This study investigates the predictive modelling of weld bead geometry in wire arc additive manufacturing (WAAM) through advanced machine learning methods. While WAAM is valued for its ability to produce large, complex metal parts with high deposition rates, precise control of the weld bead remains a critical challenge due to its influence on mechanical properties and dimensional accuracy. To address this problem, this study utilized machine learning approaches—Ridge regression, Lasso regression and Bayesian ridge regression, Random Forest and XGBoost—to predict the key weld bead characteristics, namely height, width and cross-sectional area. A Design of experiments (DOE) was used to systematically vary the welding current and travelling speed, with 3D weld bead geometries captured by laser scanning. Robust data pre-processing, including outlier detection and feature engineering, improved modelling accuracy. Among the models tested, XGBoost provided the highest prediction accuracy, emphasizing its potential for real-time control of WAAM processes. Overall, this study presents a comprehensive framework for predictive modelling and provides valuable insights for process optimization and the further development of intelligent manufacturing systems.

Dielectric elastomer actuators also known as DEAs are widely used as soft actuators. In order to control such actuators its mechanical properties are needed. Since viscoelastic materials are mainly used as a base for DEAs, an appropriate mathematical model is needed to get appropriate control parameters. Viscoelastic elastomers have very complex mechanical characteristics. They exhibit a combination of behaviors of solid material and liquid. Usually, solid materials exhibit elasticity up to a certain point. Liquids exhibits viscosity. They change their shape regardless on the stress applied on it and do not regain their original shape. Viscoelastic materials exhibit both behaviors at once. However, the ratio between elasticity and viscosity depends on the material being used. The use of fractional Kelvin–Voigt model can be of great help in determining the elastic and/or viscous material properties. Even more, a comparison between single and double fractional Kelvin–Voigt models will be given for low frequency strain ranges. It will be shown that viscoelastic mechanical properties are frequency dependent. At the end a least-square method is introduced for determination of optimal parameters for both models. This study can be used as a guidance for viscoelastic material parameter identification either for single or double fractional Kelvin–Voigt model. Both models can be further used in control theory with fractional derivatives to obtain proper control parameters since they are easily transferable to Laplace domain.

This study applied a holistic approach to the problem of controlling the temperature of critical areas of tools using conformal cooling. The entire injection molding process is evaluated at the tool design stage using four criteria, one from each stage of the process cycle, to produce a tool with effective cooling that enables short cycle times and ensures good product quality. Tool manufacturing time and cost, as well as tool life, are considered in the optimization by introducing a novel tool-efficiency index. The multi-objective optimization is based on numerical simulations. The simulation results show that conformal cooling effectively cools the critical area of the tool and provides the shortest cycle times and the lowest warpage, but this comes with a trade-off in the tool-efficiency index. By using the tool-efficiency index with non-dominated sorting, the number of relevant simulation cases could be reduced to six, which greatly simplifies the decision regarding the choice of cooling system and process parameters. Based on the study, a tool with conformal cooling channels was made, and a coolant inlet temperature of 20 °C and a flow rate of 5 L/min for conformal and 7.5–9.5 L/min for conventional cooling channels were selected for production. The simulation results were validated by experimental measurements.

Successful control of a dielectric elastomer actuator (DEA) can be a challenging task, especially if no overshoot is desired. The work presents the first use of the $P{I}^{\lambda }{D}^{\mu }$ control for a dielectric elastomer actuator to eliminate the overshoot. The mathematical model of the dielectric elastomer was established using the fractional Kelvin-Voigt model. Step responses are first tested in the Laplace domain, which gave the most satisfactory results. However, they did not represent the real model. It cannot have negative force acting on the dielectric elastomer actuator. Simulations in Matlab/Simulink were performed to obtain more realistic responses, where output of the $P{I}^{\lambda }{D}^{\mu }$ controller was limited. Initial parameters for a PID control were obtained by the Wang–Juang–Chan algorithm for the first order plus death time function approximation to the step response of the model, and reused as the basis for the $P{I}^{\lambda }{D}^{\mu }$ actuator control. A quasi-anti-windup method was introduced to the final control algorithm. Step responses of the PID and the $P{I}^{\lambda }{D}^{\mu }$ in different domains were verified by simulation and validated by experiments. Experiments proved that the fractional calculus $P{I}^{\lambda }{D}^{\mu }$ step responses exceeded performance of the basic PID controller for DEA in terms of response time, settling time, and overshoot elimination.