Machine Work Research Articles

Investigation into electrochemical machining of aviation grade inconel 625 super alloy: an experimental study with advanced optimization and microstructural analysis

Purpose This article targeted to experimentally examine the impact of several considered process parameters namely, applied voltage (AV), tool feed rate, electrolyte concentration and pulse frequency (PF), on the material removal rate (MRR) and radial overcut (ROC) while performing shaped tube drilling of aviation grade Inconel 625 super alloy through electrochemical machining principle. Further, an attempt has also been made to develop mathematical models for the process responses along with advanced optimization with evolutionary methods. Design/methodology/approach The central composite rotatable design matrix was used to scheme out the experiments in the present study. The consistency and accuracy of the developed mathematical models were confirmed through statistical results. Additionally, a field emission scanning electron microscope analysis was conducted to assess and analyze the microstructure of the machined work samples. The study also seeks to optimize the selected process inputs for MRR and ROC through the implementation of the desirability method, particle swarm optimization (PSO) and Teaching Learning-Based Optimization (TLBO). Findings The ROC is significantly influenced by the input parameters, specifically the PF and AV. Less ROC values were observed when the high PF with moderate AV. The minimum and maximum values of ROC and MRR were obtained as; 135.128 µm and 380.720 µm; 1.37 mg/min and 2.3707 mg/min, correspondingly. The best optimized confirmatory results were obtained through the TLBO approach, with an MRR value of 3.1587 mg/min and a ROC of 71.9629 µm, in comparison to the PSO and desirability approaches. Originality/value The various challenges associated with the productive machining of aviation grade Inconel 625 superalloy have been explored experimentally. The conducted experimentation has been performed on the in-house fabricated micro-electrochemical setup capable of performing a variety of advanced machining operations at the miniaturized level. Further, the application of shaped tube drilling while processing aviation grade Inconel 625 superalloy has been explored with the developed micro-ECM set-up. Moreover, the performed microstructure analysis of the machined work samples has elaborated and explored the various associated surface integrity aspects which are quite crucial when it comes to real-life aerospace-related applications. The utility of designed experiments has further made the attempted experimental analysis more fruitful and qualitative too.

Molecular model of a bacterial flagellar motor in situ reveals a "parts-list" of protein adaptations to increase torque.

One hurdle to understanding how molecular machines work, and how they evolve, is our inability to see their structures in situ. Here we describe a minicell system that enables in situ cryogenic electron microscopy imaging and single particle analysis to investigate the structure of an iconic molecular machine, the bacterial flagellar motor, which spins a helical propeller for propulsion. We determine the structure of the high-torque Campylobacter jejuni motor in situ, including the subnanometre-resolution structure of the periplasmic scaffold, an adaptation essential to high torque. Our structure enables identification of new proteins, and interpretation with molecular models highlights origins of new components, reveals modifications of the conserved motor core, and explain how these structures both template a wider ring of motor proteins, and buttress the motor during swimming reversals. We also acquire insights into universal principles of flagellar torque generation. This approach is broadly applicable to other membrane-residing bacterial molecular machines complexes.

Optimization of Material Removal Rate and Wear Rate in EDM Machining of Inconel 625 Using a Novel Nano Al-Ni Composite Electrode: A Response Surface Methodology Approach

The electric discharge machine is a novel machining technique. When the tool and the work item do not communicate. Components composed of difficult-to-machine hard materials. In the current experimental inquiry, the process factors that influence the machining concert process and its effectiveness are explored. The response table for each set of machining limitations is used in conjunction with the response surface methodology in the research to determine the appropriate levels of machining factors. The work's machining input parameters are optimized for maximum material removal rate (MRR) and reduce the electrode wear rate (EWR) when electro-discharge machining Inconel 625 using the tool Nano Al-Ni composite electrode. Another analysis of variance is to identify the factors causing the different answers mentioned above. EDAX was also used to investigate the material composition of the workpiece. Machined workpiece surfaces were evaluated using scanning electron microscopy to assess surface accuracy and microstructural characteristics (SEM). The following conclusions may be drawn from this work. The MRR parameter that is most affected is pulse off time. As pulse-off time lengthens, MRR increases. According to the response graph, the greatest MRR value that can be attained under the ideal parameters of Ip = 8A, T-ON = 50μs, and T-OFF= 100μs is 0.0115 g/min. The magnitudes of MRR range from 0.0091 to 0.044 g/min. Impact of T ON and T OFF: Higher EWR is 0.0022 g/min is seen in runs with T ON = 70 μs and T OFF = 100 μs.

