Multistage Manufacturing Research Articles

Optimizing Mean and Variance of Multiresponse in a Multistage Manufacturing Process Using a Patient Rule Induction Method

Most manufacturing industries produce products through a series of sequential stages, known as a multistage process. In a multistage process, each stage is affected by its preceding stage, at the same time, it affects its following stage. Also, each stage often includes several response variables to be optimized. In this paper, we attempt to optimize the several response variables of the multistage process simultaneously considering the relationships among the stages. For this purpose, we use a particular data mining method, called a patient rule induction method. Because the relationships among the stages are often complicated, using a data mining method is a good approach for analyzing the relationships. According to the procedure of the patient rule induction method, the proposed method searches for an optimal setting of input variables directly from operational data at which mean and variance of the several response variables of the multistage process are optimized. The proposed method is explained by a step-by-step procedure using a steel manufacturing process example.

A control model for downstream compensation strategy in multi-stage manufacturing systems of complex parts

The capability of delivering high-quality products with the required service level is a key factor for competitiveness in manufacturing companies. Zero-Defect Manufacturing control strategies aim at ensuring these targets grounding on the combination of knowledge extraction from the process and advanced statistical tools. This work presents the approach proposed within the EU-funded project ForZDM for selected use-cases in different industrial sectors characterized by multi-stage manufacturing systems and high-value complex parts. The control model is proposed and the overall control strategy is presented with respect to the state-of-the-art solutions, in order to show a comprehensive methodology integrating the joint product-system quality-oriented approach.

An Intelligent Metrology Informatics System based on Neural Networks for Multistage Manufacturing Processes

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP.

Modelling HSLA steel product quality under multi-stage manufacturing set up using multi-block partial least square regression

In order to have an understanding about the quality of steel product, involving multiple stages of manufacturing process, adequate assessment about the input-output relationship is necessary to ensure high-quality product. In this study, a high-strength low-alloy (HSLA) steel product quality, comprising of two stages of manufacturing, is modelled using partial least square regression (PLSR) and multi-block PLSR (MBPLSR) approaches. The alloy chemistry and rolling parameters are considered here as input variables along with strength and ductility of the finished steel as responses. Hotelling's T2 statistic is used for diagnosis of faults in batches of heat along with developing fault detection system through significant input variables. Both the modelling approaches are found to be useful for this purpose. However, the MBPLSR-based modelling approach is preferable since it helps to locate the source of the problem quicker at appropriate stages of operation with the help of input variables.

Using Business Analytics for Decision Support in Zero Defect Manufacturing of Composite Parts in the Aerospace Industry

In multi-stage manufacturing environments, reworking at an early stage can prevent costly defects at later stages. One goal of the EU research project ZAero (Zero-defect manufacturing of composite parts in the aerospace industry) is to generate feature data by implementing an inline quality control for the manufacturing process of carbon fibre components of aircraft. However, for each feature detected by ZAero’s inline quality control, operators must decide whether or not to rework that feature. Additional rework of non defects can reduce expensive defects in later stages, but the increased effort should not have a significant impact on production. To help operators make the right decisions, an extensible hybrid decision support system (DSS) is proposed, which combines a software application that visualizes 3D-based process-specific feature data and supports the execution of rework decisions with web-based business analytics dashboards. The dashboards visualize data generated by part flow simulation experiments for various rework strategies, as well as data from a manufacturing execution system (MES). The proposed DSS can be easily customized to integrate additional data treasures from the ever-increasing amount of data in the industrial sector.

Multistage Quality Control Using Machine Learning in the Automotive Industry

Product dimensional variability is a crucial factor in the quality control of complex multistage manufacturing processes, where undetected defects can easily be propagated downstream. The recent advances in information technologies and consequently the increased volume of data that has become readily available provide an excellent opportunity for the development of automated defect detection approaches that are capable of extracting the implicit complex relationships in these multivariate data-rich environments. In this paper, several machine learning classifiers were trained and evaluated on varied metrics to predict dimensional defects in a real automotive multistage assembly line. The line encompasses two automated inspection stages with several human-operated assembly and pre-alignment stages in between. The results show that non-linear models like XGBoost and Random Forests are capable of modelling the complexity of such an environment, achieving a high true positive rate and showing promise for the improvement of existing quality control approaches, enabling defects and deviations to be addressed earlier and thus assist in reducing scrap and repair costs.

