Aerospace Engineering: Research @ Brunel

Research published by Brunel staff

Below are recent articles (co-)authored by Brunel academic staff. Please click the title of the article to access the full-text.

• Multiple Regression Analysis and Non-Dominated Sorting Genetic Algorithm II Optimization of Machining Carbon-Fiber-Reinforced Polyethylene Terephthalate Glycol Parts Fabricated via Additive Manufacturing Under Dry and Lubricated Conditions
  Tzotzis, A. et al
  Lubricants, Vol 13, No 2, Art No. 63 (Feb 2025)
  The present research deals with the processing of the additively manufactured Carbon-Fiber-Reinforced Polymer (CFRP) under dry and lubricated cutting conditions, focusing on the generated surface roughness. The cutting speed, feed, and depth of cut were selected as the continuous variables. A comparison between the generated surface roughness of the dry and the lubricated cuts revealed that the presence of coolant contributed towards reducing surface roughness by more than 20% in most cases. Next, a regression analysis was performed with the obtained measurements, yielding a robust prediction model, with the determination coefficient R2 being equal to 94.65%. It was determined that feed and the corresponding interactions contributed more than 45% to the model's R2, followed by the depth of cut and the machining condition. In addition, the cutting speed was the variable with the least effect on the response. The Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) was employed to identify the front of optimal solutions that consider both minimizing surface roughness and maximizing Material Removal Rate (MRR). Finally, a set of extra experiments proved the validity of the model by exhibiting relative error values, between the measured and predicted roughness, below 10%.

• Bayesian monitoring of machining processes using non-intrusive sensing and on-machine comparator measurement
  Papananias, M.
  International Journal of Advanced Manufacturing Technology, [early access], (Feb 2025)
  Machining processes are largely reliant on manual intervention and non-value-added processes, such as post-process inspection, to achieve end-product conformance. However, the ever-increasing demand for high manufacturing productivity combined with low costs and high product quality requires online monitoring systems to provide real-time insights into the cutting process and minimize the volume of non-value-added processes. Most of the published work on machining process monitoring focuses on intrusive measurement equipment, such as dynamometers, to predict the dimensional quality of machined parts, preventing industrial exploitation due to practical limitations. The main focus of this work is to address this issue by developing a new product health monitoring method for machining processes using non-intrusive and low-cost instrumentation and data acquisition (DAQ) hardware. The sensing setup in this work includes an acoustic emission (AE) sensor and two accelerometers in the work holding. The proposed monitoring system is applied to milling experiments using Gaussian process regression (GPR) for probabilistic nonlinear in-process product condition monitoring. Validation results show the effectiveness of the GPR model to provide accurate probabilistic predictions of product health metric deviations with reasonable uncertainty estimates considering the large variability of the data. In addition, a Bayesian inference methodology is derived to dynamically incorporate subsequent information from on-machine probing (OMP) with a comparator method, improving the accuracy and robustness of the proposed solution. Specifically, it is demonstrated that a precision-weighted combination of prior information from the posterior predictive distribution for a future observation and new metrological information from on-machine comparator measurement (OMCM) can clearly improve posterior inferences about the end product condition.

• Bubble nucleation site density, generation frequency and departure diameter in flow boiling of HFE-7100
  Al-Zaidi, A. H. et al
  International Journal of Heat and Mass Transfer, Vol 242, Art No. 126830 (Jun 2025)
  Bubble nucleation and dynamics can play a significant role in the nucleate boiling mechanism during flow boiling. Understanding the behaviour of nucleating bubbles at different operating conditions can help identify the control parameters that should be included in proposed heat transfer models and correlations. This paper presents an experimental work on measurements of active nucleation site density, bubble generation frequency and departure diameter during flow boiling of refrigerant HFE-7100 in a microgap heat exchanger. The microgap heat exchanger had a heated flat surface of 20 mm width, 25 mm length and an adiabatic transparent cover located 1 mm above the heated surface. This allowed direct flow visualisation using a high-speed, high-resolution camera of a relatively large observation area. The effect of heat flux, mass flux and system pressure on the active nucleation site density and bubble dynamics (frequency and departure diameter) was examined. All experiments were carried out at inlet sub-cooling of 5 K, inlet pressure of 1 and 2 bar, mass flux of 100-200 kg/m2 s and wall heat flux up to 84 kW/m2. The experimental results were then compared with existing models and correlations predicting nucleation site density, bubble generation frequency and departure diameter with limited success. The dominant parameters were also identified, and new correlations were proposed based on the experimental results. The results of the current work can help develop accurate prediction heat transfer models and encourage and enable researchers working in numerical modelling to consider nucleation from multiple sites, rather than simulating one single nucleation site.