Statistical process control of assembly lines in manufacturing

Bottani, Eleonora; Montanari, Roberto; Volpi, Andrea; Tebaldi, Letizia

doi:10.1016/j.jii.2023.100435

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…On the other hand, SPC stands out as a primary method in quality control, encompassing a range of effective problem-solving techniques aimed at ensuring process stability and enhancing processing capacity by minimizing variability [19]. It is widely recognized as a traditional strategy in the manufacturing sector for reducing unpredictability and fostering continuous improvement [20].…”

Section: Statistical Process Control (Spc)mentioning

confidence: 99%

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Dogan,

Areta Hiziroglu

2024

Machines

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The production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC–PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the ‘Quality Control’ activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the ‘Return from Waiting’ activity to ‘Packing and Shipment’ is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.

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Section: Statistical Process Control (Spc)mentioning

confidence: 99%

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Dogan,

Areta Hiziroglu

2024

Machines

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“…Predictive technology is the fundamental component of the PdM plan and can be broadly categorized into three distinct approaches [ 18 ]. Several methods can be employed to assess errors from a statistical standpoint such as cluster analysis [ 19 ], statistical process control (SPC) [ 20 ], hidden Markov models (HMM) [ 21 ], and other statistical methods. Another method involves utilizing model-based approaches to develop a precise and well-defined mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

Irfan,

Liao,

Taipabu

et al. 2024

Sensors

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The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications. MoSi2 heating elements are integral components in high-temperature environments, playing a pivotal role in achieving optimal thermal performance. However, prolonged exposure to extreme conditions leads to degradation, necessitating precise RUL predictions for proactive maintenance strategies. Since insufficient failure experience deals with Predictive Maintenance (PdM) in real-life scenarios, a Generative Adversarial Network (GAN) generates specific training data as failure experiences. The Remaining Useful Life (RUL) is the duration of the equipment’s operation before repair or replacement, often measured in days, miles, or cycles. Machine learning models are trained using historical data encompassing various operational scenarios and degradation patterns. The RUL prediction model is determined through training, hyperparameter tuning, and comparisons based on the machine-learning model, such as Long Short-Term Memory (LSTM) or Support Vector Regression (SVR). As a result, SVR reflects the actual resistance variation, achieving the R-Square (R2) of 0.634, better than LSTM. From a safety perspective, SVR offers high prediction accuracy and sufficient time to schedule maintenance plans.

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“…Em um mercado cada vez mais competitivo, uma das principais estratégias adotadas pelas empresas para obter vantagens competitivas é monitorar seus processos e obter qualidade no desenvolvimento de seus produtos (Andres-Jimenez et al, 2020;Bottani et al, 2023). Dentre as diversas ferramentas existentes utilizadas para avaliar a qualidade e o controle de processos no contexto da indústria de transformação, vale destacar o Controle Estatístico de Processo (CEP), que permite monitorar e controlar a qualidade da produção que normalmente precisam atender a determinadas métricas.…”

Section: Introductionunclassified

Análise bibliométrica do controle estatístico de processo (CEP) aplicado ao processo de fabricação

Valença,

Oliveira

2024

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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Neste trabalho, foi realizada uma extensa revisão bibliométrica sobre a produção científica de controle estatístico de processo, aplicada à indústria de transformação, mapeando as principais pesquisas da literatura, bem como os principais periódicos que estão publicando esta pesquisa. O controle estatístico de processo é uma das principais ferramentas que permitem aos gestores de produção determinar se os processos atendem aos requisitos pré-determinados pelos clientes, proporcionando melhor qualidade do produto e do processo. A análise ilustra a evolução da pesquisa nas últimas décadas, os principais periódicos para publicação, o nível de concentração ou fragmentação da comunidade científica e a densidade geográfica das colaborações em pesquisa. Por fim, também são apresentados os principais temas que têm sido abordados pela comunidade científica que debatem o CEP em aplicações de manufatura e pesquisas futuras para o direcionamento deste tema.

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Statistical process control of assembly lines in manufacturing

Cited by 6 publications

References 33 publications

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Empowering Manufacturing Environments with Process Mining-Based Statistical Process Control

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

Análise bibliométrica do controle estatístico de processo (CEP) aplicado ao processo de fabricação

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