Robust model-based control of multistage manufacturing processes

This paper presents a novel method that utilizes in-process measurements of product quality and models that relate those measurements with the underlying manufacturing process parameters to drive down the product quality errors via strategic adjustments of the controllable process parameters. Uniqueness of the new method is its robustness to inevitable inaccuracies in the underlying models, as well as the absence of traditional, but restrictive assumptions of Gaussianity and independence of measurement and process noise terms. The new approach was demonstrated using models and data from an automotive cylinder head machining process and an industrial-scale semiconductor lithography overlay process.

Sustainable Lot Size in a Multistage Lean-Green Manufacturing Process under Uncertainty

Optimal lot sizing is the primary tool applied by lean practitioners to reduce inconsistency in the manufacturing system to cut down inventories, which are often considered as a type of waste in the lean culture. Managers attempt to consider environmental impacts of the manufacturing system and find ways to reduce these effects while making efforts to achieve environmental protection. From a sustainability standpoint, carbon emissions are the major source of environmental contamination and degradation. In this context, this research provides an economic production quantity model with uncertain demand and process information in a multistage manufacturing process. This imperfect manufacturing process produces defective products at an uncertain rate, and is reworked to convert them into perfect quality products and reduce wastages. To control this uncertainty in the manufacturing process, the decomposition principle and the signed distance method of fuzzy theory are applied. The manufacturing process is analyzed with regard to environmental concerns, and a sustainable lot size is obtained through an interactive Weighted Fuzzy Goal Programming (WFGP) approach for the simultaneous achievement of economic and environmental sustainability. An experimental study is performed to verify the practical implication of the model, and results are evaluated through a sensitivity analysis. Important managerial insights and graphical illustrations are provided to elaborate the model.

Event-Based Production Control for Energy Efficiency Improvement in Sustainable Multistage Manufacturing Systems

There has been an increasing trend for manufacturers to shift toward sustainable manufacturing strategies in response to an ever-growing pressure from fluctuating energy price and environmental crisis. Reducing energy consumption is considered as an important step to achieve the sustainability of a production system. This paper proposes an event-based control methodology to improve the production energy efficiency through strategically switching appropriate stations to energy saving mode. Based on an event-based analysis of production dynamics, an analytical approach is developed to quantitatively predict the system level production loss resulted from an energy saving control event (ESCE). A genetic-based control algorithm is proposed to balance the trade-off between the gain from energy saving and the expense of throughput loss. The energy improvement analysis results in a fundamental understanding of production energy dynamics and a significant decrease of energy cost for a manufacturing facility. Numerical case studies are performed to validate the effectiveness of the proposed method. It is found that the control method can effectively reduce energy cost, while only slightly impacting production.

Ensuring Product Quality, Consistency and Patient Supply over Time for a Large-Volume Biologic: Experience with Remicade®.

