Quality control and improvement for multistage systems: A survey

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems

Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.

Six-Sigma Quality Management of Additive Manufacturing

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

A robust multi response surface approach for optimization of multistage processes

Purpose

In a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but is also dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case.

Design/methodology/approach

In order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem.

Findings

The results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.

Originality/value

To the best of the authors’ knowledge, there are few papers focusing on quality-oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches.

Development of 18 Quality Control Gates for Additive Manufacturing of Error Free Patient-Specific Implants

Unlike subtractive manufacturing technologies, additive manufacturing (AM) can fabricate complex shapes from the macro to the micro scale, thereby allowing the design of patient-specific implants following a biomimetic approach for the reconstruction of complex bone configurations. Nevertheless, factors such as high design variability and changeable customer needs are re-shaping current medical standards and quality control strategies in this sector. Such factors necessitate the urgent formulation of comprehensive AM quality control procedures. To address this need, this study explored and reported on a variety of aspects related to the production and the quality control of additively manufactured patient-specific implants in three different AM companies. The research goal was to develop an integrated quality control procedure based on the synthesis and the adaptation of the best quality control practices with the three examined companies and/or reported in literature. The study resulted in the development of an integrated quality control procedure consisting of 18 distinct gates based on the best identified industry practices and reported literature such as the Food and Drug Administration (FDA) guideline for AM medical devices and American Society for Testing and Materials (ASTM) standards, to name a few. This integrated quality control procedure for patient-specific implants seeks to prepare the AM industry for the inevitable future tightening in related medical regulations. Moreover, this study revealed some critical success factors for companies developing additively manufactured patient-specific implants, including ongoing research and development (R&D) investment, investment in advanced technologies for controlling quality, and fostering a quality improvement organizational culture.

Integrated approach for evaluation of service quality in multistage sequential utilitarian service process

Purpose

In the context of sequential multistage utilitarian service processes, the purpose of this study is to develop and validate propositions to study the impact of service quality (SQ) perceptions developed in intermediate stages, along with the impact of service gestalt characteristics, such as peak and end experiences, on quality perception at each stage and on overall service quality (OSQ) perception. The cascade phenomenon (interdependency between process stages) is considered in the evaluation of OSQ perception of customer, who experiences service through a series of planned, distinct and partitioned sequential stages.

Design/methodology/approach

In this paper, a conceptual framework is used to evolve the propositions. Subsequently, propositions are tested in three different utilitarian service contexts wherein customer survey was conducted for feedback on attributes at each stage, summary perception evaluations of each stage and OSQ evaluation of multistage process. Peak experiences, considered for OSQ evaluation, were defined by a suitable statistical technique. Ordinal logistic regression with nested models is the technique used for analyzing the data.

Findings

This work reveals significant cascade effect of summary evaluation of intermediate stages on the subsequent stage. Peak customer experience (negative or positive) is observed to be marginally significant on intermediate stage and OSQ evaluation. In addition, OSQ is observed to be influenced by summary perception evaluations of intermediate stages, which leads to better model adequacy. Finally, among all the stages, end stage performance is observed to have a significant impact on the overall multistage SQ.

Practical implications

The findings suggest that in view of the cascade effect of intermediate stages, managers need to allocate resources to ensure that all stages are performing at an adequate level instead of only focusing on improving peaks and end effects of customer experiences. The proposed approach is easy to implement and suitable for evaluating SQ and OSQ in varied multistage sequential utilitarian service environment.

Originality/value

An integrated approach for evaluation of SQ in sequential multistage utilitarian service processes is proposed from the perspective of cascade effect of intermediate stages and peak and end effects on OSQ perception.

