A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC)

Patange, Abhishek D.; Jegadeeshwaran, R.

doi:10.1016/j.measurement.2020.108649

Cited by 55 publications

(21 citation statements)

References 37 publications

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“…ML, a subset of AI, provides the capacity to learn and improve the systems from experience and thus, is widely applied in different areas of manufacturing [ 108 ]. In recent years, ML has been found to be a useful tool for in-process tool condition monitoring [ 109 ], predicting self-induced vibrations and chatter in heavy-duty milling machines [ 110 ], by creating complex analytical models and simulations. The experimentation was carried out to predict chatter based on different machine positions and milling directions [ 111 ].…”

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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“…Pilot trials were made to select the working range of machining parameters. Cutting speed, depth of cut, and feed rate levels have been carefully chosen [22][23][24].…”

Section: Machining (End Milling) Of the Compositementioning

confidence: 99%

Effect of machining parameters on surface roughness and energy consumption during end milling of stir cast Al-4032/6%SiC composite

Saini¹,

Singh²

2022

Surf. Topogr.: Metrol. Prop.

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The machining of metal matrix composite (MMC) presents a significant challenge to the industry. The hard and abrasive nature of the reinforcement causes rapid tool wear and high machining cost. In the present research, Al-4032 based composite with 6% of SiC by weight has been fabricated by stir casting setup. Optical micrographs (OM) and scanning electron microscopy (SEM) with energy dispersive x-ray analysis (EDXA) have been used for the examination of the composite morphological, surface topography, and fracture behavior. Mechanical characterization i.e., testing of the composite for tensile strength, micro-hardness and impact strength has also been carried out. Cutting speed, feed rate and depth of cut are the cutting parameters selected to examine the responses—surface finish and energy consumption. The response surface methodology (RSM) based desirability approach has been employed to obtain the best combination of parameters for achieving the desired objectives. The result established that the inclusion of SiC in the base matrix demonstrates improved mechanical properties and a better-machined surface with optimized machining parameters. Multi-response optimization shows that the minimum surface roughness (0.99 μm) and minimum energy consumption (0.17 kWh) during end milling of Al-4032/6%SiC composite have been achieved at cutting speed 199 m min−1, feed rate 500 mm min−1 and depth of cut 0.50 mm. The confirmation experiments show good agreement between the predicted and measured responses.

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“…By considering the needs of machining industry for more intelligent manufacturing processes, researchers [31][32][33] showed that the fixture clamps can be designed to be able to apply variable contact stiffness during the process, as a means to achieving optimal contact stiffness.…”

Section: Introductionmentioning

confidence: 99%

Evaluation, calibration, and modal analysis for determination of contact stiffness between workpiece and components of milling fixture

Sohrabifard

Nategh

Ghazavi

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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The stability of workpiece inside fixture influences the dynamics of machining. Although, the workpiece is uniquely and deterministically positioned and grasped by the fixture, it undergoes small displacements due to the contact compliance resulting from the frictional slip under the external loads and vibrations. The frictional slip of workpiece, along or around the main axes relative to the fixture components is a major cause of instability and the main element influencing the contact stiffness between the workpiece and the fixture. In the present study, a procedure has been developed to evaluate the contact stiffness by employing mathematical and experimental tools. The grasp matrices defined in analytical model were obtained by a combination of calibration and measurement of forces, displacements, and friction coefficient. The contact stiffness was also determined by modal analysis of the assembly of workpiece and fixture and the results were compared. It was evidenced that the dry friction existing in the contact areas between the workpiece and fixture elements could lead to the stick-slip instability, detrimental to the dynamics of machining. The main contribution and novelty of the present study can be outlined, as follows. The contact stiffness between workpiece and fixture was modeled and measured. The frictional slip was modeled and measured in addition to the physical compliances. Dry friction between workpiece and fixture was detected as a source of stick-slip. Procedures were developed to calibrate and measure all forces and displacements. Modal analysis was done as a complementary tool to verify the results.

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A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC)

Cited by 55 publications

References 37 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

Effect of machining parameters on surface roughness and energy consumption during end milling of stir cast Al-4032/6%SiC composite

Evaluation, calibration, and modal analysis for determination of contact stiffness between workpiece and components of milling fixture

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