Machining Techniques Research Articles

Twin-Tool Orientation Synchronous Smoothing Algorithm of Pinch Milling in Nine-Axis Machine Tools.

Pinch milling is a new technique for slender and long blade machining, which can simultaneously improve the machining quality and efficiency. However, two-cutter orientation planning is a major challenge due to the irregular blade surfaces and the structural constraints of nine-axis machine tools. In this paper, a method of twin-tool smoothing orientation determination is proposed for a thin-walled blade with pinch milling. Considering the processing status of the two cutters and workpiece, the feasible domain of the twin-tool axis vector and its characterization method are defined. At the same time, an evaluation algorithm of global and local optimization is proposed, and a smoothing algorithm is explored within the feasible domain along the two tool paths. Finally, a set of smoothly aligned tool orientations are generated, and the overall smoothness is nearly globally optimized. A preliminary simulation verification of the proposed algorithm is conducted on a turbine blade model and the planning tool orientation is found to be stable, smooth, and well formed, which avoids collision interference and ultimately improves the machining accuracy of the blade with difficult-to-machine materials.

An AI-Based Classification and Recommendation System for Digital Libraries

The immense volume of online content linked to digital libraries has given emergence to the advancement of screening and recommendation systems. A recommendation system is vitally important in both academic institutions and elibraries to assist professors, instructors, students, and researchers in finding appropriate sources of information. Distributed or collaborative screening is the most common method used in current recommendation systems. However, collaborative approaches cannot promote library repositories, including unrated or unpurchased electronic information. Thus, this paper deals with the automated classification and recommendation of a multiclass corpus found in virtual repositories (cloud databases). In various stages, Neuro-Fuzzy (NF) and Support Vector Machine (SVM) techniques are used as the base classifiers for the categorization of the essential subjects (contents). Later, a high-level ensemble learning strategy is utilized to recommend appropriate subjects from the available multiclass corpus. The methods use a CoC (Coherence of Content)-based inference mechanism to extract and filter the critical components before beginning the recommendation process. Experiments demonstrated that a recommended approach based on detailed conceptual descriptions instead of a handful of phrases/words might help academic and research communities to find relevant sources. Observing the results over a period of months shows that the suggested method increases user comfort, proving the system’s acceptability to users in this way. In addition, compared to previous models, the accuracy in categorizing therequisite subjects is more than 97.16 per cent.

Study and optimization of process parameters for deformation machining stretching mode

Monolithic thin-structure parts with intricate geometric designs are employed in a variety of aeronautical, medical, marine, and automotive applications, which include the moldlines of the fuselage, turbine blades, impellers, avionic shelves, irregular fins, prostheses, bone and joint support, and skull plates. The deformation machining process is the solution to this challenging and difficult-to-manufacture high-quality components with intricate narrow geometries at competitive prices. The aim of the present study is to assess the effect of process parameters of the deformation machining process wherein a thin, floor-like structure is created by milling and is then formed using a single-point incremental forming tool. Investigation involves the design and development of tooling required for the process followed by feasibility checking of the process. To examine the impact of different process parameters on the process response, the experiments were carried out using the design of experiments. The findings of this study indicate that different process parameters, including spindle speed, tool diameter, incremental step depth, and feed rate, have a substantial impact on the process response, like thickness, surface finish, and hardness. Uneven and non-uniform surface patterns during SEM indicate that it is needed to examine the impact of process parameters. This research involves the feasibility study of a new hybrid technique of deformation machining. Conventionally, a metallic structure is produced by joining various components through welding or by fastening. These methods require additional expenditure on equipment, storage, floor space, human resources, etc., with higher lead time. Joining increases weight and reduces fatigue strength. The creation of monolithic structures can eliminate all these disadvantages.