Biologics are produced from living organisms in complex, multi-stage manufacturing processes and contain inherent variability, which must be understood and controlled during manufacturing to avoid unexpected changes in key quality attributes that may contribute to clinically meaningful differences. The process must also meet large commercial demand, while simultaneously being able to accommodate change without sacrificing product consistency. The four key components of successful biologics manufacturing are (1) a stable, well-defined proprietary cell line; (2) a good manufacturing practice (GMP)-compliant supply chain with a process control strategy defining acceptable levels of variability for target product/process attributes and capable of managing complex material flows; (3) a tightly controlled procedure for implementation of proposed process changes that ensures product consistency; and (4) built-in redundancy and flexibility providing the ability to adapt rapidly to unexpected developments. This report describes the requirements for the manufacturing and distribution of biologics, using Remicade® (infliximab, Janssen Biotech, Horsham, PA, USA) as an example of best practices. Since Remicade’s first marketing approval in 1998, Janssen has manufactured > 150 million vials used to treat > 2.6 million patients around the world for a variety of inflammatory diseases. Remicade displays a highly consistent quality attribute profile and meets all product/process specifications across multiple manufacturing sites and process scales. Janssen’s experience with Remicade demonstrates that deep product knowledge, extensive manufacturing experience, diligent product/process monitoring and a sustained commitment to compliance and research are required to ensure quality, consistency and uninterrupted patient supply for large-volume biologics over the long term.

Impact of condition based maintenance policies on the service level of multi-stage manufacturing systems

Preventive maintenance is crucial for keeping the operational condition of machines but it affects the available productive time of the system. This influences the lot completion time and the delivery dates. Therefore the choice of preventive maintenance policies needs to be taken by jointly considering the actual condition of machines and the service level of the system. In this context, the main contribution of this paper consists in showing how the completion time of a lot is significantly affected by the preventive maintenance policy. This is done by means of numerical illustrations obtained by using both ad-hoc models and a real industrial case. The second contribution relies in the presented methodology that allows the modeling of general synchronous production lines and, given a lot size, the computation of their corresponding service level. The methodology is described in such a way that its extension to different scenarios (such as different system layout and asynchronous machines) is fairly straightforward.

Configuration of management accounting information system for multi-stage manufacturing

The article presents an approach to configuration of a management accounting information system (MAIS) that provides automated calculations and the registration of normative production losses in multi-stage manufacturing. The use of MAIS with the proposed configuration at the enterprises of textile and woodworking industries made it possible to increase the accuracy of calculations for normative production losses and to organize accounting thereof with the reference to individual stages of the technological process. Thus, high efficiency of multi-stage manufacturing control is achieved.

Modeling and Analysis of Multiproduct Multistage Manufacturing System for Quality Improvement

The ability to produce multiple types of products using the same manufacturing system plays an essential role in the success of a manufacturing enterprise. Meanwhile, most complex manufacturing systems include many stages, called multistage manufacturing systems (MMSs). MMS has a cascade property, which means the product quality is not only affected by the current stage, but is also related to the outputs of the upstream stage. Multiproduct MMSs ( ${{{{{{\text{M}}}}}}}^{ 3}$ Ss) have been widely applied in industry. Thus, this paper is devoted to modeling and analyzing steady-state ${{{{{{\text{M}}}}}}}^{ 3}$ Ss for quality improvement. The discrete Markov model for single-product-multistage system is extended to novel Markov models for multiproduct-two-stage systems and multiproduct-multistage systems by calculating state transition probabilities of six key manufacturing parameters to obtain an acceptable product quality probability. Based on the developed models, product sequence analysis is carried out to obtain the best product sequence under Bernoulli conditions and Bernoulli relaxation conditions, and bottleneck analysis under Bernoulli conditions is conducted to identify the machine and/or parameters whose improvement can lead to the largest quality improvement. The applicability of the proposed models is validated through numerical experiments and a case study using real-world data.

Evaluation of pull production control strategies under uncertainty: An integrated fuzzy AHP-TOPSIS approach

Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty.Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method.Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system.Practical implications: To examine the approach a real case from automobile electro mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand.Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteria.

A novel model for the integrated planning of part quality inspection and preventive maintenance in a linear-deteriorating serial multi-stage manufacturing system

This paper presents a mixed-integer linear mathematical programming model for the integrated planning problem of the part quality inspection and preventive maintenance activities in serial multi-stage manufacturing system. The model concurrently determines the right time and place for performing the above-mentioned activities while the production stages are linearly deteriorating. These two decisions are made while the model is to minimize the total cost including the production, maintenance, inspection, scrap, replacement, and the penalty of shipped defective items to customer(s). A numerical example and a real case study are investigated to validate and verify the proposed model. The results show that the determination of inspection locations along a manufacturing line in different periods of time regarding the impact of preventive maintenance activities on defective production probability results in a more efficient manufacturing system.

