Anastasia N. Vasileiou

Purpose: The present study is aimed at developing a computationally efficient residual stress modelling method that is suitable for arc directed energy deposition (Arc-DED) additive manufacturing applications using macroscopic continuum finite element techniques adapted from weld modelling. Design/methodology/approach: The approaches were undertaken on the NeT-TG9 Arc-DED benchmark, which comprises five depositions on an SS316 L substrate. A thermo-mechanical modelling workflow was developed using guidelines from the R6 structural integrity assessment procedure and realised with FEAT-WMT and Abaqus solvers. Bead lumping (BL) and moving torch (MT) simulations were compared for computational time and accuracy. This was followed by pass lumping (PL), which simplified the BL method further with reductions in computational time. PL was implemented by adjusting the heat input to maintain the mid-length fusion zones. The thermo-mechanical simulation results were validated against experimental measurements. Findings: Thermal analyses showed similar temperature distributions between the MT and BL simulations, and the experimental data. The two approaches were also comparable in their residual stress predictions, closely matching experimental measurements. BL was the more computationally efficient technique. PL also predicted the longitudinal residual stresses within the acceptable threshold for accuracy and reduced the computational time by 92% from the MT simulation. Originality/value: While the methods discussed have been implemented in weld modelling, their use in Arc-DED additive manufacturing is still limited. The PL method can be applied to the TG9 benchmark and similar problems and produce accurate results. Optimisation of the technique could speed-up Arc-DED modelling to more industrially practical timeframes.

The manufacturing industry heavily relies on welding processes to join materials, forming integral components across various sectors. Many aspects will influence the quality of the weld and finally affect the structure integrity of the weldment. Welding-induced residual stress, a consequence of the thermal cycling inherent in these processes, significantly impacts the structural integrity and performance of fabricated components. Understanding and predicting this residual stress is crucial for enhancing the reliability and durability of welded structures. However, quickly assessing a welding setup in digital experiments presents significant challenges, as a traditional simulation may be time-consuming. This research outlines the application of a workflow to construct an artificial neural network-based surrogate model to predict welding-induced residual stress. The model is built using data automatically generated from finite element simulations through Python scripts based on macro functions. Unlike traditional methods that rely on manual preprocessing and finite element simulations, this approach significantly reduces the time and effort required for simulation setup and data extraction, enhancing overall efficiency. By ensuring that all simulation steps are performed consistently through macro functions, the method eliminates human-induced variability, leading to improved reproducibility. Furthermore, the automation of data generation makes it possible to create extensive datasets necessary for training machine learning models, overcoming the limitations of labor-intensive traditional techniques. The workflow contains four main steps: build a standard weld finite element simulation and validate the finite element simulation results against experimental data; develop scripts for large dataset generation using a macro function that records the preprocessing and postprocessing steps of the finite element simulation; use the scripts to generate the required data; develop surrogate models and test their performance. The artificial neural network demonstrated high accuracy in predicting stress levels, closely aligning with simulation results on the test dataset and a relative root mean square error of 0.0024.

Investigating the suitability of using a single heat transfer coefficient in metal casting simulation: An inverse approach VASILEIOU Anastasia, VOSNIAKOS Ge

The activities within a European network to develop accurate experimental and numerical methods to assess residual stresses in structural weldments are reported. The NeT Task Group 6 or NeT-TG6 project examined an Alloy 600 plate containing a three-pass slot weld made with Alloy 82 consumables. A number of identical specimens were fabricated and detailed records of the manufacturing history were kept. Parallel measurement and simulation round robins were performed. Residual stresses were measured using neutron diffraction via five different instruments. The acquired database is large enough to generate reliable mean profiles, to identify clear outliers, and to establish the systematic uncertainty associated with this non-destructive technique. NeT-TG6 gives a valuable insight into the real-world variability of diffraction-based residual stress measurements, and forms a reliable foundation against which to benchmark other measurement methods. The mean measured profiles were used to validate the accuracy achieved by the network in the prediction of residual stresses.