The Performance Analysis of Graph Neural Network (GNN) and Convolutional Neural Network (CNN) Algorithms for Cyberbullying Detection in Twitter Comments

Cyberbullying incidents have surged due to the expansion of social media network and advancements in internet technology, presenting a substantial challenge in online communities. Previous studies employing Support Vector Machine (SVM) techniques have exhibited promising outcomes, achieving a superior accuracy of 71.25%. However, recognizing the dynamic nature of cyberbullying behaviors and the necessity for more robust detection methodologies, this research explores cyberbullying detection on Twitter utilizing Convolutional Neural Network (CNN) and Graph Neural Network (GNN). The selection of CNN and GNN is motivated by the deficiencies observed in prior SVM-based approaches and the capacity of neural network to capture intricate patterns in textual and network data. The GNN consistently outperforms CNN in terms of F1 score, accuracy, precision, and recall. With only 20 epochs, GNN achieves an accuracy of 80.25%, surpassing CNN's 68.43%. Through GNN optimization, its accuracy reaches 89.04% after 100 epochs, underscoring its efficacy in Twitter cyberbullying detection.

Enhancing Ulos Batik Pattern Recognition through Machine Learning: A Study with KNN and SVM

This research aims to develop an automated classification system to accurately identify and classify Ulos batik patterns using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) techniques. The method is based on computer vision technology and texture analysis using the Gray-Level Co-occurrence Matrix (GLCM). The dataset consists of 1,800 images of Ulos fabric categorized into six main motif classes. The preprocessing process involves converting images to grayscale and extracting features with GLCM. Two classification algorithms, K-NN and SVM, were used for modeling, with evaluation using confusion matrix metrics and Area Under Curve (AUC). Evaluation results show that the K-NN model has an accuracy of 82%, while SVM has an accuracy of 57%. The analysis also highlights the superiority of K-NN in distinguishing Ulos fabric patterns. This research contributes to cultural preservation and the development of the creative industry by introducing an effective automated classification system for Ulos fabric patterns.

Research on machining Technology of Electrospark-electrochemical Hybrid Energy Field with tungsten hole

The machining technology for tungsten hole faces considerable challenges. Electrical Discharge Machining (EDM), as a non-traditional machining technique, is widely applied due to its capability to process conductive materials. This technique is characterized by high precision metal processing, high material removal rate, and low tool wear rate. Molecular dynamics simulation technology is employed in this study to model the single pulse discharge process, with the aim of elucidating the erosion mechanism of different melting point metal materials in the melt pool during spark discharge and the effect of pressure difference inside and outside the melt pool. Moreover, for the efficient machining of tungsten materials, Electrospark-electrochemical Hybrid Energy Field Machining (ECEM) method is proposed, which is conducted under a certain conductivity salt solution. The simultaneous high-speed electric spark discharge and electrochemical action effectively promote the rapid formation of spark plasma discharge channels. Orthogonal experimental data and images are utilized in this paper to study the machining of tungsten micropores, with the effects of pulse width, pulse interval, peak current, and concentration of NaNO3 being analyzed, and the optimal machining parameters being determined.

Non-Parametric Machine Learning Modeling of Tree-Caused Power Outage Risk to Overhead Distribution Powerlines

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM’s performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.

Effect of Process Parameters on the Roughness and Wetting Characteristics of SS304 Surfaces Using Electrolytic and Plasma Electrolytic Polishing Techniques

Metallic parts manufactured by conventional machining and additive manufacturing techniques possess poor surface quality and require post-processing operations before their end-use. Many methods exist for the post-processing of parts with free-form surfaces. In this study, two of the most used polishing techniques, electrolytic polishing (EP) and plasma electrolytic polishing (PeP), were investigated to understand the morphology of the resultant surface after both techniques. Pilot experiments suggested that an aqueous solution of ammonium sulphate is the most effective electrolyte for both the polishing techniques in terms of the lowest achievable value of average surface roughness ( Ra ). A systematic investigation of the electrolyte concentration, applied voltage, and polishing time revealed an identical dependence on all these process parameters: First, a decrease and then an increase in Ra. A minimum Ra of 0.63 μm and 0.49 μm were achieved with EP and PeP, respectively. Samples polished by PeP possess high gloss with negligible changes to the shape and form of the samples. Furthermore, static contact angle measurements with water revealed that surfaces treated with PeP showed higher hydrophilicity compared to those polished with EP. This can be attributed to the lower Ra value achieved with PeP.

Exploring the Impact of Antibiotics on Fever Recovery Time and Hospital Stays in Children with Viral Infections: Insights from Advanced Data Analysis.

