Proceedings of the 4th International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME2018)

The Mechatronics Department (Accredited by National Board of Accreditation, New Delhi, India) of the G H Patel College of Engineering and Technology, Gujarat, India arranged the 4th International Conference on Innovations in Automation and Mechatronics Engineering 2018, (ICIAME 2018) on 2-3 February 2018. The papers presented during the conference were based on Automation, Optimization, Computer Aided Design and Manufacturing, Nanotechnology, Solar Energy etc and are featured in this book

IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN γ-TiAl AEROSPACE ALLOY MACHINING

The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (γ-TiAl), capable of improved product and process performance. However, while γ-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency

Toward Magnetic Resonance Only Treatment Planning: Distortion Mitigation And Image-Guided Radiation Therapy Validation

While MR-only treatment planning has shown promise, there are still several well-known challenges that are currently limiting widespread clinical implementation. Firstly, MR images are affected by both patient-induced and system-level geometric distortions that can significantly degrade treatment planning accuracy. . In addition, the availability of comprehensive distortion analysis software is currently limited. Also while many groups have been working toward a synthetic CT solution, further study is needed on the implementation of synCTs as the reference datasets for linac-based image-guided radiation therapy (IGRT) to help determine their robustness in an MR-only workflow. A 36×43×2 cm3 phantom with 255 known landmarks (~1 mm3) was scanned using 1.0T high-field open MR-SIM at isocenter in the transverse, sagittal, and coronal axes, and a 465x350x168mm 3D phantom was scanned by stepping in the superior-inferior direction in 3 overlapping positions to achieve a total 465x350x400mm sampled FOV yielding \u3e13,800 landmarks(3D Gradient-Echo, TE/TR/α = 5.54 ms/30 ms/28°, voxel size =1×1×2mm3). A binary template (reference) was generated from a phantom schematic. An automated program converted MR images to binary via masking, thresholding, and testing for connectivity to identify landmarks. Distortion maps were generated by centroid mapping. Images were corrected via warping with inverse distortion maps, and temporal stability was assessed. To determine candidate materials for phantom and software development, 1.0 T MR and CT images were acquired of twelve urethane foam samples of various densities and strengths. Samples were precision machined to accommodate 6 mm diameter paintballs used as landmarks. Final material candidates were selected by balancing strength, machinability, weight, and cost. Bore sizes and minimum aperture width resulting from couch position were tabulated from the literature (14 systems, 5 vendors). Bore geometry and couch position were simulated using MATLAB to generate machine-specific models to optimize the phantom build. Previously developed software for distortion characterization was modified for several magnet geometries (1.0 T, 1.5 T, 3.0 T), compared against previously published 1.0 T results, and integrated into the 3DSlicer application platform. To evaluate the performance of synthetic CTs in an image guided workflow, magnetic resonance simulation and CT simulation images were acquired of an anthropomorphic skull phantom and 12 patient brain cancer cases. SynCTs were generated using fluid attenuation inversion recovery, ultrashort echo time, and Dixon data sets through a voxel-based weighted summation of 5 tissue classifications. The DRRs were generated from the phantom synCT, and geometric fidelity was assessed relative to CT-generated DRRs through bounding box and landmark analysis. An offline retrospective analysis was conducted to register cone beam CTs to synCTs and CTs using automated rigid registration in the treatment planning system. Planar MV and KV images were rigidly registered to synCT and CT DRRs using an in-house script. Planar and volumetric registration reproducibility was assessed and margin differences were characterized by the van Herk formalism. Over the sampled FOV, non-negligible residual gradient distortions existed as close as 9.5 cm from isocenter, with a maximum distortion of 7.4mm as close as 23 cm from isocenter. Over 6 months, average gradient distortions were -0.07±1.10 mm and 0.10±1.10 mm in the x and y-directions for the transverse plane, 0.03±0.64 and -0.09±0.70 mm in the sagittal plane, and 0.4±1.16 and 0.04±0.40 mm in the coronal plane. After implementing 3D correction maps, distortions were reduced to \u3c 1 pixel width (1mm) for all voxels up to 25 cm from magnet isocenter. All foam samples provided sufficient MR image contrast with paintball landmarks. Urethane foam (compressive strength ∼1000psi, density ~20lb/ft3) was selected for its accurate machinability and weight characteristics. For smaller bores, a phantom version with the following parameters was used: 15 foam plates, 55×55×37.5 cm3 (L×W×H), 5,082 landmarks, and weight ~30 kg. To accommodate \u3e70 cm wide bores, an extended build used 20 plates spanning 55×55×50 cm3 with 7,497 landmarks and weight ~44 kg. Distortion characterization software was implemented as an external module into 3DSlicer’s plugin framework and results agreed with the literature. Bounding box and landmark analysis of phantom synCT DRRs were within 1 mm of CT DRRs. Absolute planar registration shift differences ranged from 0.0 to 0.7 mm for phantom DRRs on all treatment platforms and from 0.0 to 0.4 mm for volumetric registrations. For patient planar registrations, the mean shift differences were 0.4±0.5 mm (range, 0.6 to 1.6 mm), 0.0±0.5 mm (range, 0.9 to 1.2 mm), and 0.1±0.3 mm (range, 0.7 to 0.6 mm) for the superior-inferior (S-I), left-right (L-R), and anterior-posterior (A-P) axes, respectively. The mean shift differences in volumetric registrations were 0.6±0.4 mm (range, 0.2 to 1.6 mm), 0.2±0.4 mm (range, 0.3 to 1.2 mm), and 0.2±0.3 mm (range, 0.2 to 1.2 mm) for the S-I, L-R, and A-P axes, respectively. The CT-SIM and synCT derived margins were \u3c0.3mm different. This work has characterized the inaccuracies related to GNL distortion for a previously uncharacterized MR-SIM system at large FOVs, and established that while distortions are still non-negligible after current vendor corrections are applied, simple post-processing methods can be used to further reduce these distortions to less than 1mm for the entire field of view. Additionally, it was important to not only establish effective corrections, but to establish the previously uncharacterized temporal stability of these corrections. This work also developed methods to improve the accessibility of these distortion characterizations and corrections. We first tested the application of a more readily available 2D phantom as a surrogate for 3D distortion characterization by stepping the table with an integrated batch script file. Later we developed and constructed a large modular distortion phantom using easily obtainable materials, and showed and constructed a large modular distortion phantom using easily obtainable materials, and used it to characterize the distortion on several widely available MR systems. To accompany this phantom, open source software was also developed for easy characterization of system-dependent distortions. Finally, while the dosimetric equivalence of synCT with CT has been well established, it was necessary to characterize any differences that may exist between synCT and CT in an IGRT setting. This work has helped to establish the geometric equivalence of these two modalities, with some caveats that have been discussed at length

