Machining Properties Research Articles

An Exploratory Study of the Surface Integrity of Various FRP Composites in Face Milling Operation

The use of glass, carbon and hybrid /glass/carbon epoxy polymer composites is widely used in the automotive, manufacturing and aerospace sectors. Milling is a secondary production process in which the final shape of the product is produced with the desired shape and high dimensional accuracy. The objective of this study is to enhance our knowledge of the machinability properties of composite materials in the secondary manufacturing industry. In this research study, performing face milling operations with different tools on bidirectional (BD) glass, unidirectional (UD) glass, carbon/epoxy, and glass/carbon/epoxy (hybrid epoxy) polymer composites. This experimental work derives conclusions that contribute to the improvement of the milling process for better surface quality. Taguchi’s L16 Design of Experiments (DOE) and Analysis of Variance (ANOVA) are used to determine the effect of tool type, speed of spindle, rate of feed, cut depth on surface roughness and delamination factor. And it was concluded that glass/carbon hybrid epoxy polymer laminates showed improved surface quality when milled with a Poly Crystalline Diamond (PCD) tool at parametric combinations of rate of feed is 300 mm/min, speed of spindle is 1000 rpm, and cutting depth is 0.5 mm. In addition, the experimental results of the scanning electron microscope examination were also verified. And found excellent machined surface quality and least damage with the above optimal process parameter combinations.

Mechanical, machinability and water absorption properties of novel kenaf fiber, glass fiber and graphene composites reinforced with epoxy

This paper aims to fabricate a novel kenaf fiber, glass fiber, graphene composite reinforced with epoxy, and to study its water absorption, mechanical and machining properties. Natural fiber composites seek numerous applications nowadays due to its high strength is to weight ratio, high impact resistance, thermal stability, and recyclability. The literature study on natural fiber composites indicates lack of research on Kenaf fibers reinforced with E-glass fibers in conjunction with 0.5% and 1.0% of graphene. Hence, this research focuses on Kenaf fiber with percentage composition of graphene was varied as 0.5%, and 1.0%, by weight of the holding matrix and with/without incorporation of glass fiber. Suitable surface modifications were done by treating natural fibres by 0.5% NaOH for better adhesion of fibre and epoxy resin. Sonication and Cetyl trimethyl ammonium bromide (CTAB) treatments were also done to realize the fine scattering of graphene in the epoxy matrix in order to achieve better mechanical behaviour. Moulds were prepared as per D-638 ASTM standards. The treated fibres were then arranged in the mould by the conventional hand layup technique. The main objectives of this study are to conduct tensile, flexural, Impact, water absorption, machinability, and FTIR test. Result shows that the incorporation of graphene in natural fiber composites showed an increase in tensile strength, flexural strength, and water absorption properties by 27.7%, 53%, and 15% on average, respectively. The composites used in this research find their use in potential applications such as defence, biomedical aids, automobiles, packaging, actuators, etc. These often generate great interest, whenever there is a demand for fabricating lightweight and high strength material.

Optimum selection of nature-inspired texture pattern for cutting tool surface using an integrated multi-criteria decision-making approach: a comparative analysis

