Hybrid offline-online optimization, monitoring and control of milling processes

Gohari, Hossein; Mohamed, Ayman; Hassan, Mahmoud; M’Saoubi, Rachid; Attia, Helmi

doi:10.1016/j.cirp.2023.04.017

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…A summary of the developed optimization systems to control force/power and MRR is presented in Table 6, which shows that offline process optimization is the primary approach to optimizing cutting operations. In a recent study performed by the authors (published in 2023) [13], an offline/online optimization approach was developed for milling operations, in which offline optimization of the cutting forces was implemented based on a limit for the tool deflection, along with online power optimization to reduce the cutting time and avoid excessive tool wear and high thermal stresses at the tool-chip interface.…”

Section: Process Parameters Adaptive Controlmentioning

confidence: 99%

“…Cutting force, dynamic stability and cutting temperature [13] A hybrid optimization, monitoring, and control (HOMC) system was introduced considering the machining primary limits of chatter, tool deflection, and thermal stresses…”

Section: Offline/ Onlinementioning

confidence: 99%

“…(2) offline-online optimization and adaptive machining. This technology, which can easily be incorporated in a CCPS platform, was shown to improve productivity by up to 45%, and when integrated with a TCM, the production cost could be reduced by up to 25% [13]; and (3) physics-based constitutive models for anisotropic and graded materials that need to be developed and combined with artificial intelligence (AI) and swarm intelligent (SI) techniques to improve CCPS's adaptability and scalability.…”

Section: Introductionmentioning

confidence: 99%

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Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

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Section: Process Parameters Adaptive Controlmentioning

confidence: 99%

Section: Offline/ Onlinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

Self Cite

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“…Modelling is expected to be the first step towards creating a nominal case and studying variations on top of it. This is highly relevant to the concept of milling in an adaptive environment [30,31].…”

Section: Introductionmentioning

confidence: 99%

Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning

Papacharalampopoulos,

Alexopoulos,

Catti

et al. 2024

Processes

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Monitoring tool wear is key for the optimization of manufacturing processes. To achieve this, machine learning (ML) has provided mechanisms that work adequately on setups that measure the cutting force of a tool through the use of force sensors. However, given the increased focus on sustainability, i.e., in the context of reducing complexity, time and energy consumption required to train ML algorithms on large datasets dictate the use of smaller samples for training. Herein, the concepts of active learning (AL) and transfer learning (TL) are simultaneously studied concerning their ability to meet the aforementioned objective. A method is presented which utilizes AL for training ML models with less data and then it utilizes TL to further reduce the need for training data when ML models are transferred from one industrial case to another. The method is tested and verified upon an industrially relevant scenario to estimate the tool wear during the turning process of two manufacturing companies. The results indicated that through the application of the AL and TL methodologies, in both companies, it was possible to achieve high accuracy during the training of the final model (1 and 0.93 for manufacturing companies B and A, respectively). Additionally, reproducibility of the results has been tested to strengthen the outcomes of this study, resulting in a small standard deviation of 0.031 in the performance metrics used to evaluate the models. Thus, the novelty presented in this paper is the presentation of a straightforward approach to apply AL and TL in the context of tool wear classification to reduce the dependency on large amounts of high-quality data. The results show that the synergetic combination of AL with TL can reduce the need for data required for training ML models for tool wear prediction.

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Active-passive hybrid feed rate control systems in CNC machining: Mitigating force fluctuations and enhancing tool life

Li,

Zhao,

Wang

et al. 2024

Journal of Manufacturing Systems

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Hybrid offline-online optimization, monitoring and control of milling processes

Cited by 4 publications

References 3 publications

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning

Active-passive hybrid feed rate control systems in CNC machining: Mitigating force fluctuations and enhancing tool life

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