Prediction of the Wear & Evolution of Cutting Tools in a Carbide / Ti-6Al-4V Machining Tribosystem by Volumetric Tool Wear Characterization & Modeling

The objective of this research work is to create a comprehensive microstructural wear mechanism-based predictive model of tool wear in the tungsten carbide / Ti-6Al-4V machining tribosystem, and to develop a new topology characterization method for worn cutting tools in order to validate the model predictions. This is accomplished by blending first principle wear mechanism models using a weighting scheme derived from scanning electron microscopy (SEM) imaging and energy dispersive x-ray spectroscopy (EDS) analysis of tools worn under different operational conditions. In addition, the topology of worn tools is characterized through scanning by white light interferometry (WLI), and then application of an algorithm to stitch and solidify data sets to calculate the volume of the tool worn away. The motivation for this work is two-fold. First, the evolving dominance of different wear mechanisms with time, as well as with significant tool and process factors has been characterized only in a limited fashion for this tribosystem. Traditional modeling of tool wear treats wear mechanisms individually. Hence, quantifying the mechanism-dominance at different operational conditions through a comprehensive approach of combining and weighting wear mechanisms is essential for understanding wear. Second is the critical need for better quantifying the wear itself. Wear is a 3D phenomenon. However, machining tool wear has historically been measured only in 1D which is inadequate to capture the true tool wear status, even with standardization. The methodology was to first combine and weight dominant microstructural wear mechanism models, to be able to effectively predict the tool volume worn away. Then, by developing a new metrology method for accurately quantifying the bulk-3D wear, the model-predicted wear was validated against worn tool volumes obtained from corresponding machining experiments. The changing dominance of different microstructural wear mechanisms was captured by formulating mechanism-weighting-factors from SEM imaging and EDS analysis. These were formulated for each of the three speed-regimes, which then fed into a multi-mechanistic volumetric wear rate model. On comparing this model-predicted wear to the actual tool volume worn away, prediction on the order of the observed wear was achieved, with better prediction at low and medium surface speeds - this was quantified by sum-of-squares computations. On analyzing worn crater faces using SEM/EDS, adhesion was found dominant at lower surface speeds, while dissolution wear dominated with increasing speeds - this is in conformance with the lower relative surface speed requirement for micro welds to form and rupture, essentially defining the mechanical load limit of the tool material. It also conforms to the known dominance of high temperature-controlled wear mechanisms with increasing surface speed, which is known to exponentially increase temperatures especially when machining Ti-6Al-4V due to its low thermal conductivity. Thus, straight tungsten carbide wear when machining Ti-6Al-4V is mechanically-driven at low surface speeds and thermally-driven at high surface speeds. Further, at high surface speeds, craters were formed due to carbon diffusing to the tool surface and being carried away by the rubbing action of the chips - this left behind a smooth crater surface predominantly of tungsten and cobalt as observed from EDS analysis. Also, at high surface speeds, carbon from the tool was found diffused into the adhered titanium layer to form a titanium carbide (TiC) boundary layer - this was observed as instances of TiC build-up on the tool edge from EDS analysis. A complex wear mechanism interaction was thus observed, i.e., titanium adhered on top of an earlier worn out crater trough, additional carbon diffused into this adhered titanium layer to create a more stable boundary layer (which could limit diffusion-rates on saturation), and then all were further worn away by dissolution wear as temperatures increased. At low and medium feeds, notch discoloration was observed - this was detected to be carbon from EDS analysis, suggesting that it was deposited from the edges of the passing chips. Mapping the dominant wear mechanisms showed the increasing dominance of dissolution wear relative to adhesion, with increasing grain size - this is because a 13% larger sub-micron grain results in a larger surface area of cobalt exposed to chemical action. On the macro-scale, wear quantification through topology characterization elevated wear from a 1D to 3D concept. From investigation, a second order dependence of volumetric tool wear (VTW) and VTW rate with the material removal rate (MRR) emerged, suggesting that MRR is a more consistent wear-controlling factor instead of the traditionally used cutting speed. A predictive model for VTW was developed which showed its exponential dependence with workpiece stock volume removed. Also, both VTW and VTW rate were found to be dependent on the accumulated cumulative wear on the tool. Further, a ratio metric of stock material removed to tool volume lost is now possible as a tool efficiency quantifier and energy-based productivity parameter, which was found to inversely depend on MRR - this led to a more comprehensive tool wear definition based on cutting tool efficiency

