Industry 4.0 driven statistical analysis of investment casting process demonstrates the value of digitalisation
The purpose of this research is to perform statistical data analysis of currently manually collected data in an area of the industrial manufacturing organisation employed in this study that is not digitalised to show the value that can be achieved through digitalisation. The insights gained through analysis of the data can be used to drive decision making in relation to the optimisation of input parameters to minimise the level of defective parts. The parts under investigation in this study were ceramic shells used in the manufacturing process of orthopaedic metal implants. The ceramic shell is a crucial element in the investment casting process because molten metal is poured into the ceramic shell to form the shape of the metal orthopaedic implant. Hence, by minimising the number of defective ceramic shells, there are fewer defective metal implants produced, resulting in cost savings and increased efficiency of the manufacturing process. A number of scientific questions to establish the relationship between the quantity of scrapped products and the level of the silica component in the ceramic slurry were defined and a series of independent t-tests were conducted to address these questions. The results from the t-tests showed the statistically optimal percentage of silica in the binder of the ceramic slurry to minimise the rate of a particular scrap type caused by thin or weak areas of the shell. These results demonstrate the value of analysing digital data relating to the manufacturing process to understand relationships between parameters in the manufacturing process and effectively root-cause scrap outputs. The results from the analysis gave rise to the implementation of a digitalised data collection system that allows continuous monitoring of the components in the ceramic slurry to ensure they are in the optimal specified range. Hence, the quality and yield rate of the orthopaedic implants are maintained at a high level. The digital data collection system also acts as a resource containing historical data for further potential scrap root-cause analysis.
Digitalisation , Industry 4.0 , Manufacturing , Data analytics
Clancy, R., Bruton, K., O'Sullivan, D. and Keogh, D. (2022) 'Industry 4.0 driven statistical analysis of investment casting process demonstrates the value of digitalisation', Procedia Computer Science, 200, pp. 284-297. doi: 10.1016/j.procs.2022.01.227