Uncertainty Evaluation in Multistage Assembly Process Based on Enhanced OOPN

This study investigated the uncertainty of the multistage assembly process from the viewpoint of a stream of defects in the product assembly process. The vulnerable spots were analyzed and the fluctuations were controlled during this process. An uncertainty evaluation model was developed for the assembly process on the basis of an object-oriented Petri net (OOPN) by replacing its transition function with a fitted defect changing function. The definition of entropy in physics was applied to characterize the uncertainty of the model in evaluating the assembly process. The uncertainty was then measured as the entropy of the semi-Markov chain, which could be used to calculate the uncertainty of a specific subset of places, as well as the entire process. The OOPN model could correspond to the Markov process because its reachable token can be directly mapped to the Markov process. Using the steady-state probability combined with the uncertainty evaluation, the vulnerable spots in the assembly process were identified and a scanning test program was proposed to improve the quality of the assembly process. Finally, this work analyzed the assembly process on the basis of the uncertainty of the assembly structure and the variables of the assembly process. Finally, the case of a certain product assembly process was analyzed to test the advantages of this method.

Monitoring the process quality for multistage systems with multiple characteristics

Purpose

The purpose of this paper is to provide a new approach for detecting the small sustained process shifts in multistage systems with correlated multiple quality characteristics.

Design/methodology/approach

The authors propose a new multivariate linear regression model for a multistage manufacturing system with multivariate quality characteristics in which both the auto-correlated process outputs and the correlations occurring between neighboring stages are considered. Then, the multistage multivariate residual control charts are constructed to monitor the overall process quality of multistage systems with multiple quality characteristics. Moreover, an overall run length concept is adopted to evaluate the performances of the authors’ proposed control charts.

Findings

In the numerical example with cascade data, the authors show that the detecting abilities of the proposed multistage residual MEWMA and MCUSUM control charts outperform those of Phase II MEWMA and MCUSUM control charts. It further demonstrates the usefulness of the authors’ proposed control charts in the Phase II monitoring.

Practical implications

The research results of this paper can be applied to any multistage manufacturing or service system with multivariate quality characteristics. This new approach provides quality practitioners a better decision making tool for detecting the small sustained process shifts in multistage systems.

Originality/value

Once the multistage multivariate residual control charts are constructed, one can employ them in monitoring and controlling the process quality of multistage systems with multiple characteristics. This approach can lead to the direction of continuous improvement for any product or service within a company.

An Integrated Reliability Estimation Approach With Stochastic Filtering and Degradation Modeling for Phased-Mission Systems

Reliability estimation is central to enhance safety, availability, and effectiveness of phased-mission systems (PMSs). With the development of information and sensing technologies, condition monitoring (CM) data are now available in many real-world PMSs, and then a more interesting question: how can we dynamically estimate the reliability of PMSs using the in-situ CM data, is of considerable significance to industrial practitioners. In this paper, using the CM data and degradation data of PMS, we present a novel condition-based approach to resolve this question under dynamic operating scenarios. This paper differs from most existing methods which only consider the static scenario without using real-time information, and estimate the reliability only for a population of PMSs but not for an individual PMS in service. To establish a linkage between the historical data and real-time data of the individual PMS, a stochastic filtering model is first utilized to model the phase duration. As such, the updated estimation of the mission time can be obtained by Bayesian law at each phase. To account for the dependency of the degradation progression of PMS on the mission process, the degradation process of PMS is modeled by a Brownian motion with a mission phase-dependent drift coefficient. The corresponding lifetime is derived and the lifetime distribution of PMS can be updated under Bayesian framework once new information is available. Unique to this paper is the union of the CM data and degradation data of PMS to real-time estimate the mission reliability through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution, in which the estimated lifetime considers the dependency of the degradation rate of PMS on mission phase. The effectiveness of the proposed approach is verified by a numerical simulation and a case study.

An empirical study-based state space model for multilayer overlay errors in the step-scan lithography process

In semiconductor manufacturing, the multilayer overlay lithography process is a typical multistage manufacturing process; one of the key factors that restrict the reliability and yield of integrated circuit chips is overlay error between the layers.