Machine Type Research Articles

Positioning errors and accuracy of CNC machine tools

Abstract Computerized numerically controlled (CNC) machine tools are a solid foundation for modern industrial manufacturing. These machines provide linear and angular displacements in three or five axes. The accuracy of moving and positioning the tooltip relative to the work part's surface determines the final product's accuracy. Errors due to machine positioning can be linear or angular or both. This work studies the positioning errors of a vertical turning center CNC machine type. This machine has three axes, two linear X (horizontal) and Z (vertical), and one rotation axis “C”. The travel of the spindle carrying the cutting tool along the X-axis is 1400mm, and the travel of the carriage holding the spindle along the Z-axis is 1300mm divided into six latches. The machine rotary table which carries the work part can rotate 360°. A highly precise laser interferometer system determines the linear positioning errors in both the X and Z axes. The angular positioning errors for the “C” rotation axis are determined accurately by an autocollimator system. The positioning error measurements are carried out based on ISO 230-2 as a measurement standard of positioning deviation, accuracy, and repeatability. The collected error measurements in linear displacements and angular rotation are analyzed, interpreted, and compared with manufacturer specifications and ISO 13041-4 allowable tolerances.

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Batch-sizing and machinability data systems for milling operations: An optimal sustainable cost of quality approach

Nowadays, manufacturers make every effort to achieve a higher quality of their products at an attractive cost. With all the introduced legislation and incentives in the developed world to address global warming, machining shops in the West also strive to cut greenhouse emissions. This article offers an optimal approach to the micro Computer-Aided Process Planning (CAPP) problem to optimize the internal quality cost and buffering effect while keeping the environmental impact low. To optimize the machining parameters, the mathematical model is developed for different milling operations, face, side, and peripheral. cutting speed, feed rate, axial depth of cut, radial depth of cut, nose radius, and batch sizing while maximizing profit and meeting customer demand. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated and solved using Classical Constrained Nonlinear Optimization (CCNO) and Genetic Algorithms (GAs). Surface roughness, used as a metric to evaluate the desired quality level of a finished machined part type, is modeled as a Gaussian random variable to model the surface roughness of the machined part utilizing a cumulative normal distribution. The ratio of rework and scrap is calculated in terms of the surface roughness of the machined part shifting away from the target and exceeding upper and lower specification limits. The internal failure cost model, addressing both scrap and rework, is developed based on Taguchi’s quadratic loss function. CCNO is employed to validate the results obtained by GAs, relaxing the lot-sizing integrality constraint and, thus, the convexity of the produced relaxed model. An iterative method employing a developed multi-regression model is used to solve for the expended power consumption (an inherent highly nonlinear environmental criterion of the developed model) within both GAs and CCNO. This study reveals that the machining parameters substantially impact the cost components of the objective function as well as the scrap and rework quantities. A stringent quality cost target can force the model to optimize the feed rate and nose radius to minimize the internal failure quality cost while improving the environmental impact, including direct and indirect power consumption and CO2 emissions considerations.

Assessment of Conventional X-ray Machine Resolution, Using Designed Phantom

The diagnostics x-ray machine produces electromagnetic radiation used to image the body in case of pathology; therefore, this exposure was justified. But if the exposure parameters were not chosen satisfactorily, imaging has to be repeated where the patient was subjected to a substantial amount of radiation. To determine the spatial resolution in order to find the optimum kV relative to machine type, the phantom is placed on the detector surface, and a uniform source of radiation is placed above the bar phantom, where the focus-to-film distance was 1 m. An image is acquired; the unit was set at 2 mAs and 40 kVp value. In this study, 3 conventional x-ray machines were investigated using point phantom designed by the researcher, which consisted of a variable diameter used to test variable exposure factors. The results showed that the best machine resolution was Toshiba model E7239X (2016) for Modern Medical Center that had a high resolution of 96% at 45 kV for a point of thickness of 1.4 mm.