Purpose This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin textures of different snakes scrolling on highly rough and projected surface conditions to analyze suitability of texture based on the texture geometry and machining conditions. The work also aims to propose a texture pattern selection process to incorporate on cutting tool tribological surface. Design/methodology/approach The selection of alternative nature-inspired texture patterns based on the texture pattern geometry and machining properties leads to a multi-criteria decision-making problem. Thirteen criteria are considered for selecting an appropriate texture pattern among 14 alternatives, i.e. nature-inspired texture patterns. In the present work, an integrated analytical hierarchy process (AHP)-TOPSIS, AHP-multi-objective optimization on the basis of ratio analysis (MOORA) and AHP-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) approaches have been proposed for the selection of an appropriate nature-inspired texture pattern. AHP is used for the formulation of decision-making matrix and criteria weight calculations and ranking of alternatives is done by three methods. Spearman’s correlation compared and found positive relations between rank assigned by methods. Experimental validation is done in Lathe for selected texture effects. Findings The texture parameters C-1 (Width of texture) and C-2 (Depth of texture) are found significant, while T-2 (Blended Krait) and T-6 (Banded Racer-1) texture is found optimal to generate on cutting tool surface. Research limitations/implications Only some nature-inspired texture patterns have been recognized before the selection; an infinite number of textures are available in nature. The size of the texture pattern is difficult to identify by the selection process because each texture pattern may have different effects on tribological surfaces. Practical implications The proposed selection methodology of nature-inspired texture patterns will help identify optimal texture geometry for specific tribological applications. The nature-inspired texture patterned tool has a significant impact on the cutting force and temperature due to its tribological effect on the cutting tool surface; it decreases the power required for machining. The machining characteristics like roughness are found to decrease by using nature-inspired texture patterned tools. Social implications Various nature-inspire texture studies to generate specific effects on the tribological surfaces may be started study for the surface of aircraft, ships, bearings, etc. Small and big fabrication industries may benefit by decreasing the cost of machining using nature-inspired texture-patterned tools. Research society will pay attention to nature’s inspiration. Originality/value Novel snake-skin-inspired texture patterns are recognized and hybrid MCDM methods are proposed to select optimal texture pattern. Proposed method used single time normalization to effectively rank the alternatives. The insights gained from this research can be extrapolated to address similar challenges in selecting nature-inspired textures for various applications. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0163/

Shape design optimization of a flat endmill tool

AbstractAmong the many possible metal‐cutting processes, milling is regarded as one of the most widely applied processes to machine different engineering materials productively. During the milling process, there is relative motion between the endmill tool and the workpiece. Energy is required to drive the cutting force used by the tool in separating the chips from the workpiece. Several studies have focused on the effects of milling process parameters on the cutting force, for instance by varying the cutting parameters, machining properties and the extent of cooling/lubrication. Other studies have explored the influence of tool geometry and material properties on the cutting force. Results from these studies have brought about improvements in the design of milling machinery and tools. The current study presents a novel automated approach for tool geometry optimization which is fully coded in Matlab. An automated and parametrized endmill design procedure was created featuring input parameters such as rake angle, relief angle, clearance angle and helix angle, all of which have been shown, through numerous studies, to influence cutting forces. The parameters were here used to generate the shape profile for one cutting edge of a flat endmill. This cutting edge profile is next copied and rotated, as per the design's pitch angle, to form the complete set of cutting flutes for the flat endmill. Next, the surface defined by the completed profile is meshed using quadrilateral elements. The 3D endmill design is then formed by extruding, and twisting as per the helix angle, this quadrilateral mesh to form a solid hexahedral mesh. Finally, an automated iterative optimization process was defined, featuring the above parametrized design process coupled with Abaqus‐based finite element simulation of the milling process. The expected result is that the proposed optimization procedure will be able to identify the endmill design parameters which minimize the cutting force. At the completion of the optimization, a minimized maximum resultant cutting force value is obtained from using the newly optimized endmill. The resultant cutting force was reduced by 17.6%. It is anticipated that the automated design, meshing and simulation‐driven optimization approach is generalizable to other tool design variations.

Numerical Investigation of the Impact of Cracks and Stiffness Loss in the Supporting System for the Dynamic Characteristics of a Rotating Machine.

In the literature on rotating machinery, many articles discuss the analysis of various rotor and bearing defects, including both sliding and rolling bearings. Defects in the rotor supporting system are investigated much less frequently. In rotor-bearing-supporting structure systems, where there are couplings between the individual sub-systems, damage to the supporting structure can significantly impact the dynamic properties of the entire machine. The authors of this article have, therefore, focused on analysing the defects that can occur in the supporting system of the rotor and bearings. This article presents the results of a numerical analysis of two common defects in the supporting structure: cracks in the bolted joints attaching the machine body to the foundation and a decrease in foundation stiffness. The research object was a test rig that accurately reproduced the dynamic phenomena occurring in rotating machinery, such as vapour and gas turbines. In the numerical model of the rotating machine, a three-dimensional linear model of the supporting structure was combined with a beam model of the rotor line via a nonlinear fluid film-bearing model. The developed model allowed for the analysis of two different failures in the supporting system over a wide range of rotational speeds. The calculations showed that damage to the supporting structure can significantly impact the dynamic characteristics of the entire rotating machine.