A deterministic fluid model for production and energy mode control of a single machine

Improving machines’ energy efficiency through dynamic energy mode control to meet demand requirements with minimal energy consumption is a promising approach. This study considers a machine operating in working, idle, off, and warmup energy modes with different energy consumption in each mode. A deterministic fluid model is developed to analyze an energy mode control policy that determines when to keep the machine working, off or idle, and switch to other modes based on the inventory/backlog level to minimize the total energy, inventory, and backlog costs. This approach facilitates the derivation of closed-form expressions for the optimal thresholds and the associated costs. This modeling approach allows us to prove that a policy that operates the machine between the working and off modes or the working and idle modes is always better than a hybrid policy that operates the machine in working, off, and idle modes simultaneously. We use the solution of the deterministic fluid model to propose an approximate policy for machines with stochastic production, warmup, and demand processes. We compare the results of the proposed approximation method with the optimal solution of a stochastic system where the production and warmup times are exponential and the demand inter-arrival times have Erlang distribution. The optimal solution for the stochastic system is determined by solving a Markovian Decision Process (MDP). Our numerical experiments show that the proposed approximation method predicts the optimal policy type for the stochastic case with a 89.3% accuracy, and the average error between the optimal cost and the cost obtained with the approximation method is 1.37% for 729 different cases tested. Furthermore, the computational efficiency of the proposed approximation is around 250 times better than the effort to determine the optimal policy using an MDP approach. We propose this approximation method where the control parameters are given in closed form as an easy-to-implement and effective policy to control energy modes to minimize the total energy, inventory, and backlog costs. Furthermore, we present the deterministic fluid modeling approach as a versatile approach to analyze energy mode control problems.

Structural characterisation of the complete cycle of sliding clamp loading in Escherichia coli.

Ring-shaped DNA sliding clamps are essential for DNA replication and genome maintenance. Clamps need to be opened and chaperoned onto DNA by clamp loader complexes (CLCs). Detailed understanding of the mechanisms by which CLCs open and place clamps around DNA remains incomplete. Here, we present a series of six structures of the Escherichia coli CLC bound to an open or closed clamp prior to and after binding to a primer-template DNA, representing the most significant intermediates in the clamp loading process. We show that the ATP-bound CLC first binds to a clamp, then constricts to hold onto it. The CLC then expands to open the clamp with a gap large enough for double-stranded DNA to enter. Upon binding to DNA, the CLC constricts slightly, allowing clamp closing around DNA. These structures provide critical high-resolution snapshots of clamp loading by the E. coli CLC, revealing how the molecular machine works.

A Mathematical Modelling for Three-Factor Quantum Biometric Authentication Using Machine Learning, Double-Layer Encryption, Cryptographic Algorithms

Traditional authentication mechanisms are vulnerable to sophisticated attacks in the ever-changing cybersecurity paradigms. In this paper, a new three-factor quantum biometric system is proposed that were analysed to increase security level and reduce threats. This model employs multiple strategies, like quantum key distribution, a biometric identification system and machine learning which encrypts data based on their unique properties in an innovative way to provide not only one but two layers of encryption from unauthorized access. The system in its essence combines three critical elements: quantum generated cryptographic keys, biometric data (fingerprint or retinal scans) and dynamic behaviour-based identification using machine learning algorithms. By adding QKD to the hardware, an encryption key that should be practically impossible for anyone else (except sender and receiver) to intercept is securely generated and distributed through quantum mechanics principles of detection preventing possible eavesdropper. Secondly, the biometric data means that an imposter would have to not only steal your smartphone but also be able to authenticate themselves using unique physiology which takes even more of the likelihood out. Machine learning models study user behaviour patterns and work to adapt against evolving threats, gradually improving the accuracy of authentication with time. For added security, the system uses a double-layer encryption routine. The newly proposed system has a two-level encryption, and in the first layer, quantum keys encrypted bio-metric data while classical algorithms are applied to encrypt overall communication as well storage process. Besides ensuring the safety of private biometric information through dual encryption, this added redundancy also allows them to function as extra fail safe so that if one layer is breached, the entire system will still stand uncompromised. We evaluate the implementation of this new authentication scheme to a series-simulations and real-world test, showing the performance in different scenarios including high threat level environments. The results show major security enhancement compared to the traditional implementation of authentication methods with an observed amount decrease in data breaches and unsanctioned access attempts.