Background: Antibiotic overuse in pediatric patients with upper respiratory tract infections (UR-TIs) raises concerns about antimicrobial resistance. This study examines the impact of antibiotics on hospital stay duration and fever resolution in pediatric patients diagnosed with viral infections via a multiplex polymerase chain reaction (PCR) respiratory panel. Methods: In the pediatric ward of Imam Abdulrahman Bin Faisal Hospital, a retrospective cohort analysis was conducted on pediatric patients with viral infections confirmed by nasopharyngeal aspirates from October 2016 to December 2021. Cohorts receiving antibiotics versus those not receiving them were balanced using the gradient boosting machine (GBM) technique for propensity score matching. Results: Among 238 patients, human rhinovirus/enterovirus (HRV/EV) was most common (44.5%), followed by respiratory syncytial virus (RSV) (18.1%). Co-infections occurred in 8.4% of cases. Antibiotic administration increased hospital length of stay (LOS) by an average of 2.19 days (p-value: 0.00). Diarrhea reduced LOS by 2.26 days, and higher albumin levels reduced LOS by 0.40 days. Fever and CRP levels had no significant effect on LOS. Time to recovery from fever showed no significant difference between antibiotic-free (Abx0) and antibiotic-received (Abx1) groups (p-value: 0.391), with a hazard ratio of 0.84 (CI: 0.57-1.2). Conclusions: Antibiotics did not expedite recovery but were associated with longer hospital stays in pediatric patients with acute viral respiratory infections. Clinicians should exercise caution in prescribing antibiotics to pediatric patients with confirmed viral infections, especially when non-critical.

Experimental investigations on coated copper wire and annealed copper wire electrodes in wire EDM machining through a comparative assessment employing the TOPSIS technique

Metals that are electrically conductive but are difficult to work with are typically machined using WEDM (Wire Electric Discharge Machining). The production of tiny components, which are challenging to mill using earlier non-traditional machining techniques, has undergone a substantial transformation thanks to these machines. Two wire electrodes coated copper wire and annealed copper wire electrodes are used for the testing in this investigation, and the analysis is completed utilizing the tool material. The workpiece material, D2 Steel, was cut into cubes that were 10 mm thick for the experiment, and a wire electrode with a diameter of 0.25 mm was used. Using an L27 orthogonal array, six input variables were employed at three different levels in the testing. The TOPSIS technique is utilized to optimize the output variables, leading to the determination of various output parameters, including Material removal rate (MRR), Kerf width (KW), tool wear rate (TWR), and surface roughness (SR). The chosen input variables include wire tension (WT), a gap voltage (GV), input current (IP), wire feed rate (WF), pulse off time (Toff), and pulse on time (Ton). The 10 mm-wide D2 steel was milled using two wire electrodes, and an ANOVA table was made to identify the factors that had the greatest impact on the WEDM parameters. The rank and ideal Wire EDM parameter values must be calculated using main effect plots and tables of replies. From this investigation, we can also draw the conclusion that for machining D2 steel, annealed wire electrodes provide greater MRR than coated wire.

A literature review: machine learning-based stem cell investigation.

Stem cell (SC) is a crucial factor of the human organ that is significantly important for clinical solutions. However, consideration of SC in the therapeutic or disease classification process is complex in terms of accurate classification and prediction. To overcome this issue, Machine learning (ML) is the most effective technique that is frequently used in cell-based clinical applications for diagnosis, treatment, and disease identification. Recently it has been implemented for SC observation which is a crucial factor for clinical solutions. Thus, the objective of this review work is to represent the effectiveness of ML techniques for SC observation from clinical perspectives with current challenges and future direction for further improvement. In this study, we conducted a short review of ML-based applications in SCs investigation and classification for the improvement of clinical solutions. We explored studies from five scientific databases (Web of Science, Google Scholar, Scopus, ScienceDirect, and PubMed) with several keywords related to the objective of our research study. After primary and secondary screening, 15 articles were utilized for this research study and summarized the observation results in terms of ten aspects (year of publication, focused area, objective, experimented datasets, selected ML classifiers, experimental procedure, classification parameter, overall performance in terms of accuracy, advancements, and limitations) with their current limitations and future improvement directions. The majority of the existing literature review works are limited to focusing on specific SC-based investigation, limited evaluation attributes, and lack of challenges and future improvement suggestions. Also, most of the review work didn't consider the investigation of the effectiveness of the ML technique in SC biology. Therefore, in this paper, we investigate existing literature related to the development of clinical solutions considering ML techniques, in the area of SC and cell culture processes and highlight current challenges and future directions. The majority of studies focused on the disease identification process and implemented the convolutional neural network and support vector machine techniques. The prime limitations of the investigated studies are related to the focused area, investigated SCs, the small number of experimental datasets, and validation techniques. None of the studies provided complete evidence to determine an optimal ML technique for SC to build classification or predictive models. Therefore, further concern is required to develop and improve the developed solutions including other ML techniques, large datasets, and advanced evaluation processes.