The influences of alloying elements and processing conditions on the machinability of wrought aluminium alloys: A literature review

Today, wrought aluminium alloys are ranked among the most important materials with low specific density, strength features, corrosion resistance, machinability and recyclability properties in numerous application fields. Machining process of these alloys involves a number of considerations to ensure the highest quality and productivity. Key machinability issues include the rate of tool wear, workpiece surface finish and integrity and machining process sustainability. Understanding and optimising these parameters can lead to improving the machinability, better surface finish, longer tool life, and overall, more efficient and cost-effective machining processes for wrought aluminium alloys. The machining of wrought aluminium alloys remains a challenging task due to their unique characteristics, such as high strength, thermal conductivity and refined grain structures with mechanical and/or heat treatment. Although considerable progress has been made in the development of cutting tool materials and machining techniques for wrought aluminium alloys, further research is required to improve the machinability of these materials and reduce the associated costs and time needed for production. Having access to relevant information about these characteristics can help industries and researchers make informed decisions and achieve better outcomes when machining of these important materials. Accordingly, in the current research work, a comprehensive study has been carried out on machining of wrought aluminium from different points of view.

Enhanced mechanical and dielectric properties of Si3N4−BN composite ceramics fabricated via reaction re-sinteirng process

Dense Si3N4−BN composite ceramics were synthesized using reaction re-sintering method which involved an in-situ reactive sintering followed by a re-sintering process, employing Si3N4 and SiB6 powders as raw materials. Initially, SiB6 underwent nitridation to form Si3N4 and BN during the in suit reactive sintering. Subsequently, the pores between the Si3N4 and BN grains were significantly removed during the re-sintering stage, facilitating the strong integration of Si3N4 and BN grains within the composite ceramics. The re-sintering process significantly enhanced both the density and mechanical properties of the composite ceramics. The generated BN grains were uniformly distributed throughout the Si3N4 matrix, thereby improving both the machinability and dielectric properties. The composite ceramics with 20 wt.% BN demonstrated excellent mechanical and dielectric properties, characterized by a low apparent porosity of 3.8%, a high flexural strength of 315.6 MPa, a Vickers hardness of 5.0 GPa, a low dielectric constant of 5.02, and a loss tangent ranging from 1× 10-3 to 2.5 × 10-3 at frequencies between 8.2 and 12.4 GHz.

Analysis of the Application of Artificial Intelligence in Mahjong

Over the past few years, AI-based models for Mahjong have received a substantial amount of contribution due to the advancements in machine learning and game theory. In this review, these state-of-the-art AI models are considered, including Tjong, Kanachan, Suphx, and Zhejiang University’s developed model. The deployed approaches are disparately represented by reinforcement learning, Monte Carlo tree search (MCTS), and deep neural networks, all of which aim at solving the issues that come with the game’s structure, such as incomplete and overburdening information. The Tjong model complements the power of the transformer architecture with hierarchical decision-making and in turn, brings strategic depth to the gameplay. Kanachan employs Q-learning and MCTS to optimize decision-making, while Suphx combines these methods with the novel ideas of Double Q-learning and Thompson Sampling to achieve higher performance than seen before. The integration of reinforcement learning and a new evaluation model imbues the model with the unique properties of both learning machines and human expertise, namely depth and intelligence. As a result of all these exploits, obstacles still exist, the most notable among them are the management of completely unknown information, handling long-distance games, and the strategic balance between offense and defense. In the future, determinants of probabilistic reasoning, the improvement of deep learning systems, and the prowess of reward shaping will result in AI improvement in Mahjong.

