is constantly expanded with the continuous development of high-power ultrasonic welding system. Some scholars have successfully welded together aluminium and magnesium, [5][6][7][8] aluminium and titanium, [9][10][11] aluminium and steel, [12][13][14][15] magnesium and copper, 16 magnesium and titanium 17,18 etc. of thick plate using ultrasonic welding.…”

Impact Energy of Weld Metal in CK45 Carbon Steel

“…Numerous metallic materials can be joined using various welding methods, including friction stir welding (FSW), friction stir spot welding (FSSW), ultrasonic welding (USW), gas tungsten arc welding (GTAW), laser beam welding (LBW), gas metal arc welding (GMAW), and arc stud welding (ASW). All of these methods have different advantages and disadvantages in terms of cost, appropriateness, labor, training, efficiency, time, temperature, and simplicity [2][3][4][5][6][7]. There are five mechanization and automation levels stated in welding: (1) manual (2) semiautomatic (3) mechanized (4) automatic, and (5) robotic.…”