Optimization Methodologies in Intelligent Machining Systems - A Review

Imad, Mohamd; Hosseini, Ali; Kishawy, Hossam A.

doi:10.1016/j.ifacol.2019.10.043

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…From an intelligent manufacturing context, the research focus has been directed toward intelligent machining [ 86 ] with an aim for online monitoring and optimization of the machining parameters [ 87 ]. Thus, monitoring the machining state (i.e., tool condition, tool wear, machine stability, chatter, and vibration) becomes an important research area for intelligent subtractive manufacturing.…”

Section: Intelligence In State-of-the-art Manufacturing Technology: M...mentioning

confidence: 99%

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

et al. 2023

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Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

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Section: Intelligence In State-of-the-art Manufacturing Technology: M...mentioning

confidence: 99%

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

et al. 2023

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“…PSO algorithm is created by natural swarms that are formed of volume-less particles with random velocities, each of which shows a feasible solution. This algorithm finds the optimum solution by moving the particles in the solution space 39 . PSO performs searching via a swarm of particles that updates from iteration to iteration.…”

Section: Theoretical Studiesmentioning

confidence: 99%

A numerical approach to determine the optimal condition of the gas anti-solvent supercritical process for nanoparticles production

Ardestani

Amani

2022

Sci Rep

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Supercritical gas antisolvent (GAS) process is an efficient method for nanoparticles production, in which accurate selection of operational condition is essential. Thermodynamic models can be applied for evaluation the phase equilibrium behavior and determination the required precipitation pressure of GAS process. In this research, thermodynamic behavior of (CO2—dimethyl sulfoxide (DMSO)) binary system and both of (CO2–DMSO-anthraquinone Violet 3RN (AV3RN)) and (CO2–DMSO-solvent Yellow 33 (SY33)) ternary systems in the GAS process were studied at different temperatures (308, 318, 328 and 338) K and pressures (1.0–14.0) MPa, using Peng–Robinson equation of state (PR-EoS). The minimum precipitation pressure of AV3RN and SY33 at 308, 318, 328 and 338 K were 7.80, 8.57, 9.78 and 11 MPa and 8, 8.63, 9.5 and 10.77 MPa, respectively. Also, the mole fraction of substances in liquid phase of ternary systems were determined by PR-EoS, at 328 K versus pressure. The accuracy of the obtained results were investigated using the experimental data reported in the literatures.

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“…Additionally, process monitoring research still focuses on design, as highlighted by the review of Imad et al [14], Hopkins and Hosseini [15]. Lu et al [16] review the deployment of the STEP-NC standard for a CNC system programming interface that enables bi-directional communication between machine tool controllers, computer-aided design (CAD)/Computer-Aided Manufacturing (CAM) software, monitoring software, etc.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

Bernard

Achiche

Girard³

et al. 2021

JMMP

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Manufacturing processes can be monitored for anomalies and failures just like machines, in condition monitoring and prognostic and health management. This research takes inspiration from condition monitoring and prognostic and health management techniques to develop a method for part production process monitoring. The contribution brought by this paper is an automated technique for process monitoring that works with low sampling rates of 1/3Hz, a limitation that comes from using data provided by an industrial partner and acquired from industrial manufacturing processes. The technique uses kernel density estimation functions on machine tools spindle load historical time signals for distribution estimation. It then uses this estimation to monitor the manufacturing processes for anomalies in real time. A modified version was tested by our industrial partner on a titanium part manufacturing line.

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Optimization Methodologies in Intelligent Machining Systems - A Review

Cited by 8 publications

References 30 publications

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

A numerical approach to determine the optimal condition of the gas anti-solvent supercritical process for nanoparticles production

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

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