EETS: An energy-efficient task scheduler in cloud computing based on improved DQN algorithm

The huge energy consumption of data centers in cloud computing leads to increased operating costs and high carbon emissions to the environment. Deep Reinforcement Learning (DRL) technology combines of deep learning and reinforcement learning, which has an obvious advantage in solving complex task scheduling problems. Deep Q Network(DQN)-based task scheduling has been employed for objective optimization. However, training the DQN algorithm may result in value overestimation, which can negatively impact the learning effectiveness. The replay buffer technique, while increasing sample utilization, does not distinguish between sample importance, resulting in limited utilization of valuable samples. This study proposes an enhanced task scheduling algorithm based on the DQN framework, which utilizes a more optimized Dueling-network architecture as well as Double DQN strategy to alleviate the overestimation bias and address the shortcomings of DQN. It also incorporates a prioritized experience replay technique to achieve importance sampling of experience data, which overcomes the problem of low utilization due to uniform sampling from replay memory. Based on these improved techniques, we developed an energy-efficient task scheduling algorithm called EETS (Energy-Efficient Task Scheduling). This algorithm automatically learns the optimal scheduling policy from historical data while interacting with the environment. Experimental results demonstrate that EETS exhibits faster convergence rates and higher rewards compared to both DQN and DDQN. In scheduling performance, EETS outperforms other baseline algorithms in key metrics, including energy consumption, average task response time, and average machine working time. Particularly, it has a significant advantage when handling large batches of tasks.

A unified solution framework for flexible job shop scheduling problems with multiple resource constraints

This paper examines flexible job shop scheduling problems with multiple resource constraints. A unified solution framework is presented for modelling various types of non-renewable, renewable and cumulative resources, such as limited capacity machine buffers, tools, utilities and work in progress buffers. We propose a Constraint Programming (CP) model and a CP-based Adaptive Large Neighbourhood Search (ALNS-CP) algorithm. The ALNS-CP uses long-term memory structures to store information about the assignment to machines of both individual operations and pairs of operations, as encountered in high-quality and diverse solutions during the search process. This information is used to create additional constraints for the CP solver, which guide the search towards promising regions of the solution space. Numerous experiments are conducted on well-known benchmark sets to assess the performance of ALNS-CP against the current state-of-the-art. Additional experiments are conducted on new instances of various sizes to study the impact of different resource types on the makespan. The computational results show that the proposed solution framework is highly competitive, while it was able to produce 39 new best solutions on well-known problem instances of the literature.

DNA damage in workers exposed to mineral oils

Mineral oils, untreated or mildly treated, have been classified in group 1 as a potential source of cancer by the International Agency for Research on Cancer (IARC). Although numerous studies have implicated metalworking fluids (MWFs) as human carcinogens, toxicology data regarding the mechanism of carcinogenicity are limited. This study is intended to examine the systemic effects of machining workers’ exposure to MWFs. The potential toxicity of mineral oils was investigated in 65 lathe workers compared to controls (66 men). The occupational exposure was measured by the National Institute for Occupational Safety and Health (NIOSH) 5026. The DNA damage has been examined by the comet assay method. According to the field assessments, the time-weighted average (TWA) exposure to mineral oil mist was 7.67 ± 3.21 mg/m3. A comet assay of peripheral blood cells showed that tail length (TL) and olive moment (OM) were significantly higher in the exposed group (p < 0.05). A multiple logistic regression analysis revealed that, within subjects with over 10 years of exposure, the odds ratio of worker with high TL, percent of DNA in tail, OM, and tail moment (TM) were 1.68, 1.41, 1.71, and 2.71, respectively. DNA strand break in exposed workers was associated with higher exposure time in years. Mineral oil toxicity could be altered in the presence of by-products and impurities. For a better understanding of genotoxicity, further studies are required.

