ABSTRACT
Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate Cpk integrated Bootstrap-t. The Cpk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.
ABSTRACT
Objective To analyze the quality control data of linear accelerator detected by Daily QA3 and to evaluate this quality control process using statistical process control.Methods After the calibrations of the accelerator and Daily QA3,Daily QA3 device was used to perform daily quality control by technicians and physicists and 100 groups and 30 groups of daily quality control data were collected.After the accelerator and Daily QA3 were re-calibrated,Daily QA3 device was utilized to perform daily quality control by technicians and 100 groups of the daily quality control data were repeatedly collected.The variations of normalized signal-to-noise ratio of quality control data collected after two calibrations were analyzed.The first 30 groups of daily quality control data measured by technicians and physicists were adopted to calculate the I-MR control chartsand compare the location of CL and the range of UCL and LCL.The process capability indices were calculated for three different quality control processes bytechnicians and physicists,respectively.Results For twice calibrations,normalized signal-to-noise ratio of quality control data significantly changed before 6 weeks,became stable between 6 and 8 weeks,and the changes became smaller after 8 weeks.For dose output measured by physicists,the rang of UCL and LCL was more narrow.In terms of flatness and symmetry,the location of CL was closer to zero.Regarding dose output and flatness,the process capability indices of three different quality control process were all satisfied ≥ 1,whereas unsatisfied for transverse symmetry.Conclusions The first 30-40 data points should be adopted to delineate I-MR control chart of the linear accelerator in daily quality control process.The quality control process should be completed by a fixed and small group of personnel and an optimal tolerance level should be customized.
ABSTRACT
ABSTRACT: The objective of this study was to evaluate the use of statistical quality control tools in the analysis of the uniformity of a microsprinkler irrigation system. For the analysis of irrigation Christiansen uniformity coefficient (CUC) and the distribution uniformity coefficient (DU) were statistically analyzed by means of the Shewhart control charts and process capability index (Cp). For the experiment 25 tests were carried out with a single micro sprinkler and subsequently seven different spacing between micro sprinklers were simulated. Control charts contributed to the diagnosis of the treatments to be under control and with satisfactory uniformity outcomes. Increase in process capability index was directly proportional to the average of CUC and DU.
RESUMO: O estudo objetivou avaliar o uso das ferramentas do controle estatístico de qualidade na análise da uniformidade de um sistema de irrigação por microaspersão. Para a análise da irrigação foram utilizados os coeficientes de uniformidade de Christiansen (CUC) e o coeficiente de uniformidade de distribuição (CUD), e estes foram analisados estatisticamente através dos gráficos de controle de Shewhart e do índice de capacidade do processo (Cp). Para o experimento foram realizados 25 ensaios com um único microaspersor, sendo posteriormente simulados sete diferentes espaçamentos entre microaspersores. Os gráficos de controle contribuíram para o diagnóstico dos tratamentos sob controle e com resultados satisfatórios de uniformidade. O aumento do índice de capacidade do processo mostrou-se diretamente proporcional as médias dos valores de CUC e CUD.