Design of Prototype Phantom to Measure Quality Control for Conventional X-ray Machine Using Quantitative Image Parameters

A variety of phantoms that measure image quality parameters are commercially expensive and often complicated to use as they measure only one parameter of a particular x-ray machine, which leads to the difficulty of measuring the quality of x-ray image and thus increase the risk of radiation to the patient and society. The main objective of this study was to design a prototype phantom to measure quality control for conventional x-ray machine using quantitative image parameters. In this study, 5 conventional x-ray machines were investigated using wire phantom designed by the researcher, which consisted of variable thicknesses used to test variable exposure factors. This study introduced a more reliable method of measuring the resolution, which is modulation transfer function, which gives a complete description of the resolution instead of using full width at half maximum or the visibility method, which is more qualitative where modulation transfer function is a real quantitative method, by designing a prototype phantom that consisted of 5 wires with different thicknesses and kVp for 5 x-ray units and assessing the diagnostic x-ray machine resolution by the optimum kV found relative to machine type. This study showed that the one with the best machine resolution was Shimadzu (2011) for Khartoum Emergency Hospital, which had a high resolution of 97% at 46 kV for thickness of 1.4 mm compared with Toshiba 2011 for Almotkamil Hospital, which had 92% at 46 kVp. Also, the result showed that for the 2 types of x-ray machines the x-ray tubes do not produce the same exposure, and the output decreased with age of the x-ray unit; also, the resolution reduced when the thickness of the wires decreased.

An Improved Phase Noise Reduction in Millimeter-Wave FBMC Systems

The next generation mobile network needs to have a spectrum above the 30 GHz radio band. The 5G system is a promising technology for future communication systems. It has been used in Device-to-Device (D2D) communications, IoT, Machine type communications (MTC), and wireless backhaul. The 5G is expected to have speeds of hundreds of times and satisfy the requirements of larger traffic density, low latency, connective density, higher capacity & reliability, data rate, super high mobility, etc. It is expected to have applications such as HD video, virtual reality & augmented reality, online games, and cloud desktops. For this, Millimeter wave (Mmwave) has been used for fifth-generation mobile networks. The millimeter wave has a frequency range from 30 to 300 GHz. When the usage of frequency is above 60 GHz, the band will acquire RF impairments such as PAPR, Phase noise, non-linearity, and phase and quadrature timing mismatch (IQTM). This article shows the Phase noise reduction in MIMO FBMC systems using Extended Kalman filter techniques.

Dynamic priority-based bandwidth allocation scheme for machine type communications

The burgeoning realm of Machine Type Communications (MTC) has ushered in a paradigm shift in wireless communication, necessitating innovative strategies to efficiently manage diverse application requirements. This paper presents a pioneering Dynamic Priority-based Bandwidth Allocation (DPBA) scheme tailored specifically for MTC scenarios, encompassing both MTC and Human Type Communications (HTC) coexistence. DPBA employs an adaptive framework addressing dynamic MTC traffic, optimizing resource use while meeting latency constraints and sporadic data patterns. By dynamically prioritizing MTC applications based on requirements, the scheme allocates bandwidth for reliable data delivery. DPBA extends to MTC and HTC coexistence, managing diverse quality demands. This underlines its versatility and capacity to serve distinct needs in a shared spectrum. To substantiate its performance, the DPBA scheme is rigorously evaluated against a spectrum of related allocation methods like Proportional Fairness (PF), Static Priority Scheduling (SPS), and the Machine Learning-based Scheme (MLS), DPBA excels in simulations, minimizing packet loss, latency, enhancing throughput and ensuring fairness, surpassing benchmarks. This research underscores the critical importance of tailored bandwidth allocation strategies in advancing MTC and HTC coexistent environments. With its adaptability, efficiency, and remarkable performance, the DPBA scheme emerges as a cornerstone in the trajectory of wireless communication networks, catering to the distinct requirements of both MTC and HTC applications.

Quantum structure-property relationship (QuaSPR) assay of the molecular machine for rotor-axon congeners in a rotaxanic complexes

The present article aims, as far as is known for the first time, to investigate and indeed to causally justify the cycle of a molecular machine, in the present case for rotaxanes formed by the combination of axons and wheel, based on the wave functions associated with various stages in the evolution of molecules involved in the cycle of molecular machines. The purpose of the article is to investigate the relationship between the wave functions Ψ1, Ψ2, and Ψ3, associated with the no-complex, pre-complex, and complex stages, respectively, for a molecular machine type evolution composed of the molecular structure DB24C8 and the set of axons numbered 1+–10+, with ionic charge.