Minimization of residual stress, surface roughness and tool wear in Electro Discharge Machining of inconel 625

Electro Discharge Machining (EDM) is a well-known non-traditional machining technique which is widely used to make die castings and turbine blades out of difficult-to-cut materials like Inconel 625. The machined part's surface quality and dimensional accuracy are affected by the EDM electrode's wear. Moreover, surface roughness and residual stress augmentation of EDM-machined components can reduce the workpiece's lifespan by decreasing the fatigue life of machined parts. To enhance the precision and durability of EDM machined components, residual stress, tool wear, and surface roughness should be evaluated and minimized. The majority of published research works for the assessment and optimization of EDM machining parameters are based on experimental works which are limited to testing, workpiece materials, and machining conditions. In order to improve the quality of machined surfaces and reduce tool wear and residual stress during Inconel 625 EDM processes, a virtual machining approach has been developed in the research work. To predict the cutting temperature during EDM operations, the modified Johnson Cook model of Inconel alloys is used. The finite element approach is then used to calculate the generated residual stress during the EDM process. The proposed virtual machining approach is also applied in order to predict the surface roughness of EDM machined parts. To minimize the residual stress, tool wear, and surface roughness during EDM operations of Inconel 625, the machining parameters of gap voltage, peak current, pulse-off time, and pulse-on time are optimized using the Taguchi optimization approach. Experiments and simulations are then conducted to verify the developed virtual machining system in the study. So, using the optimal machining parameters, the residual stress, surface roughness of machined items and wear of EDM electrode are minimized by 24.5 %, 25.4 %, and 25.4 % respectively. Thus, the quality and reliability of components made using EDM processes may be enhanced by the suggested virtual machining system.

Optimizing main cutting force in high-pressure jet-assisted turning using Taguchi method

In high pressure turning, the optimization of cutting forces is crucial to improve the efficiency and precision of machining. In this study, the Taguchi method is used to systematically investigate and optimize the input parameters that influence the cutting forces. By using an orthogonal array and analyzing the signal-to-noise ratio, the main factors affecting the cutting force are identified and their optimal values are determined. The effectiveness of the optimization is confirmed by validation tests, which show a significant improvement in cutting performance. The results provide actionable insights for machining and lead to better decision-making and process control in high-pressure turning. This research not only highlights the benefits of the Taguchi method in process optimization, but also contributes to advancing machining techniques by minimizing cutting forces and improving overall process stability.

Cylinder accuracy analysis of tangential turning of disk-like workpieces with increased cutting speed

Tangential turning, a precision high-speed finishing procedure, is crucial for achieving superior surface quality and dimensional accuracy in cylindrical workpieces. This advanced machining technique leverages tangentially applied cutting forces to minimize tool deflection and vibration, thereby enhancing the surface integrity of the final product. Despite its advantages, tangential turning poses challenges in maintaining cylindrical accuracy, necessitating a thorough investigation of cylindrical error. Cylindrical error, the deviation of the machined surface from a perfect cylinder, significantly impacts the functional performance of precision components. Factors such as tool wear, machine dynamics, and thermal effects can induce these errors, demanding comprehensive studies to optimize process parameters and tool paths. By thoroughly analyzing cylindrical error, manufacturers can refine tangential turning processes, ensuring high precision and consistency in high-speed finishing operations. This research underscores the importance of precision error analysis in advancing manufacturing capabilities and achieving stringent quality standards. Cylindrical accuracy is analyzed using the full factorial experimental design methodology to carry out practical cutting experiments, where the cutting speed, feed, and depth of cut were the altered variables. The cylindricity error, peak maximum departure, their ratio, and coaxiality are measured and analyzed. The main effect analysis and detailed study are elaborated using the determined equation. It is found that decreasing the studied parameters is advisable if increasing cylinder accuracy is needed.

