http://doras.dcu.ie/17608/1/Final_Version%2C_Review_paper.pdf

Optimization of different welding processes using statistical and numerical approaches - A reference guide

http://doras.dcu.ie/16049/1/Effect-Olabi-DCU.pdf

Effect of laser welding parameters on the heat input and weld-bead profile

The main emphasis of this review is on thermal modelling and prediction of laser welding in metals. However as similar techniques are employed to model conventional welding processes such as arc, resistance and friction, as well as related processes such as alloying, cladding and surface hardening, part of this review is given over to the modelling of these processes where appropriate. The time frame of the review is up to the year 2002.

Thermal modelling of laser welding and related processes: a literature review

Laser welding, as a highly efficient processing technology, has been widely applied to manufacturing industry. This paper makes an overview on real time monitoring of laser welding. It begins with a detailed introduction to six typical sensors (photodiode, visual, spectrometer, acoustical sensor, pyrometer, plasma charge sensor) in laser welding detection. Then it makes a review on multi-sensor fusion technology in both laser welding monitoring and adaptive control. Last, subjects for future research concerning welding monitoring and control have been proposed. The paper concludes that the real-time monitoring of laser welding can provide a great amount of valid information about welding status to help effectively identify weld defects and realize adaptive control.

Review of laser welding monitoring

Quality assessment methods and techniques for laser welding have been developed both in- and post-process. This paper summarizes and presents relevant studies being classified according to the technology implemented (vision, camera, acoustic emissions, ultrasonic testing (UT), eddy current technique (ECT)) for the quality inspection. Furthermore, the current review aims to map the existing modeling approaches used to correlating measured weld characteristics and defects with the process parameters. Research gaps and implications of the quality assessment in laser welding are also described, and a future outlook of the research in the particular field is provided.

/pdf/quality-assessment-in-laser-welding-a-critical-review-1gufcvu7vf.pdf

Quality assessment in laser welding: a critical review

The welding process, due to its complexity, has relied on empirical and experimental data to determine its welding conditions. However, trial-and-error methods to determine optimal conditions incur considerable time and cost. In order to overcome these problems, a genetic algorithm and response surface methodology have been suggested for determining optimal welding conditions. First, in a relatively broad region, near-optimal conditions were determined through a genetic algorithm. Then, the optimal conditions for welding were determined over a relatively small region around these near-optimal conditions by using response surface methodology. In order to give different objective function values according to the positive or negative response from the set target value in the optimization problem, a desirability function approach was used. Application of the method proposed in this paper revealed a good result for finding the optimal welding conditions in the gas metal arc (GMA) welding process.

Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology

Laser welding is one of the most precise welding processes in joining sheet metals. In laser welding, performing real time evaluation of the welding quality is very important to enhance the efficiency of the welding process. In this study, the plasma and spatter, which are generated during laser welding, are measured using UV and IR photodiodes. The factors that influence weld quality are classified into five categories; optimal heat input, slightly low heat input, low heat input, partial joining due to gap mismatch, and nozzle deviation. The data number deviated from reference signals and their standard deviations were also considered to evaluate the qualities. A system was also formulated to perform real time evaluations of the weld quality using a fuzzy multi-feature pattern recognition with the measured signals.

Real time estimation of CO2 laser weld quality for automotive industry

Many automotive companies are endeavouring to reduce the weight of the car body in response to various environmental issues. One initiative is the development of TRIP (Transformation Induced Plasticity) steels with a high strength and ductility. Resistance spot welding is a complex process, which requires specific optimal welding conditions based on experimental data. However, the trial-and-error method to determine the optimal conditions requires a large number of experiments, and so response surface methodology has been employed to overcome this problem. The second-order model was used here. This has been used in the resistance spot welding process of the TRIP steel and galvanized TRIP steel with a zinc-coated layer to optimize the welding parameters. The welding current, welding time, and welding force were selected as input variables, and the shear strength and indentation were selected as output variables.

Optimization of welding parameters for resistance spot welding of TRIP steel with response surface methodology

In-process monitoring of welding has become important as the use of laser welding increases. Plasma and spatter are measured and used as signals for estimating the quality of a weld. The measurement system consists of three photodiode sensors (one IR and two UV) to detect the plasma and spatter signals in CO2 laser welding. The estimating algorithm was constructed using fuzzy pattern recognition considering the amplitudes as well as amounts of data beyond the tolerance boundary. Weld qualities were classified as optimal heat input, slightly low heat input, low heat input and misalignment of focus. Also, an algorithm for detecting spatter was created in order to find the partially produced pit. These algorithms were used for quality monitoring in tailored blank welds with a CO2 laser.

https://iopscience.iop.org/article/10.1088/0957-0233/12/8/345/pdf

A fuzzy pattern recognition based system for monitoring laser weld quality

In the present study, depressions of the GMA weld pool due to the impact of droplets are numerically investigated. The numerical simulations is performed on the basis of the Navier-Stokes equation and the volume of fluid (VOF) functions. The kinetic energy of the transferring droplets produces a depression on the weld pool surface. The surface active element affects the depression of the weld pool. In GMAW, the droplet transferred to the surface of the weld pool is one of the factors to produce the finger shape penetration at high current.

Hyunsung Park

Papers

Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology

Real time estimation of CO2 laser weld quality for automotive industry

Optimization of welding parameters for resistance spot welding of TRIP steel with response surface methodology

A fuzzy pattern recognition based system for monitoring laser weld quality

Analysis of Weld Geometry Considering the Transferring Droplets in Gas Metal Arc Welding