A Sequential Inspection Procedure for Fault Detection in Multistage Manufacturing Processes

Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable. In this paper, a sequential inspection procedure to detect the process fault based on a sequential testing algorithm and a minimum monitoring system is proposed. After the monitoring system detects that the process is out of statistical control, the features to be inspected (end of line or in process measurements) are defined sequentially according to the expected information gain of each potential inspection measurement. A case study is analyzed to prove the benefits of this approach with respect to a predefined inspection scheme and a randomized sequential inspection considering both the use and non-use of fault probabilities from historical maintenance data.

A QMM-MOP methodology for the maintenance scheduling of multistage manufacturing systems with a stream of deterioration

In multistage manufacturing systems, a stream-of-deterioration (SOD) phenomenon poses two challenges for effective preventive-maintenance (PM) scheduling. First, the deterioration of each machine contributes to the deterioration of the final-product quality, and thus timely PM should be conducted to prevent excessive quality deterioration. Second, the deterioration of different machines leads to different degrees of deterioration in the final-product quality; thus, the PM of different machines will result in different degrees of improvement in the final-product quality. To address both challenges, a QMM-MOP methodology that adopts an interactive bi-level scheduling framework is proposed. In machine-level scheduling, a quality-integrated maintenance model (QMM) is developed by incorporating intermediate-product quality deterioration into the total cost to schedule timely PM for each individual machine. In system-level scheduling, maintenance-operation prioritization (MOP), based on a SOD-enabled quality-improvement factor, is proposed to select machines for PM. The case study shows that the proposed methodology can ensure a higher final-product quality with a lower total cost. The contribution of this paper is to develop a QMM-MOP methodology that integrates product-quality improvement into an interactive bi-level PM scheduling framework and enables MOP based on the-quality improvement factor to best improve the final-product quality.

Industrial Quality Prediction System through Data Mining Algorithm

Based on an assessment of production capabilities, manufacturing sectors' core competency is increased. The importance of product quality in this aspect cannot be overstated. Several academics have introduced Deming's 14 principles, Shewhart cycle, total quality management, and other approaches to decrease the external failure costs and enhance product yield rates. Analysis of industrial data and process monitoring is becoming increasingly important as a part of the Industry 4.0 paradigm. In order to reduce the internal failure cost and inspection overhead, quality control (QC) schemes are utilized by industries. The final product quality has an interactive and cumulative effect of various parameters like operators and equipment in multistage manufacturing processes (MMP). In other cases, the final product is inspected in a single workstation with QC. It's challenging to do a cause analysis in MMP whenever a failure occurs. Several industries are looking for the optimal quality prediction model in order to achieve flawless production. The majority of current approaches solely handles single-stage manufacturing and is inadequate in dealing with MMP quality concerns. To overcome this issue, this paper proposes an industrial quality prediction system with a combination of multiple Program Component Analysis (PCA) and Decision Stump (DS) algorithm for MMP quality prediction. A SECOM (SEmiCOnductor Manufacturing) dataset is used for verification and validation of the proposed model. Based on the findings, it is clear that this model is capable of performing accurate classification and prediction in the field of industrial quality.

Variation propagation modelling in multistage machining processes using dual quaternions

Variation propagation models play an important role in part quality prediction, variation source identification, and variation compensation in multistage manufacturing processes. These models often use homogenous transformation matrix, differential motion vector, and/or Jacobian matrix to represent and transform the part, tool and fixture coordinate systems and associated variations. However, the models end up with large matrices as the number features and functional element pairs increase. This work proposes a novel strategy for modelling of variation propagation in multistage machining processes using dual quaternions. The strategy includes representation of the fixture, part, and toolpath by dual quaternions, followed by projection locator points onto the features, which leads to a simplified model of a part-fixture assembly and machining. The proposed approach was validated against stream of variation models and experimental results reported in the literature. This paper aims to provide a new direction of research on variation propagation modelling of multistage manufacturing processes.