Comparative assessment of flexural strength of monolithic zirconia with different thicknesses and two sintering techniques.

This study aimed to compare the flexural strength of monolithic zirconia with different thicknesses and two sintering techniques. This in vitro, experimental study was conducted on 28 monolithic zirconia discs with 10 mm diameter and 0.5 (n = 14) and 1.2 mm (n = 14) thickness. Each group was divided into two subgroups (n = 7) for fast (60 min) and conventional (120 min) sintering at 1450°C. After sintering, the specimens were thermocycled and their flexural strength was measured by piston-on-3-balls technique in a universal testing machine (0.5 mm/min, 1.2 mm pin diameter). Data were analyzed by the Weibull test, one-way analysis of variance, and Tukey's test (α = .05). The flexural strength of specimens with 1.2 mm thickness was significantly higher than that of specimens with 0.5 mm thickness (p < .05). The flexural strength of 1.2 mm/120-min group was slightly, but not significantly, higher than that of 1.2 mm/60-min group (p > .05). The flexural strength of 0.5 mm/120-min group was slightly, but not significantly, higher than that of 0.5 mm/60-min group (p > .05). The increase in thickness of monolithic zirconia increases its flexural strength; however, increasing the sintering time appears to have no significant effect on the flexural strength of monolithic zirconia.

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Brain tumors being ninth in terms of prevalence and one of the most frequently diagnosed malignant tumors, negatively impact millions of individuals. Identifying and classifying tumors from MRI used for health monitoring poses a challenge for radiologists, yet early detection could significantly enhance the chances of effective treatment. Researchers in the field of explainable AI are currently focused on developing sophisticated techniques to classify and diagnose brain diseases. This study presents a novel framework that enhances the interpretability of our proposed system for brain tumor detection by utilizing explainable AI techniques. To enhance interpretability, we integrate the optimized recursive feature elimination selection technique with support vector machines. This method effectively eliminates redundant features, identifies the most important ones, and enhances the efficiency of detecting tasks. Following that, the optimal recursive feature elimination (ORFE) features are combined using the supervised support vector machine (SVM) technique. While EfficientNet-CNN is a very useful feature extraction framework that extracts the most important features from a transparent model, we reduced the overall computational complexity through feature elimination and supervised models, and the Figshre dataset clearly demonstrated the efficacy of our model. This study achieved very exceptional results and reduced computational complexity as compared to a single CNN model. The experimental results indicate that the proposed SVM-RFE based technique accurately detects brain tumors with a 99.51% accuracy and a specificity score of 99.63%. The proposed approach obtained an accuracy of 98.93% with a standard deviation of 0.032 using 10-fold cross-validation. Additionally, it produced an optimal ROC_AUC of 100% for cases including meningiomas and pituitary tumors.

Classification of Human Concentration Levels Based on Electroencephalography Signals

Concentration denotes the capability to direct one's attention to a specific subject matter. Presently, within the era characterized by an overwhelming abundance of information inundating human existence, distractions frequently impede human concentration, thereby influencing the depth of knowledge acquisition. Various elements contribute to the decline in human concentration, including diminished metabolic states, inadequate sleep, and engaging in multiple tasks simultaneously. The cognitive state of an individual during the process of thinking can be assessed through the analysis of electroencephalography signals. The primary objective of this investigation is to facilitate experts' interpretation of electroencephalography signal outcomes for categorizing concentration levels. The dataset utilized in this examination comprises unprocessed EEG data obtained from observing individuals in both relaxation and concentration states. After data preprocessing, feature extraction is executed, and classification is performed using the Support Vector Machine technique. The outcome of this study reveals an accuracy rate of 84%. These developments allow for continual monitoring of brain function, an enhanced comprehension of cerebral activities, and increased operational efficacy of end-effectors. The implications of these advancements on prospective research opportunities are evident in the potential for more accurate diagnosis of neurological disorders and the progression of sophisticated BCI applications designed to support healthcare and monitor cognitive states. The evolution of EEG technology is paving the way for novel research pathways in neuroscience and human-computer interaction.