Comparative Analysis of the use of Band Heaters and Infrared Heaters to Save Energy on Injection Molding Machines Type Borche Bi 320T

This study aims to compare the energy efficiency between the use of a heater band and an infrared heater on an injection molding machine type borche bi 320T. The injection molding machine is one of the main pieces of equipment in the manufacturing process of plastic products and plays an important role in shaping various consumer and industrial products. One of the critical aspects of the operation of injection molding machines is the significant energy consumption, especially related to heating. This study was conducted by measuring the energy consumption of both types of heaters under the same operational conditions. The results showed that the energy consumption of using a band heater was 46.9 kW, while the infrared heater was 38.9 kW, significantly more energy efficient by 17.1% compared to the band heater. Although the initial installation cost of the infrared heater is 87% higher than that of the band heater, the energy savings generated from the infrared heater can reduce operating costs by approximately $39 million per year. Higher energy efficiency means lower electricity costs and, thus, more efficient total production costs. The use of infrared heaters also has a positive impact on the environment. Reduced energy consumption means reduced greenhouse gas emissions from power generation. Therefore, besides the economic benefits, the use of infrared heaters is also in line with sustainability and environmentally friendly practices.

Surface Roughness Evaluation of AZ31B Magnesium Alloy After Rough Milling Using Tools with Different Geometries

This paper presents the experimental results of a study investigating the impact of machining parameters on 3D surface roughness after rough, dry milling. The following 3D roughness parameters were analysed: Sa (arithmetic mean height), Sq (root mean square height), Sz (maximum height), Sku (kurtosis), Ssk (skewness), Sp (maximum peak height), and Sv (maximum pit height). Roughness measurements were made on the end face of the specimens. Additionally, 3D surface topography maps and Abbot-Firestone material ratio curves were generated. Carbide end mills with variable rake and helix angles were used in the study. Experiments were conducted on AZ31B magnesium alloy specimens using a contact-type profilometer. The machining process was conducted using the parameters of so-called high-speed machining. Three variable technological parameters were analysed: cutting speed vc, feed per tooth fz, and axial depth of cut ap. The results showed that the surface roughness of the rough-milled specimens depended to a great extent on the tool geometry and applied machining parameters. Feed per tooth was found to have the greatest impact on surface roughness parameters. Lower values of the analysed surface roughness parameters (and therefore higher surface quality) were obtained (in most cases) for the tools with a rake angle γ of 5° and a helix angle λs of 50°. The results provided both theoretical and practical knowledge about the achievable surface roughness after rough milling using tools with different tool blade geometry. It was shown that rough milling is an effective and efficient type of machining for the AZ31B alloy.

Assessing mowing intensity: A new index incorporating frequency, type of machinery, and technique

AbstractBackgroundOnly a few decades ago, colorful, small‐scale, heterogeneous, and species‐rich hay meadows or extensive pastures were common, but have often been replaced by species‐poor, uniform, large‐scale multicut meadows. Technological advancements and improved efficiency in grassland management have come at the cost of biodiversity.MethodsIn Germany, 150 grassland plots have been investigated since 2006. Using these extensive data, we propose a new compound index for estimating the site‐specific mowing intensity in order to facilitate assessment of the impact of mowing intensity on biodiversity and ecosystem processes. Our index integrates the various qualitative components of mowing machine type, mowing height and use of a conditioner, with the annual number of cuts.ResultsThe newly proposed index achieves a much finer gradation of mowing intensity compared to the previous quantification based on the number of cuts only. Furthermore, a decrease in plant and arthropod species was observed at higher mowing intensity.ConclusionsThe proposed mowing intensity index offers enhanced precision in calculations and can easily be integrated into assessments of land‐use intensity in grasslands. Further, it could serve as a basis for providing subsidies to farmers, who adopt low‐impact mowing practices.

