Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation.

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Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling

Application of Measurement Systems in Tool Condition Monitoring of Milling: A Review of measurement science approach

Shape memory alloys (SMA) hold a very promising place in the field of manufacturing, especially in biomedical and aerospace applications. Owing to the unique and favorable properties such as pseudo elasticity, shape memory effect and Superelasticity, Nitinol is the most popular amongst other SMAs. However, a major challenge lies in the final surface features of the machined component. In the current study, Nitinol rods were machined using the wire electrical discharge machining (WEDM) process and subsequently, the surfaces were investigated using the Field emission scanning electron miscroscope (FESEM) technique for the features. In addition to this, Singular Generative Adversarial Network (SinGAN) and DenseNet deep learning models were prepared and applied for the prediction of surface morphology and its correlation with the process parameters. It was concluded from the study that the DenseNet model was highly effective in predicting the surface images with 100% average accuracy both with training and testing whereas the least average accuracy of 99.13% and 98.98% with training and testing respectively are observed with the MNB model. Thus, the proposed methodology can prove to be highly beneficial for prediction, specifically for manufacturing applications where the data is limited. 

Experimental investigations and prediction of WEDMed surface of Nitinol SMA using SinGAN and DenseNet deep learning model

The study of microstructures for the accurate control of material properties is of industrial relevance. Identification and characterization of microstructural properties by manual measurement are often slow, labour intensive, and have a lack of repeatability. In the present work, the intermetallic phase ratio and grain size in the microstructure of known Mg-Sn-Al alloys were measured by computer vision (CV) technology. New Mg (Magnesium) alloys with different alloying element contents were selected as the work materials. Mg alloys (Mg-Al-Sn) were produced using the hot-pressing powder metallurgy technique. The alloys were sintered at 620 °C under 50 MPa pressure in an argon gas atmosphere. Scanning electron microscopy (SEM) images were taken for all the fabricated alloys (three alloys: Mg-7Al-5Sn, Mg-8Al-5Sn, Mg-9Al-5Sn). From the SEM images, the grain size was counted manually and automatically with the application of CV technology. The obtained results were evaluated by correcting automated grain counting procedures with manual measurements. The accuracy of the automated counting technique for determining the grain count exceeded 92% compared to the manual counting procedure. In addition, ASTM (American Society for Testing and Materials) grain sizes were accurately calculated (approximately 99% accuracy) according to the determined grain counts in the SEM images. Hence, a successful approach was proposed by calculating the ASTM grain sizes of each alloy with respect to manual and automated counting methods. The intermetallic phases (Mg17Al12 and Mg2Sn) were also detected by theoretical calculations and automated measurements. The accuracy of automated measurements for Mg17Al12 and Mg2Sn intermetallic phases were over 95% and 97%, respectively. The proposed automatic image processing technique can be used as a tool to track and analyse the grain and intermetallic phases of the microstructure of other alloys such as AZ31 and AZ91 magnesium alloys, aluminium, titanium, and Co alloys.

Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement

Acoustic micromanipulation technologies are a set of versatile tools enabling unparalleled micromanipulation capabilities. Several characteristics put the acoustic micromanipulation technologies ahead of most of the other tweezing methods. For example, acoustic tweezers can be adapted as non-invasive platforms to handle single cells gently or as probes to stimulate or damage tissues. Besides, the nature of the interactions of acoustic waves with solids and liquids eliminates labeling requirements. Considering the importance of highly functional tools in biomedical research for empowering important discoveries, acoustic micromanipulation can be valuable for researchers in biology and medicine. Herein, we discuss the potential of acoustic micromanipulation technologies from technical and application points of view in biomedical research.

Potential of the acoustic micromanipulation technologies for biomedical research.

Precise control of fluid flows in microfluidic applications is crucial for various applications in lab-on-a-chip and point-of-care diagnostics. Standard bench-top equipment for providing this capability are syringe pumps. However, high cost of these systems limit their availability in low resourced laboratories. There are various open-sourced alternative syringe pump systems that can be fabricated and assembled using 3D printing, but they lack versatile control and flow rate characterization that are required for microfluidic applications. We report a simple and cost-effective approach to control an open-source multi-channel syringe pump. Simultaneous and adjustable flow control, and detailed characterization of the volume flow rates for different syringe volumes are also demonstrated.

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A Simple Approach for Controlling an Open-Source Syringe Pump

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Fatih Akkoyun

Papers

Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling

Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement

Potential of the acoustic micromanipulation technologies for biomedical research.

A Simple Approach for Controlling an Open-Source Syringe Pump

Rapid Characterization of Cell and Bacteria Counts using Computer Vision