Alison J. McMillan

Synthetic experiments, meaning experiments performed using models of reality, are used in a variety of contexts. In recent years, advances in computing capability have led to the development of modelling capability that replicates reality to such an extent that in some circumstances it is difficult to distinguish the model from reality. From a science or engineering perspective, computational analysis models are used to replicate or predict phenomena such as fluid flow or stress in a machine component, but the capability has also crossed over into the arts enabling the computer generated imagery (CGI) used in films, and the augmented reality (AR) of video games. This paper reviews the application of synthetic experiments to replace physical experiments and discusses some of the advantages and pitfalls of such an approach in the context of education. The trend for democratizing analysis is considered, and the dangers of misinterpreting results are discussed. A discussion of the issue of model over–simplification is given, and finally the use of synthetic experiments as part of a systematic scientific investigation is also considered.

High duty engineering component life is usually demonstrated through extensive testing and statistical analysis applied to empirical curve‐fit equations. Because of this, the extent of the testing required is huge and costly: it must consider the load cycle range and test to high numbers of cycles. Additive Manufacturing (AM) for high duty components has brought to the fore the question of the effect of porosity and surface roughness on fatigue life, and how the true life of a critical component can be assessed conservatively. The authors propose the first step toward the development of a fatigue model based on well‐established engineering physics principles, by creating computational specimens with modeled surface roughness and porosity, and subjected to cyclic loading using Finite Element Analysis. They show that the combination of roughness features and sub‐surface pores leads to an equivalent plastic strain distribution pattern that suggests an emergent physical process that has not been reported before, and which indicates that the component strength and life reduction arising from surface roughness can be made significantly worse by the presence of porosity. The development of such phenomenological understanding should lead to improved life prediction techniques, more cost effective test procedures, and the development of better AM methods.

Surface roughness is inherently a product of manufacturing process and is often undesirable. In surface metrology, the term “roughness” is typically applied to the highfrequency and short-wavelength parameters of a finished surface. In practical applications, the surface roughness is directly associated with the quality of the manufacturing. This study proposes the use of ultrasonic surface guided waves to characterize the surface roughness amplitude and frequency during the manufacturing process. Finite element modeling (FEM) of acoustic wave propagation along a rough surface has revealed that there is a cut-off threshold for Rayleigh wave propagation, which is indicative of the surface roughness description. This cut-off occurs for a particular ratio of the spatial surface waviness and the acoustic wavelength, and the detection of the resulting wave attenuation and decay characterizes the surface roughness. If such a measurement system can be used in-line with the production process then it could signal adjustment of the material deposition rate when needed to achieve the required product quality.

Purpose: The purpose of this paper is to explore two identified knowledge gaps: first, the identification and analysis of online searching trends for Financial Technology (FinTech)-related jobs and education information in UK, and second to assess the current strength of the FinTech-related job distribution in terms of job titles and locations in UK, job market in UK and what is required to help it to grow. Design/methodology/approach: Two sets of data were used in this study in order to fill the two identified knowledge gaps. First, six years’ worth of data, for the period from September 2012 to August 2018 was collected from Google Trends. This was in the form of search term keyword text. The hypothesis was designed correspondingly, and the results were reviewed and evaluated using a relevant statistical tool. Second, relevant data were extracted from the “Indeed” website (www.indeed.co.uk) by means of a simple VBA programme written in Excel. In total, the textual data for 500 job advertisements, including the keyword “FinTech”, were downloaded from that website. Findings: The authors found that there was a continuously increasing trend in the use of the keyword “fintech” under the category “Jobs and Education” in online searching from September 2012 to August 2018. The authors demonstrated that this trend was statistically significant. In contrast, the trends for searches using both “finance” and “accounting” were slightly decreased over the same period. Furthermore, the authors identified the geographic distribution of the fintech-related jobs in the UK. In regard to job titles, the authors discovered that “manager” was the most frequently searched term, followed by “developer” and “engineer”. Research limitations/implications: Educators could use this research as a reference in the development of the portfolio of their courses. In addition, the findings from this study could also enable potential participators to reflect on their career development. It is worth noting that the motivations for carrying out an internet search are complex, and each of these needs to be understood. There are many factors that would affect how an information seeker would behave with the obtained information. More work is still needed in order to encourage more people to enter to the FinTech sector. Originality/value: In the planning stage prior to launching a new course educators often need to justify the market need: this analysis could provide a supporting rationale and enable a new course to launch more quickly. Consequently, the pipeline of talent supply to the sector would also be benefitted. The authors believe this is the first time that a study like this had been conducted to explore specifically the availability and opportunities for FinTech education and retraining in UK. The authors anticipate that this study will become the primary reference for researchers, educators and policy makers engaged in future research or practical applications on related topics.

This paper presents a model of surface grinding with superimposed oscillation of the workpiece. The parameters of the model were experimentally derived and the equations of motions of the system were solved using Matlab. The results obtained showed a significant decrease in the amplitude of the relative vibration between the wheel and workpiece when the oscillation was superimposed onto the feed motion of the workpiece. A range of experimental work was undertaken and the results showed that the vibratory process had a superior performance in absolute terms with reference to conventional grinding. Low forces along with longer tool life were recorded with the added vibration. A notion of dynamic relief was introduced to express the efficiency of the vibratory process.