A methodology for evaluating feature selection and clustering methods with project-specific requirements

This paper describes a methodology for ranking feature selection and clustering methods with user-specific preferences and taking data properties into account. For a better understanding of this paper, the developed methodology is referred to as the Two Machine Learning Procedures, Preferences and Properties (2ML3P) methodology. The 2ML3P methodology aims to support users from multiple domains, such as engineers, who have little expertise in machine learning (ML). It is also independent from the disciplinary core competencies of the manufacturer, with a strong focus on employability in small and mid-sized enterprises (SME). The foundation of the methodology to evaluate the combination of the two machine learning classes is described. It focuses on a range of feature selection and clustering methods, their limitations, and their challenges. The paper covers the concept phase by defining the inputs, such as the specific characteristics of machine learning classes or the properties of the production data and the user preferences. With applied methodologies such as the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), the preferences of the user as valid input are integrated. The scientific contribution of this methodology is the approach to include user preferences and specific data properties in the selection process of two ML methods. As digitalisation progresses, making data-driven decisions in the domains of production and logistics is a goal for many SMEs. This methodology can support a data-driven decision-aid model by providing a guided method, which requires relatively little ML knowledge on the part of the engineer. It allows the user(s) to select the best suited combination of ML methods for a clustering use case.

Comparing coherent and incoherent models for quantum homogenization

Here we investigate the role of quantum interference in the quantum homogenizer, whose convergence properties model a thermalization process. In the original quantum homogenizer protocol, a system qubit converges to the state of identical reservoir qubits through partial-swap interactions, that allow interference between reservoir qubits. We design an alternative, incoherent quantum homogenizer, where each system-reservoir interaction is moderated by a control qubit using a controlled-swap interaction. We show that our incoherent homogenizer satisfies the essential conditions for homogenization, being able to transform a qubit from any state to any other state to arbitrary accuracy, with negligible impact on the reservoir qubits' states. Our results show that the convergence properties of homogenization machines that are important for modeling thermalization are not dependent on coherence between qubits in the homogenization protocol. We then derive bounds on the resources required to re-use the homogenizers for performing state transformations. This demonstrates that both homogenizers are universal for any number of homogenizations, for an increased resource cost. Published by the American Physical Society 2024

Gaussian Distributions in Machine Learning

To help teaching and learning of machine learning courses, properties and application examples of Gaussian distributions in machine learning are presented, including classification, regression, and approximation. In each typical cases, emphasis is placed on the assumptions and the conclusions related to Gaussian distributions, so that students can make clear that it is the Gaussian distribution that make the somewhat abstract Bayes’ theorem more concrete and yields closed-form analytical predictive distributions.

Process Parameter Effects on Powder Mixed EDM Machining Characteristics Using Biocompatible Ti-6Al-4V Alloy

This study examines how various process parameters affect the machining properties of a bio-compatible Ti-6Al-4V alloy using PMEDM with silicon carbide (SiC) powder. The parameters investigated include peak current, pulse on/off time, powder concentration, and voltage gap. The study analyzed their effects on material removal rate (MRR), tool wear rate (TWR), surface roughness (SR), and surface morphology. A central composite design was used in the tests to make empirical models that use response surface methods to link the process parameters to the machining results. It is found that the Pulse current and Ton influence the material removal rate and the surface roughness significantly. The powder concentration also impacts PMEDM's machining performance. The Scanning electron microscopic images reveal the effect of powder seen in the machined components. The crater, micro cracks and machining marks can be seen in the SEM images. The surface integrity is correlated with the output parameters of surface roughness. The developed mathematical models effectively predict and optimize the machining properties of Ti-6Al-4V alloy using PMEDM with SiC powder. This research offers valuable insights for applying PMEDM in the fabrication of biomedical implants and devices made from Ti-6Al-4V alloy.