Analysis, optimization, FE simulation of micro-cutting processes and integration between Machining and Additive Manufacturing.

La seguente Tesi di Dottorato riguarda i processi di Micro-Machining (MM) applicati su materiali ottenuti per fabbricazione additiva. I processi MM sono un insieme di tecnologie di produzione utilizzate per fabbricare componenti o realizzare features di piccole dimensioni. In generale, i processi di taglio sono caratterizzati da un'interazione meccanica tra un pezzo e un utensile che avviene lungo una determinata traiettoria. Il contatto determina una rottura del materiale lungo un percorso definito, ottenendo diverse forme del pezzo. Più precisamente, la denominazione di microlavorazione indica solo le lavorazioni di taglio eseguite utilizzando un utensile di diametro inferiore a 1 mm. La riduzione della scala dimensionale del processo introduce alcune criticità non presenti negli analoghi processi su scala convenzionale, come l'effetto dimensionale, la formazione di bave, la rapida usura dell'utensile, le forze di taglio superiori alle attese e l'eccentricità del moto dell'utensile. Negli ultimi decenni, diversi ricercatori hanno affrontato problemi relativi alla microlavorazione, ma pochi di loro si sono concentrati sulla lavorabilità dei materiali prodotti per Additive Manufacturing (AM). L’AM è un insieme di processi di fabbricazione strato per strato che possono essere impiegati con successo utilizzando polimeri, ceramica e metalli. L'AM dei metalli si sta rapidamente diffondendo nella produzione industriale trovando applicazioni in diversi rami, come l'industria aerospaziale e biomedica. D’altro canto, la qualità del prodotto finale non è comparabile con gli standard ottenibili mediante i metodi convenzionali di rimozione del materiale. Lo svantaggio principale dei componenti realizzati mediante AM è la bassa qualità della finitura superficiale e l'elevata rugosità; pertanto, sono solitamente necessari ulteriori trattamenti superficiali post-processo per adeguare le superfici del prodotto ai requisiti di integrità superficiale. L'integrazione tra le due tecnologie manifatturiere offre opportunità rilevanti, ma la necessità di ulteriori studi e indagini è evidenziata dalla mancanza di pubblicazioni su questo argomento. Questa ricerca mira ad esplorare diversi problemi connessi alla microlavorazione di leghe metalliche prodotte mediante AM. Le prove sperimentali sono state eseguite utilizzando il centro di lavoro ultrapreciso a 5 assi “KERN Pyramid Nano”, mentre i campioni AM sono stati forniti da aziende e gruppi di ricerca. L'attrezzatura sperimentale è stata predisposta per eseguire la micro-fresatura e per monitorare il processo in linea misurando la forza di taglio. Il comportamento di rimozione del materiale è stato studiato e descritto per mezzo di modelli analitici e simulazioni FEM. I metodi FE sono stati utilizzati anche per eseguire un confronto tra le forze di taglio previste e i carichi sperimentali, con lo scopo finale di affinare la legge di flusso dei materiali lavorati. La ricerca futura sarà focalizzata sulla simulazione FE dell'usura dell'utensile e dell'integrità della superficie del pezzo.This thesis is focused on Micro-Machining (MM) processes applied on Additively Manufactured parts. MM processes are a class of manufacturing technology designed to produce small size components. In general, cutting processes are characterized by a mechanical interaction between a workpiece and a tool. The contact determines a material breakage along a defined path, obtaining different workpiece shapes. More specifically, the micro-machining designation indicates only the cutting processes performed by using a tool with a diameter lower than 1 mm. The reduction of the process scale introduces some critical issues, such as size effect, burr formation, rapid tool wear, higher than expected cutting forces and tool run-out. In the last decades, several researchers have tackled micro-machining related issues, but few of them focused on workability of Additive Manufactured materials. Additive Manufacturing (AM) is a collection of layer-by-layer building processes which can be successfully employed using polymers, ceramics and metals. AM of metals is rapidly spreading throughout the industrial manufacturing finding applications in several branches, such as aerospace and biomedical industries. Moreover, the final product quality is not comparable with the standards achievable through the conventional subtractive material removal methods. The main drawback of additively manufactured components in metals is the low quality of the surface finish and the high surface roughness, therefore further post-process surface treatments are usually required to finish and to refine the surfaces of the build product. The embedding between the two technologies offers relevant opportunities, but the necessity of further studies and investigation is highlighted by the lack of publication about this topic. This research aimed to explore several micro-machining issues with regards to Additive Manufactured metals. Experimental tests were performed by using the ultraprecision 5-axes machining center “KERN Pyramid Nano”, while the AM samples were provided by companies and research groups. The experimental equipment was set-up to perform micro-milling and to monitor the process online by measuring the cutting force. The material removal behavior was investigated and described by means of analytical models and FEM simulations. FE methods were employed also to perform a comparison between the predicted cutting forces and the experimental loads, with the final purpose of refining the flow stress law of the machined materials. The future research will be focused on the FE simulation of the tool wear and the workpiece surface integrity by means of specific subroutines