Transient analysis of quality performance in two-stage manufacturing systems with remote quality information feedback

Abstract Recently modeling and analysis of product quality propagation in multi-stage manufacturing systems has received lots of attention. However, most existing results are focused on steady state performance, while transient analysis of system quality remains largely unexplored. When product changeover or scheduled maintenance happens, the system quality may undergo transients, which describe the system quality behavior before approaching the steady state at targeted levels of quality and cost. It is of practical importance to comprehensively investigate the quality performance especially during transients in order to reduce quality loss and improve product quality. In this paper, a Markov model is developed to address quality propagation in a two-stage manufacturing system with remote quality information feedback during transients. Based on the proposed mathematical model, analytical formulas for evaluating transient quality performance including the real-time product quality, settling time, and quality loss due to transients, are derived. In addition, the monotonicity properties of critical transient system characteristics and quality performance metrics are explored. The proposed method is validated with numerical data and real-world data, and the results demonstrate the effectiveness for transient quality analysis in two-stage manufacturing systems.

Lead oxide-decorated graphene oxide/epoxy composite towards X-Ray radiation shielding

In this study, employing modified Hummers method coupled with a multi-stage manufacturing procedure, graphene oxide (GO) decorated with Pb3O4 (GO-Pb3O4) at different weight ratios was synthesized. Thereupon, via the vacuum shock technique, composites holding GO-Pb3O4 at different filler loadings (5 and 10wt%) and thicknesses (4 and 6mm) were fabricated. Successful decoration of GO with Pb3O4 was confirmed via FTIR analysis. Moreover, particle size distribution of the produced fillers was examined using particle size analyzer. X-ray attenuation examination revealed that reinforcement of epoxy-based composites with GO-Pb3O4 led to a significant improvement in the overall attenuation rate of X-ray beam. For instance, composites containing 10wt% GO-Pb3O4 with 6mm thickness showed 4.06, 4.83 and 3.91mm equivalent aluminum thickness at 40, 60 and 80kVp energies, denoting 124.3, 124.6 and 103.6% improvement in the X-ray attenuation rate compared to a sample holding neat epoxy resin, respectively. Simulation results revealed that the effect of GO-Pb3O4 loading on the X-ray shielding performance undermined with increase in the voltage of the applied X-ray beam.

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products’ manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality.

Monitoring the process quality for multistage systems with multiple characteristics

PurposeThe 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/approachThe 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.FindingsIn 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 implicationsThe 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/valueOnce 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.

Event-Based Supervisory Control for Energy Efficient Manufacturing Systems

It becomes more and more critical for manufacturing enterprises to improve energy efficiency because of the escalating energy prices, increasing global competitions, and more rigorous government regulations. In this paper, a systematic method is developed to improve the energy efficiency of a multistage manufacturing system through production control. The method aims at reducing energy consumption with minimal negative impact on production. We start from the analysis of system dynamics and develop quantitative methods to estimate energy saving opportunities. A supervisory control algorithm is developed to improve system energy efficiency by periodically taking the saving opportunities. Simulation case studies are performed to validate the effectiveness of the control algorithm. Note to Practitioners —Manufacturing systems are facing increasing pressure to reduce energy consumption, as global competition, sustainability, and green processes are becoming more prevalent. Although most of the research efforts on manufacturing energy saving have focused on developing individual energy efficient machines, it can be more cost-effective to improve energy efficiency through better control of the energy usage of the whole production system. Therefore, this paper presents a systematic method to improve system energy efficiency with a minimal negative impact on production. This paper continues the work by Chang et al. by extending the scope of energy saving opportunity theory from serial production systems to general serial–parallel production systems. It also develops analytical methods based on Markov chain models to estimate the energy saving opportunity accurately.