Exploratory factor analysis for machining error data of compressor blades based on dimensionality-reduced statistical analysis method

Abstract Various machining errors inevitably occur on aero-engine compressor blades, including leading-edge contour error, trailing-edge contour error, camber contour error, and more. The current complexity surrounding the numerous ma-chining error types and their obscure interrelationships imposes immense effort for aerodynamic analysis and hin-ders overall error control. Thus, elucidating error correlations to achieve error dimensionality reduction is imperative. This study pioneers a dimensionality reduction approach via factor analysis to conduct a comprehensive statistical analysis of 13 types of blade machining errors. The proposed technique can categorize the 13 errors into three groups, each dominated by a distinct common factor. To validate the accuracy of the extracted factors, the factor scores of the three identified latent factors are computed. Cluster analysis is then performed on the factor scores, and the clustering results are visualized by t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, boot-strap resampling establishes the 95% confidence intervals for the factor scores. Capitalizing on the grouping struc-ture uncovered by factor analysis, multiple linear regression models are built for the errors within each group, and then, a preliminary conjecture is made about the potential key error types for each group of errors based on the re-gression coefficients. This hypothesis is then evidenced by the statistical analysis of cross-section profile error data of 28 blades. The present work can not only optimize machining processes but also relax tolerance requirements and diminish the effort of aerodynamic analysis.

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

SummaryTraditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra‐reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single‐carrier‐non‐orthogonal multiple access (SC‐NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time‐frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC‐NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC‐NOMA, this paper introduces a new method called user‐paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near‐far/far‐near (NF‐FN) and near‐near/far‐far (NN‐FF). NF‐FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN‐FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade‐offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC‐NOMA balances performance but experiences interference. NN‐FF prioritizes eMBB and offers best latency, while NF‐FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

Surface integrity characterization of third-generation nickel-based single crystal blade tenons after ultrasonic vibration-assisted grinding

Machined surface integrity of workpieces in harsh environments has a remarkable influence on their performance. However, the complexity of the new type of machining hinders a comprehensive understanding of machined surface integrity and its formation mechanism, thereby limiting the study of component performance. With increasing demands for high-quality machined workpieces in aerospace industry applications, researchers from academia and industry are increasingly focusing on post-machining surface characterization. The profile grinding test was conducted on a novel single-crystal superalloy to simulate the formation of blade tenons, and the obtained tenons were characterized for surface integrity elements under various operating conditions. Results revealed that ultrasonic vibration-assisted grinding (UVAG) led to multiple superpositions of abrasive grain trajectories, causing reduced surface roughness (an average reduction of approximately 29.6%) compared with conventional grinding. After examining the subsurface layer of UVAG using transmission electron microscopy, the results revealed that the single-crystal tenon grinding subsurface layer exhibited a gradient evolution from the near-surface to the substrate. This evolution was characterized by an equiaxed nanocrystalline layer measuring 0.34 μm, followed by a sub-microcrystalline grain-forming zone spanning 0.6 μm and finally, a constituent phase-twisted distorted deformation zone over 0.62 μm. Under normal grinding conditions, the tenon exhibited low surface hardening (not exceeding 15%), and residual compressive stresses were observed on its surface. In cases where grinding burns occurred, a white layer appeared on the tenon’s surface, which demonstrated varying thicknesses along the teeth from top to root due to thermal-force-structural coupling effects. Additionally, these burns introduced residual tensile stresses on the tenon’s surface, potentially substantially affecting its fatigue life. This paper enhances our understanding of UVAG processes and establishes a foundation for their application in manufacturing single-crystal turbine blades for next-generation aero-turbine engines.

Q-Learning Based Load Balancing in Heterogeneous Networks with Human and Machine Type Communication Co-existence

Q-Learning Based Load Balancing in Heterogeneous Networks with Human and Machine Type Communication Co-existence

Non-destructive on-machine inspection of machining-induced deformed layers

Complete inspection of workpiece surface integrity invariably involves a form of destructive testing to enable the assessment of microstructural defects such as machining-induced white layers and near-surface plastic deformation. The incumbent offline and destructive microscopy inspection process is incompatible with both a digital and sustainable manufacturing vision of zero waste, as such, a non-destructive technique which utilises a novel X-ray diffraction surface integrity inspection method (XRD-SIIM) has been developed. This approach has been designed to complement traditional machinability-type assessments of tool life and machined surface topography, establishing a new process flow for validation. In this paper, for the first time, non-destructive on-machine validation of workpiece microstructural surface integrity is demonstrated, via a comparative investigation into the effect of insert grade, cutting speed and coolant delivery method on the depth of the imparted plastic deformation depth. It is shown that XRD-SIIM allows repeatable, non-destructive determination of deformed layers within a typical machining centre enclosure, with comparable findings to the incumbent cross-sectional microscopy approach. The generation of surface integrity digital fingerprints of a machining operation facilitates rapid comparison between testing variables, with a transition to an objective quantifiable assessment rather than one which open to subjectivity. In turn, XRD-SIIM expedites the development and benchmarking of new operations, tooling, materials, or coolant.