Studi Hubungan Drilling Rate Index Dengan Uniaxial Compressive Strength Pada Batupasir Formasi Pulaubalang dan Formasi Kampungbaru

Drilling capability, is one of the most important considerations in rock excavation, which can be defined as the ease of drilling a rock mass at a certain time for a long period of time with a drill bit. Rock drilling capabilities are influenced by various factors related to drilling machine working parameters and geotechnical characteristics of the rock mass (Yenice, 2019). Based on the above, this research was carried out to determine the UCS value in rock samples and the relationship formed by the DRI test value with the value produced in the UCS test using a regression analysis graph.

A quantum otto heat engine driven by three quantum dots

A quantum heat engine composed of three coupled quantum dots as a working substance is proposed. Since quantum dots naturally obey the Fermi Hubbard Hamiltonian, the strong coupling interaction regime allows the working substance to be evaluated under an effective Heisenberg Hamiltonian. Indeed, the influence of the strength coupling, between the three dots, on quantum machine efficiency and work in the presence of a homogeneous magnetic field is also examined. Furthermore, the influence of entanglement on the efficiency & work of the quantum dot Otto heat engine is well analyzed. As a tripartite working substance, we are interested in analyzing the local work and efficiency associated with each single and pair of quantum dots. The results show that the local efficiency associated with a pair of quantum dots achieves a maximum value, unlike the global efficiency. Indeed, the entanglement impact on Global/local work is studied.

Research Status and Development Trend of Mechanized Sand Production Process and Equipment Optimization

With the continuous development of engineering construction industry, the demand for sand used in construction is increasing, the traditional natural sand is increasingly in short supply, and the demand for mechanical sand is increasing. The vertical shaft impact sand machine is an important equipment machine in the production process of mechanical sand. The rotor in the vertical shaft impact sand machine is the core part of the normal work of the sand machine. Studying the rotor in the vertical shaft impact sand machine can improve the efficiency and stability of the sand machine in the sand making process. Compared with the research results at home and abroad, the type and research direction of rotor structure are summarized, and the development trend of vertical shaft impact sand making machine is proposed.

Analysis of Freon Leakage in Condenser to Room Temperature on MT. Kuang

The development of technology has led to increased use of air conditioning. Air conditioning is a process that cools, dries, cleans, and circulates air, while controlling its quantity and quality. It is important for machinists and electricians responsible for cooling machines to effectively carry out their duties and responsibilities by maintaining the machines properly. This research is a qualitative case study involving descriptive analysis using written or spoken observations of behavior. It focuses on the analysis of freon leaks in the condenser related to room temperature on the MT ship, Kuang. The research concludes that the decrease in condenser work in refrigerator machines is caused by interference with the dryer. The recommended efforts include inspecting and repairing leaking pipes, and conducting maintenance and repairs in accordance with the Manual Book standards.

MaGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

Comparative Ergonomic Posture Analysis of CNC Milling Machine Workers through Digital Human Modeling and Artificial Neural Networks

Objectives: To analyze the critical postures of the CNC milling machine operators by RULA (Rapid Upper Limb Assessment) scores and develop an ANN (Artificial Neural Network) prediction model. Methods: The methodology includes a postural analysis of 40 male CNC milling machine operators across Bangladesh, employing both manual (using manual RULA assessment worksheet) and digital (using CATIA V5R21 software) RULA methods complemented by an ANN prediction model. Finally, Digital RULA scores through DHM (Digital Human Modeling) and ANN predicted RULA scores would be compared. Findings: Digital RULA analysis reveals that lifting, carrying, and positioning are the most crucial ergonomic postures, and the most prominent high-risk category limbs are wrist and arm. The overall initial RULA score for lifting, carrying, and positioning are 7, 6, and 7, respectively, and reduced to 3, 3 and 4 respectively for ergonomically designed posture. The ANN model, structured with input, hidden, and output layers of 7, 10, and 1 nodes, significantly refines ergonomic risk prediction by aligning predicted scores closely with actual outcomes during the first stage, emphasized for training. It demonstrates a perfect correlation (R=1) in training, testing, validation, and overall performance for using manual RULA scores. The model's accuracy is further evidenced by minimal prediction offsets across all datasets for digital RULA score in the second stage, with correlation coefficients of 0.87003 (training), 0.93676 (validation), 0.89113 (testing), and (0.88395) for overall. This study contributes significant advancements in ergonomic risk assessment, highlighting the adoption of improved postures to reduce musculoskeletal disorders. Novelty: Employing both manual and DHM methods for RULA score calculation combined with ANN model, which can predict postural risk as floating number and fit a wider range of parameters. Keywords: ANN, CNC, Digital Human Modeling (DHM), Ergonomics, RULA

