Modified Multivariate Process Capability Index Using Principal Component Analysis

The existing research of process capability indices of multiple quality characteristics mainly focuses on nonconforming of process output, the concept development of tmivariate process capability indices, quality loss function and various comprehensive evaluation methods. The multivariate complexity increases the computation difficulty of multivariate process capability indices（MPCI）, which makes them hard to be used in practice. In this paper, a new PCA-based MPCI approach is proposed to assess the production capability of the processes that involve multiple product quality characteristics. This approach first transforms the original quality variables into standardized normal variables. MPCI measures are then provided based on the Taam index. Moreover, the statistical properties of these MPCIs, such as confidence intervals and lower confidence bound, are given to let the practitioners understand the capability indices as random variables instead of deterministic variables. A real manufacturing data set and a synthetic data set are used to demonstrate the effectiveness of the proposed method. An implementation procedure is also provided for quality engineers to apply our MPCI approach in their manufacturing processes. The case studies demonstrate the effectiveness and feasibility of this new kind of MPCI, which is easier to be used in production practice. The proposed research provides a novel approach of MPCI calculation.

A multivariate process capability index model system

This paper presents a systematic multivariate process capability index （MPCI） method, which may provide references for assuring and improving process quality levels while achieving an overall evaluation of process quality. The system method includes a spatial MPCI model for multivariate normal distribution data, MPCI model based on factor weight for multivariate no-normal distribution application, and MPCI model based on yield foryield application. At last, examples for calculating MPCI are given, and the experimental results show that this systematic method is effective and practical.

A multivariate process capability index with a spatial coefficient

After analyzing the multivariate Cpm method （Chan et al. 1991）, this paper presents a spatial multivariate process capability index （PCI） method, which can solve a multivariate off-centered case and may provide references for assuring and improving process quality level while achieving an overall evaluation of process quality. Examples for calculating multivariate PCI are given and the experimental results show that the systematic method presented is effective and actual.

Data-driven engineering framework with AI algorithm of Ginkgo Folium tablets manufacturing

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 C_(pk)integrated Bootstrap-t.The C_(pk)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.