Elevating Sustainability: The Role of Machining in Modern Eco-Friendly Manufacturing Processes

Sustainable manufacturing has become a critical concern across various industries, including aerospace, automotive, and services. It involves the creation of manufactured products through economically sound processes that minimize negative environmental impacts while conserving energy and natural resources. Additionally, sustainable manufacturing aims to enhance employee, community, and product safety.This paper focuses on the machining process as a key component of sustainable manufacturing. Machining is widely used in manufacturing industries to fabricate components, but it also significantly contributes to environmental pollution. Sustainable machining has been a topic of discussion for over a decade. In sustainable machining processes, every factor acts differently like, the tool life, productivity, and effective utilization of resources must be increased. In contrast, the machining cost, machine cutting power, and adverse effect of cooling and lubrication fluids will be decreased. Important aspects of sustainable machining include dry and near-dry machining, cryogenic machining, and minimum quantity lubricant (MQL) were presented in this article. The effectiveness of these processes is assessed based on cutting tool life and the quality of machined components. In addition, the advancements consideration such as novel process variations, process hybridization, the use of sustainable tools and green transfer mediums, and the optimization of process parameters. Recommendations include increasing efficiency and reducing waste by incorporating recyclable tools in the machine industry implementing either stand alone or hybrid sustainable lubrication machining techniques.

Non-interference slow tool servo turning method for complex surfaces with large undulation changes

Complex curved surface parts are integral to the mechanical industry, demanding precise machining techniques. Multi-axis slow tool servo turning has emerged as an efficient method due to its high precision and quality. However, the challenge of severe tool flank interference arises, especially on surfaces with large undulation changes. Conventional methods, such as increasing tool clearance angles or adding extra tool axes, detrimentally affect machining precision and stability. To address this, a non-interference toolpath planning method is proposed for complex surfaces with large undulation changes. The approach begins with establishing geometric models of the single-point diamond turning tool and typical complex surfaces, followed by analyzing the mechanism and evolution of tool flank interference. By considering geometric parameters and vector relationships, the practical tool clearance angles are calculated. And then a criterion for subregion division is introduced and the non-cutting stroke is designed, accounting for the maximal acceleration of machine tool axis. Multi-pass toolpaths are generated to navigate different subregions while avoiding interference by transforming spindle rotation direction. Furthermore, a method is proposed to optimize toolpath stitching in practical machining regions, ensuring continuous and smooth transitions while maintaining machining precision and surface quality. Experimental validation, conducted on a hyperbolic paraboloid surface with large undulation changes, demonstrates a significant reduction in surface roughness (Sa) from 380.503 nm to 75.664 nm—a reduction of 80.115 %. The proposed method not only enhances machining precision and surface quality but also extends tool life and improves working conditions. This research offers vital guidance for non-interference toolpath planning in engineering practice.

High-efficiency single-layer corporate-feed gap waveguide based circularly polarized antenna array with compact size

A high-efficiency, single-layer gap waveguide based circularly polarized (CP) metasurface (MTS) antenna array has been proposed at Ka-band, employing a combination of printed circuit board (PCB) and machining techniques. The coupling slot parallel to the feeding ridge is proposed, which can be beneficial for designing single-layer ridge gap waveguide (RGW) networks, avoiding the traditional cavity layer and reducing the transmission loss. The MTS elements in a 4 × 4 lattice of truncated-corner patches are etched on a single substrate layer for lightweight and low-cost, validating the concept of a compact mixed design that can be easily adapted to other printed CP antennas. A 4 × 4 MTS array prototype was fabricated and tested. The impedance bandwidth and axial ratio bandwidth are 26–31.3 GHz (18.5%) and 28.2–33 GHz (15.7%), respectively. Over the entire operating bandwidth of 28.2–31.3 GHz (10.4%), antenna radiation efficiency exceeds 82%, and the maximum realized gain at 29.2 GHz is 21.2 dBi. The designed broadband MTS antenna array is efficient, compact, and suitable for certain millimeter-wave communication applications.