Analisis System Kerja Mesin Injection ‎Molding Haitian Ma2000 Pada Produksi Super ‎Mop di PT Bolde Makmur Indonesia

Nowadays, plastic has become a material that is inseparable from human life. Plastic is considered an easily available, practical, light and modern material. ‎In order to be able to make a plastic product that suits what we want, of course we need adequate technology, both in terms of injection machines, injection molding, materials, methods and people. The way this injection molding machine works is by putting plastic pellets into a barrel which is then heated until it melts. The melted plastic is injected into the mold which is then cooled. After that, it is released from the mold and a plastic product is formed. Current analysis produces values of 23.2 A and 20 A, which are then used to calculate power in the injection molding process. The calculation results show a power of 8.7 KW and 7.5 KW based on the data obtained.

Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.

Effects of Cryogenic- and Cool-Assisted Burnishing on the Surface Integrity and Operating Behavior of Metal Components: A Review and Perspectives

When placed under cryogenic temperatures (below −180 °C), metallic materials undergo structural changes that can improve their service life. This process, known as cryogenic treatment (CrT), has received extensive research attention over the past five decades. CrT can be applied as either an autonomous process (for steels and non-ferrous alloys, tool materials, and finished products) or as an assisting process for conventional metalworking. Cryogenic impacts and conventional machining or static surface cold working (SCW) can also be performed simultaneously in hybrid processes. The static SCW, known as burnishing, is a widely used environmentally friendly finishing process that achieves high-quality surfaces of metal components. The present review is dedicated to the portion of the hybrid processes in which burnishing under cryogenic conditions is carried out from the viewpoint of surface engineering, namely, finishing–surface integrity (SI)–operational behavior. Analyzes and summaries of the effects of cryogenic-assisted (CrA) burnishing on SI and the operational behavior of the investigated materials are made, and perspectives for future research are proposed.

Reviewing the Concepts of Productivity Management

Experts and academics in disciplines including economics, industrial and organisational psychology, accounting, physics, engineering, and management have previously focused on productivity. They define and interpret productivity differently as a result of their varied understandings of knowledge, experience, fields, and environmental factors. Many factors are effective in the definition and views of different schools towards productivity, about that how organizations, Groups, Human beings, Machines work in different environments and how their productivity should be measured each, and discipline has its own principles and insights. Considering that the importance of management concepts is due to their role in the organization's productivity, managers should work on productivity both in the short term and in the long term to avoid the challenges caused by the lack of productivity growth. This research systematically examines the concept of productivity and identifies factors affecting it based on Joseph Prokopenko's model. The purpose of this article is to examine the different ways of dealing with the concepts of "productivity" in the literature and to show that the definitions used regarding productivity do not follow a common grammar. Due to a misunderstanding of the concepts of productivity and the factors affecting it, most measurement and improvement methods are used without a clear understanding of what should be measured or improved. Therefore, this study reviews the concepts of productivity, examines the main factors of productivity (efficiency and effectiveness) and explains the different relationships between input and output in productivity, and explains the factors affecting productivity based on Joseph Prokopenko's model.

TRIBOLOGICAL TESTS OF ACRYLIC TEETH FOR REMOVABLE DENTURES

The functionality of removable dentures is ensured by proper diagnostics and clinical modeling. Noless important are the structural conditions and the biotribological and biomechanical context, whichdetermine the wear resistance in the contact of opposing teeth and the contact of the denture plate with thestomatognathic system. The aim of the study is to evaluate acrylic teeth used in prosthetic reconstructionsbased on microstructural, micromechanical and tribological tests. Samples for testing were taken from teethfor removable dentures made by various manufacturers. Microstructural analyses were performed using anoptical microscope and a scanning microscope. Microhardness and elasticity coefficient measurements wereperformed on the NHT device. Tribological tests were performed on a Roxana Machine Works tester using afriction node: ball – 3 discs made of the tested material. The conducted research allowed for the evaluation ofthe structural quality of acrylic teeth and the determination of the tribological interaction resulting from thecontact of synthetic and natural teeth in the presence of artificial saliva.