Titanium is a versatile biocompatible metal that is desirable in additively manufactured medical implant devices. However, additively manufactured parts have particular microstructures, porosity, residual stress, and surface conditions which can have a strong impact on fatigue performance. Implants have an added complexity from the saline operating environment and the associated impact on the safe design life. Equally, direct energy deposition induces a complex thermal history which, if not carefully controlled, can significantly alter the mechanical/material properties of the component. This study investigates the decrease in fatigue life, in an in vitro body fluid simulation using Ringer's solution, observed in Ti‐6Al‐4V specimens extracted from coupons manufactured by directed energy deposition. An interrupted deposition strategy was employed to control build regularity, which appeared to influence certain mechanical properties, including corrosion fatigue life. An ≈50% decrease in fatigue life was observed in Ringer's solution at 6 Hz loading frequency, clearly important in designing implants.

Steam turbines used in the power generation industry are subject to fatigue during normal operation which includes transient events such as start-ups and steady state operation. Surface treatment methods, such as shot peening (SP) and roller burnishing, to induce surface compressive residual stresses in critical areas and improve fatigue life are commonly used, but the depth of the induced residual stresses is limited by the process. Laser shock peening (LSP) is a more recent development that has been applied in the aerospace industry on titanium blades, but is not yet commonly used in the power generation industry. The current research is focused on optimizing LSP parameters for the application of the process on 12Cr steels used for turbine blades. Evaluation of the induced residual stress was done with both conventional laboratory Xray diffraction (XRD) and synchrotron X-ray diffraction (SXRD) techniques.

This paper presents a neuro-fuzzy control scheme for the nonlinear process of complex curvature Friction Stir Welding with multi-input-multi-output parameters. The proposed scheme consists of integrated sensor monitoring, fuzzy logic controller with basic membership functions, a trained back-propagation ***feed-forward neural network used to generate on-line fuzzy rules, and an input/output scale tuning system used to improve output response to process changes. To validate the feasibility of the neuro-fuzzy controller, simulations have been conducted with the objective to maintain tool/workpiece contact during complex curvature FSW. The simulation results show that the control variables were well maintained within limited ranges from reference values.

The article provides insight to the complex relationships that exist between the selected weld process parameters and the associated effects on weld responses by application of statistical analysis relating to response predictive models for Friction Hydro-Pillar Processing (FHPP). The literature review focused at obtaining the current assumptions made with regards to the relationship between FHPP and conventional Friction Welding (FW) variations. Experimental welds were produced at varying selected process parameters; motor speed, axial force, consumed length and forging time. These parameters were compared to five weld response variables through a 27-run full factorial Design of Experiment and multiple regressions. The analysis focussed on quantifying the main effects of process parameters on energy input, temperature profile, friction time, torque and consumed rate. Comparison with experimental results served to validate the effect of dominant process parameters. The process and statistical analysis are explained in detail to assist further understanding of the applied methodology and the effect of the various process parameters on weld responses presented. Results indicate that the mathematical equation based models predict the responses adequately within the limits of welding parameters used and that no single parameter solely control the weld responses during FHPP. This study provides a clearer understanding of FHPP showing that generalised conclusions, with regards to the influences of process parameters on weld responses during conventional FW, cannot be made as the effects of these inputs differ depending on the combination of levels included in a parameter set.