Determination of Machinability Properties of Nimonic-60 Superalloy Under Sustainable Conditions

Determination of Machinability Properties of Nimonic-60 Superalloy Under Sustainable Conditions

EXPERIMENTAL DETERMINATION OF TORQUE FOR THE DRILLING OFAL7SIMG ALLOY

The productivity of a machining process is closely related to the value of the cutting force.For the calculation of the cutting forces, a series of models have been developed to allow the determination of these forces according to the machined material and the specific conditions in which the process is carried out. Usually, for a better approximation of the value of the cutting force, the unitary specific cutting force is used, which depends on the machined material. For common materials, the values of specific unitary cutting forces are presented in the specialized literature, but for less frequently used materials, for example, those with reduced machining properties, the respective values are difficult, sometimes impossible to find. In this paper, determining the unitary specific cutting force for an aluminum alloy with a high silicon content is intended, recognized as difficult to machine. The determination of the value of this force is done starting from the measured values ofthe torsional moment at the drills.

Enhancement of machinability and surface tribological property of hardened bearing steel by electric pulse-assisted hard turning

Electric pulse-assisted cutting (EPAC) can reduce the hardness of the workpiece, improve the tribological state of the tool-workpiece/chip interfaces, and enhance the machinability of the workpiece material. Hardened bearing steel has a hardness higher than 60 HRC, so the hardened bearing steel has an extremely bad machinability. This paper carried out an experimental study on electric pulse assisted hard turning (EPAHT) of hardened bearing steel, and the tribological property of the turned hardened bearing steel was also explored. Results shown that compared to conventional hard turning, the EPAHT reduced the surface roughness of hardened bearing steel by 44.2 %. The workpiece surface work-hardening rate was reduced by 3.6 %, the thickness of the workpiece surface metamorphic layer was increased by 91.4 %, the friction coefficient of the workpiece surface was reduced by 20 %, the wear mark depth was decreased by 91 %, a uniform and clean wear morphology of the workpiece surface wear appeared. The surface friction and wear performance of hard-turning of hardened bearing steel were significantly enhanced by the EPAHT.

System Study Review of Electrical Machines under Specific Operating Conditions of Power Installations

The problem of life cycle vulnerability, predetermined by the development stages, operation and modernization of electrical machines due tosize and weight restrictions, dynamic electromagnetic and thermal overloads, possible flooding and changes in ventilation and cooling conditionsis considered. Reducing the weight and size of electric machines implies eliminating passive sections of magnetic circuits and optimizing the ratio of copper and steel. The desire to reduce weight and size indicators and increase energy density entails increased heat generation and temperature loads of structural elements. The increase in operatingtemperatures of modern machines indicates the need to develop mathematical models that account the response of electrical materials and structural elements to the combined thermal and magnetic fieldeffects. Such models are in demand to study the temperature dependence of insulating, magnetic and operating properties of electrical machines when establishing the requiredoperating moderestrictions in order to preserve and extend the life cycle. The criterion for making management decisions aimed at changing operating modes during operation can be maximum loads that limit exceeding the maximum permissible temperatures of the electrical insulationclass used, as well as critical values of induction determined by the temperature dependence of ferromagnetproperties that affect the performance characteristics of electrical machines. Further solution to the problem lies in the creation of digital twins containing complete mathematical and computer simulation models that describe processes in electrical machines, taking into account the temperature dependence of materialphysical properties of structural elements being subjected to thermal, magnetic and electric fields.

ÉTUDE COMPARATIVE D'UNE MACHINE À AIMANT PERMANENT À INVERSION DE FLUX À ROTOR INTÉRIEUR ET EXTÉRIEUR POUR ÉOLIENNE À ENTRAÎNEMENT DIRECT

The topology, operation principle, and electromagnetic performance of the inner rotor flux reversal permanent magnet (IRFRPM) machine and the outer rotor flux reversal permanent magnet (ORFRPM) machine are quantitatively compared in this paper. First, the mass torque of both machines is optimized and compared using particle swarm optimization (PSO) combined with the finite element method (FEM). Second, the static properties of both machines are calculated using finite element analysis (FEA). Both machines are suitable for direct-drive wind turbines, but ORFRPM outperforms IRFRPM.

