Journal Papers

Within the Industry 4.0 landscape, humans collaborate with cyber and physical elements to form human-cyber-physical systems (HCPS). These environments are increasingly complex and challenging workspaces due to increasing levels of automation and data availability. An effective system design requires suitable frameworks that consider human activities and needs whilst supporting overall system efficacy.

Although several reviews of frameworks for technology were identified, none of these focused on the human in the system (moving towards Industry 5). The critical literature review presented provides a summary of HCPS frameworks, maps the considerations for a human in HCPS, and provides insight for future framework and system development. The challenges, recommendations, and areas for further research are discussed.

Abstract

Data-enabled approaches that complement experimental testing offer new capabilities to investigate the interplay between chemical, physical and mechanical attributes of alloys and elucidate their effect on biological behaviours. Reported here, instead of physical causation, statistical correlations were used to study the factors responsible for the adhesion, proliferation and maturation of pre-osteoblasts MC3T3-E1 cultured on Titanium alloys. Eight alloys with varying wt% of Niobium, Zirconium, Tin and Tantalum (Ti— (2–22 wt%)Nb— (5–20 wt%)Zr— (0–18 wt%)Sn— (0–14 wt%)Ta) were designed to achieve exemplars of allotropes (incl., metastable-β, β + α′, α″). Following confirmation of their compositions (ICP, EDX) and their crystal structure (XRD, SEM), their compressive bulk properties were measured and their surface features characterised (XPS, SFE). Because these alloys are intended for the manufacture of implantable orthopaedic devices, the correlation focuses on the effect of surface properties on cellular behaviour. Physico-chemical attributes were paired to biological performance, and these highlight the positive interdependencies between oxide composition and proliferation (esp. Ti⁴⁺), and maturation (esp. Zr⁴⁺). The correlation reveals the negative effect of oxide thickness, esp. TiO_x and TaO_x on osteoblastogenesis. This study also shows that the characterisation of the chemical state and elemental electronic structure of the alloys’ surface is more predictive than physical properties, namely SFE and roughness.

Partially sintered satellite particles in scaffolds produced via Selective Laser Melting (SLM) create discrepancies between the as-designed and the as-manufactured properties (esp. porosity). These discrepancies impede direct comparison of manufactured parts performance to computer simulations. We propose anodic electrolysis using an electrolyte based on non-aqueous ethlylene-glycol TiCl4 (EthaTi) to remove the secondary roughness on titanium SLM-ed porous scaffolds. Post-processed gyroid scaffolds regained 10% porosity with respect to their as-manufactured value (65.20 ± 0.23%), which was close to the as-designed value (75.12%). Compared to other well-established electrolytes, this method is cost-effective, user-friendly and practical, as it requires shorter processing times, is temperature-stable and of gentler chemistry.

Understanding electrical energy consumption of machines and processes is of increasing importance to (i) minimise costs and environmental impact of production activities and (ii) provide an additional information stream to inform condition monitoring systems (i.e. digital twins) about a machine’s status and health. The research outlined in this paper develops a Virtual Machining Energy Toolkit (V_MET) to predict the electrical power consumption of a Computer Numeric Control (CNC) milling machine cutting a particular part program from preparatory codes (i.e. G code). In this way the evaluation of the energy impact of manufacturing part programs prior to implementation and real-time monitoring of the process can become a routine activity at part of a total manufacturing system optimisation. The novelty of this work lies in the inclusion of a virtual CNC process model to determine cutting geometry (i.e. width and depth of cut) to enable the prediction of relatively complex part program geometry.

V_MET consists of three components: (i) the NC interpreter to extract key parameters (e.g. spindle speed, feed rate, tool path) from G-code instructions, (ii) a virtual CNC process model to determine instantaneous cutting geometry (i.e. width and depth of cut) and the material removal from the resulting machining by simulating the motion of the tool path to predict the interaction between the tool tip and workpiece and (iii) an energy model to predict the electrical power consumption for a given set of conditions, developed using regression analysis of data collected under real manufacturing conditions.

Validation of V_MET has been conducted by physical machining of different product features to evaluate the validity over a range of different cutting parameters, NC operations (i.e. linear, clockwise interpolations) and repasses over previously cut regions. Overall good accuracy has been observed for the predicted energy requirements as a function of the cutting regimes, with 4.3% error in total energy and Mean Average Percentage Error (MAPE) of 5.6% when compared with measurements taken during physical cutting trials.

Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm” (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.

Published in the Journal of the Mechanical Behaviour of Biomedical Materials

Journal of the Mechanical Behavior of Biomedical Materials | ScienceDirect.com by Elsevier

Celebrating Excellence in Advanced Engineering Materials: Women in Engineering Materials: Advanced Engineering Materials (wiley.com)

Triply periodic minimal surface and trabecular-like structures are common approaches in tissue engineering. There are few comparative studies assessing the impact of topology on biological and mechanical performance independent of porosity and surface area. Herein, these two features are controlled, despite design-to-manufacture disparities intrinsic to selective laser melting. Smoothed trabecular scaffolds, with more accessible throats lined with microporosity, enhance osteoblastogenesis.

Published in the Journal of Computational Design and Engineering

This work presents a novel bottom-up methodology to generate designs that can be tightly integrated with the additive manufacturing environment and that can respond flexibly to changes in that environment….The method is bio-inspired, based on strategies observed in natural systems, particularly in biological growth and development. The design geometry is grown in a computer-aided design-based, bio-inspired generative design system called ‘Biohaviour’.