A proposal for improving production efficiency of existing machining line through a hybrid monitoring and optimisation process

Herwan, Jonny; Misaka, Takashi; Furukawa, Yoshiyuki; Ogura, Ichiro; Komoto, Hitoshi

doi:10.1080/00207543.2022.2101403

Cited by 9 publications

(5 citation statements)

References 44 publications

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“…With the onset of Industry 4.0 and realization of the IIoT, there has been a marked shift towards smart maintenance, combining the TPM activities of lean with predictive capabilities presented from sensors, data sets and ML (Hosseinzadeh et al , 2023). For example, Herwan et al (2023) presented ML as a means of realizing smart tool life management by monitoring tool wear and optimizing tool usage. Mjimer et al (2023) also discussed the transition from preventive maintenance to predictive maintenance, allowing the elimination of unnecessary downtime for scheduled maintenance if the condition of the equipment deems servicing premature.…”

Section: Resultsmentioning

confidence: 99%

“…Results Ahmed et al, 2023;Antosz et al, 2020;Herwan et al, 2023;Hosseinzadeh et al, 2023;Küfner et al, 2021a;Mjimer et al, 2023;Shahin et al, 2023c;Shakir and Iqbal, 2018) Smart production planning and control(Bouzekri et al, 2022;Castej on-Limas et al, 2022;Duhem et al, 2023;Fanti et al, 2022;Herwan et al, 2023;ITO et al, 2020; Jan et al, 2023; Javaid et al, 2022; Khadiri et al, 2022; Küfner et al, 2021b; Kutschenreiter-Praszkiewicz, 2018; Paraschos et al, 2023; Puche et al, 2019; Rossit et al, 2019; Sordan et al, 2022; Tripathi et al, 2022b, 2022c; Ulhe et al, 2023; Vickranth et al, 2019; Villalba-Díez et al, 2020; Xia et al, 2022; Xin et al, 2015) Quality control (Bhatia et al, 2023; Duc and Bilik, 2022; Kumar et al, 2021; Park et al, 2020; Perera et al, 2021; Pongboonchai-Empl et al, 2023; Shahin et al, 2023b; Yadav et al, 2020) Towards Industry 5.0: Sustainability, Resilience, Human-centricity…”

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confidence: 97%

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Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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Section: Resultsmentioning

confidence: 99%

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confidence: 97%

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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“…Vibration Monitoring [19][20][21][22]: Based on the principle that tool wear alters the geometry of the tool, as well as the dynamic behavior of the machining process. Accelerometers are commonly used to measure the vibrations present during the machining process.…”

Section: Literature Review: Tool Wear Monitoringmentioning

confidence: 99%

An experimental study of multi-sensor tool wear monitoring and its application to predictive maintenance

Granados,

Misaka,

Herwan

et al. 2024

Preprint

Self Cite

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Wear in cutting tools is a critical issue that can lead to reduced product quality, increased production costs, and unexpected downtime. To mitigate these challenges, the implementation of tool wear monitoring systems and predictive maintenance strategies has gained significant attention in recent years. Early detection or prediction of tool wear is vital to optimize tool life and maintain the manufacturing processes efficiently. This paper presents a method to determine the tool wear progression based on the collaboration of direct and indirect monitoring techniques. By analyzing the monitoring of data from force, vibration, sound, and current sensors to estimate the tool wear state, and correlating this information with a photographic database of the tool wear progression used to create an image recognition system that can classify the tool wear at any moment into three states: Good, Moderate and Worn. A case study was conducted to test the advantages and limitations of the proposed method. The case study also shows that the improvement of the prediction of the tool wear state might be useful in the decision-making of whether the tool life can be extended, or the tool must be replaced, as well as in the detection of anomalies during the machining process, aiming its improvement and the reduction of operational costs.

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“…This innovative approach leverages a risk matrix function, which enables a structured evaluation and visualization of threats. This approach is driven by real-time data, which require a dynamic understanding of risk by considering multiple factors and probabilistic approaches for making real-time decisions [23]. The methodology established in this study will accelerate the development of tasks, monitor real-time data, and assess the risk of error creation and its severity.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approach in Aerospace for Error Mitigation

Bautista-Hernández,

Martín-Prats

2024

Aerospace

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Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

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A proposal for improving production efficiency of existing machining line through a hybrid monitoring and optimisation process

Cited by 9 publications

References 44 publications

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Artificial intelligence in lean manufacturing: digitalization with a human touch?

An experimental study of multi-sensor tool wear monitoring and its application to predictive maintenance

Artificial Intelligence Approach in Aerospace for Error Mitigation

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