On straightness measurements of large CNC machine tools

The CNC (computerized numerically controlled) machines are widespread in use due to their high capability of precise manufacturing in industrial production. They have a wide range of designs depending on the working capacity in manufacturing. The associated form errors in large-capacity CNC machines during production shall be identified and corrected or eliminated. This study presents an investigation of one of the main form errors that may affect the manufacturing precision of these machines. This error type is a straightness error with both two kinds of horizontal and vertical errors. The study is carried out for a vertical turning center CNC machine type. The straightness errors are measured for the X axis at different latches in the Z direction and for the Z axis at three positions in the X direction with multi-displacement steps. Different algorithms are used in the determination of straightness errors. The X-axis has minimum horizontal straightness errors at latch Nr. 3 and minimum vertical straightness errors at latch Nr. 5. For the Z axis, the minimum values for horizontal and vertical straightness errors exist when the spindle is located 1200 mm away from the machining center to the right. The displacement steps have a significant impact on the determination of straightness errors.

43-PUB: A Bibliometric Analysis of Machine Learning and Type 2 Diabetes from 2010 to 2023

43-PUB: A Bibliometric Analysis of Machine Learning and Type 2 Diabetes from 2010 to 2023

Mechanised Removal of Cocoa Beans from the Pod and Strategies to Optimize the Technique: A Review

Confronted with problems associated with removing cocoa beans, methods, technologies and equipment have been developed and applied over the years. This paper comprehensively reviews the most effective method and technology to split cocoa pods and remove the beans. The working principle of the technique was explained with the cocoa pod opening mechanism, structures applicability and cost of the working method. The forces involved in opening cocoa pods are shearing, compressive, and impact depending on the machine type and the process used. The techniques applied in opening cocoa pods are grouped into traditional (manual) and improved (mechanised). The manual method of opening the cocoa pod is time-consuming and prone to accidents leading to injury to beans and farmers. The strength of the labour force available during harvest also affects the manual method. When cocoa beans are damaged, they deteriorate and are not appropriate for fermentation. The mechanised splitting has a high initial investment but very fast and reduced losses. Information gathered on cocoa pod mechanization shows that despite the countless efforts in developing various opening mechanisms. a high bean damage ratio and separation problems are still yet to be controlled. The current trends and techniques in cocoa pod opening mechanisms are also presented.

Chatter Stability of Orthogonal Turn-Milling Process in Frequency and Discrete-Time Domains

Abstract As the industry seeks better quality and efficiency, multitasking machine tools are becoming increasingly popular owing to their ability to create complex parts in one setup. Turn-milling, a type of multi-axis machining, combines milling and turning processes to remove material through simultaneous rotations of the cutter and workpiece with the translational feed of the tool. While turn-milling can be advantageous for large parts made of hard-to-cut materials, it also offers challenges in terms of surface form errors and process stability. Because tool eccentricity and workpiece rotation lead to more complexity in process mechanics and dynamics, traditional milling stability models cannot predict the stability of turn-milling processes. This study presents a mathematical model based on process mechanics and dynamics by incorporating the unique characteristics of the orthogonal turn-milling process to avoid self-excited chatter vibrations. A novel approach was employed to model time-varying delays considering the simultaneous rotation of the tool and workpiece. Stability analysis of the system was performed in both the discrete-time and frequency domains. The effects of eccentricity and workpiece speed on stability diagrams were demonstrated and validated through experiments. The results show that the tool eccentricity and workpiece speed alter the engagement geometry and delay in the regeneration mechanism, respectively, leading to significant stability diagram alterations. The proposed approach offers a comprehensive framework for the stability of orthogonal turn-milling and guidance for the selection of process conditions to achieve stable cuts with enhanced productivity.