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme

In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university

Condition monitoring of tool performance using a machine learning-based on-machine vision system during face milling of Inconel 718

The superior properties of Inconel 718 necessitate its use in manufacturing more than 50% of aircraft engine structural components, including high-pressure compressor blades, casings, and discs. However, literature attributed the synergistic impact of these properties and process parameters as the primary cause of wear complexity, notably affecting the performance of PVD-coated carbide inserts during CNC milling of Inconel 718. Features stemming from the wear complexity include uncontrolled wear mechanisms, failure modes, and a rapid flank wear rate, serving as significant indicators of sub-optimal cutting conditions. In trying to diagnose tool wear, previous Tool Condition Monitoring (TCM) techniques could not decipher, explore, and synthesise the diverse features essential for the predictive control of tool performance in challenging CNC machining conditions. Therefore, the successful implementation of advanced feature engineering and Machine Learning (ML) models in Machine Vision-based TCM (MVTCM) offers a proactive approach in predicting and controlling the performance of PVD-coated carbide tools in challenging CNC machining domains. The hypothesis of this study encompassed three aspects. The first aspect focused on the study of tool wear complexity by characterizing the dominant wear mechanisms, failure modes, and flank wear depth (VB) during face milling of Inconel 718. These features were correlated with the process parameters to establish a coherent tool wear dataset for training the feature engineering and ML models. The second aspect involved the development of feature engineering and ML models, including the multi-sectional singular value decomposition (SVD), a YOLOv3 Tool Wear Detection Model (YOLOv3-TWDM), a multi-layer perceptron neural network (MLPNN), and an inductive-reasoning algorithm. The final aspect pertained to the development of a volatile MV-TCM system’s design, which was integrated with feature engineering and ML techniques to create an enhanced ML-based MV-TCM system. The system was vigorously validated by conducting an online experiment, where the predicted were compared with the actual wear measurements. Furthermore, the inductive reasoning algorithm was devised to regulate process parameters for in-process control of flank wear evolution. The findings demonstrate that the Diverse Feature Synthesis Vector devised in this research was superior in representing the complex flank wear morphology as compared to some data reported by relevant literature, where geometric and fractal features were used to predict VB progression online. In addition, the ML-based MV-TCM system successfully utilized the DFSV to predict and control VB rate during face milling of Inconel 718. The system achieved higher predictive efficiency than image processing-based MV-TCM systems applied in the previous studies, with an offline validation RMSE of 45.5µm, R2 of 96.52%, and MAPE of 2.36%, as well as an online validation RMSE of 29.09µm, R2 of 97%, and MAPE of 3.52%. Additionally, the system employed a multi-stage optimization strategy that regulated process parameters at different VB levels to minimize the magnitudes of flank wear and chipping. This strategy extended tool life by 63.63% (relative to the conventional method) and 56.52% (relative to the GKRR soft-computing technique). Therefore, this research demonstrates the significance of applying ML-based MV-TCM system for predictive control of tool wear evolution during CNC milling of Inconel 718