MULTI-OBJECTIVE OPTIMIZATION OF ROTARY ASSISTED ELECTROCHEMICAL ARC DRILLING (R-ECAD) PROCESS USING GRA

Electrochemical Arc Drilling (ECAD) has demonstrated its effectiveness in micro-machining a variety of materials notwithstanding the inherent properties of materials. The increased machining properties of the ECAD method are a result of the inclusion of rotational effect of the working material. Better electrolyte replenishment, effective debris flushing, thin gas layer development, and spark uniformity are all credited with this improvement. Several input factors affect the machining characteristics of ECAD, making it difficult to simultaneously optimize these factors for several objectives. In order to maximise Material Removal Rate (MRR) and minimising Hole Overcut (HOC), this paper focuses on the multi-objective optimization of rotary-assisted ECAD (R-ECAD) input factors. Taguchi's L9 experimental design is used to produce micro-holes, and then Grey Relational Analysis (GRA) is used to perform the multi-objective optimization. The chosen input factors are working material rotation (WR), tool feed rate (FR) and applied voltage (V), whereas the chosen response factors are MRR and HOC. Results indicate that the rotating effect of the working material, which aids in the replenishment of electrolyte and the creation of a stable gas layer surrounding the tool, is notably the most significant input factor. For maximising the MRR and minimising HOC, the GRA-based optimised factors were found to be AIICIIBIII (60 rpm, 40 V, 0.8 mm/min). The responses are greatly improved by 39% as compared to the original machining, as demonstrated by microscopy images obtained during the GRA-based input factor optimization.

The Impact of nanoparticles (B4C-Al2O3) on mechanical, wear, fracture behavior and machining properties of formwork grade Al7075 composites

This study explores how ageing temperature and the volume percentage of Al2O3+B4C nanoparticles influence the machinability and hardness of stir-cast Al-7075 Metal Matrix Composite (MMC). Using liquid metallurgy techniques, hybrid materials were created by reinforcing Al7075 metal matrix with varying weight percentages of nanosized B4C (1.5%, 3%, and 4.5%) and Al2O3 (1%, 1.5%, and 2%). After fabrication, the samples were subjected to five-hour ageing process at temperatures of 100, 120, and 140 degrees Celsius, followed by cooling to ambient temperature (27 degrees Celsius). Hybrid nano composites that had been heat treated were tested for wear, tensile strength, and hardness. Results shows that, the addition of nanoparticles and heat treatment considerably improves the tensile strength, hardness, and wear resistance of hybrid composites by 3%, 17%, and 10%, respectively, for samples reinforced with 4.5% B4C + 2% Al2O3. SEM analysis was used to investigate the type of wear and the tensile fracture mode of nano composite samples by analyzing the wornout surface and the surface where tensile fracture occurred. Machinability was assessed using L27 orthogonal array tests, focusing on three key process parameters: feed rate (0.1 mm/min), depth of cut (0.2 mm/min), and spindle speed (1000 rpm). Outcomes show that, increasing the wt. % of nano-Al2O3/B4C leads to higher machining force and surface roughness (Ra) of MMCs. Conversely, higher ageing temperatures result in decreased machining force and surface roughness. Optimal surface roughness and machining force were achieved with 1% Al2O3 + 1.5% B4C and an ageing temperature of 140°C. These findings offer valuable insights into the ease of machining of composite metal alloys, emphasizing the importance of parameter selection and optimization for desired machining outcomes.

Development and Validation of a 1D Dynamic Model of an Injection Moulding Process and Design of a Model-Based Nozzle Pressure Controller.

A 1D model describing the dynamics of an injection moulding machine and the injection process is presented. The model describes an injection cylinder actuated by a dual-pump electro-hydraulic speed-variable drive and the filling, holding and cooling phases of the injection moulding process utilising amorphous polymers. The model is suggested as the foundation for the design of model-based pressure controllers of, e.g., the nozzle pressure. The focus is on using material, mould and machine properties to construct the model, making it possible to analyse and design the dynamic system prior to manufacturing hardware or conducting experiments. Both the presented model and the developed controller show good agreement with experimental results. The proposed method is general in nature and enables the design, analysis and evaluation of the machine, material and mould dynamics for controller design based solely on the physical properties of the system.