Types Of Machines Research Articles

Comparative analysis of dual stator machines

Electromagnetic performance of three different types of double stator permanent magnet machine is analyzed and compared in this study. The analyzed machines in this study are Machine 1, Machine 2 and Machine 3. These machines are designated as: M 1, M 2 and M 3, respectively. The studies are implemented using two-dimensional and three-dimensional finite element analysis (2D-FEA and 3D-FEA) methods. The predicted performance indices are: total harmonic distortion of the voltage, torque ripple, cogging torque, winding inductances, output torque and unbalanced magnetic force (UMF). The studies show that the investigated machine types have negligible reluctance torque and thus, similar axes inductance values. Therefore, the machines’ bulk torque components are contributed mainly by the magnets while the armature excitation sources yield lesser torque components. Amongst the compared machines, M 3 type has an outstanding performance in almost all the performance metrics, compared to M 2 and M 1 types. Nevertheless, M 1 machine type has some good attributes, particularly, with respect to its high output torque per applied magnet volume, in addition to its widest operating speed range ability. Low-speed high-torque applications are most suitable for the investigated machines in practice.

Exploring Human-Like Translation Strategy with Large Language Models

Abstract Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-level intelligence. Among their numerous skills, the translation abilities of LLMs have received considerable attention. Compared to typical machine translation that focuses solely on source-to-target mapping, LLM-based translation can potentially mimic the human translation process, which might take preparatory steps to ensure high-quality translation. This work explores this possibility by proposing the MAPS framework, which stands for Multi-Aspect Prompting and Selection. Specifically, we enable LLMs first to analyze the given source sentence and induce three aspects of translation-related knowledge (keywords, topics, and relevant demonstrations) to guide the final translation process. Moreover, we employ a selection mechanism based on quality estimation to filter out noisy and unhelpful knowledge. Both automatic (3 LLMs × 11 directions × 2 automatic metrics) and human evaluation (preference study and MQM) demonstrate the effectiveness of MAPS. Further analysis shows that by mimicking the human translation process, MAPS reduces various translation errors such as hallucination, ambiguity, mistranslation, awkward style, untranslated text, and omission. Source code is available at https://github.com/zwhe99/MAPS-mt.

Design of a New Drip Irrigation Belt Recovery Machine with Anti Breakage Function

Drip irrigation technology is widely used in agricultural production in the Xinjiang region. However, there are still a series of problems in the mechanized recovery of field drip irrigation belts, such as easy breakage of drip irrigation belts, low recovery efficiency, and excessive doping of recycled drip irrigation belts. This study focused on the problems of mechanized recovery of drip irrigation belts in Xinjiang and designed a new type of drip irrigation belt recovery machine. The key mechanisms of the new drip irrigation belt recovery machine were designed and theoretically analyzed, and their basic parameters were determined. Then, the superiority of the new drip irrigation belt recovery machine’s operational performance was verified through experiments. The experimental results showed that the new drip irrigation belt recovery machine is superior to existing machines in terms of the drip irrigation belt recovery rate and operational efficiency. In addition, the drip irrigation belts recovered by the newly designed machine have low impurity content. During the recovery process, the new machine requires less labor input than that of the existing machine.

Abstract A031: Prospective and clinical prediction in a retrospective trial that experimentally validated an AI/ML-derived whole-genome predictor as the most accurate and precise predictor of survival and response to treatment in glioblastoma

Abstract Cancer is complex, with contributing factors distributed across the entire genome affecting every aspect of the disease. But typical artificial intelligence and machine learning (AI/ML) would require 3B-patient training sets to generate predictive models from the whole 3B-nucleotide genome. As a result, tests remain limited to one to a few hundred genes. Prediction continues to rely mostly on such factors as a tumor’s grade and the patient’s age. And the understanding and management of cancer continue to involve guesswork. A genome-wide pattern in tumors from glioblastoma (GBM) patients was recently experimentally validated in a retrospective clinical trial as the most accurate and precise predictor of life expectancy and response to standard of care [doi: 10.1063/1.5142559]. Applicable to the U.S. population at large, this predictor, the first to encompass the whole genome, was mathematically (re)discovered and computationally (re)validated in open-source datasets from as few as 50–100 patients by using our data-agnostic physics-inspired AI/ML [doi: 10.1063/1.5099268, 10.1073/pnas.0530258100]. All other attempts to connect a GBM patient’s outcome with the tumor’s DNA copy numbers failed. For 70 years, the best indicator has been age. At 75–95% accuracy, our predictor is more accurate than and independent of age and all other indicators, including the one-gene tests for MGMT, IDH1, and TERT. Platform- and reference genome-agnostic, the predictor’s >99% precision is greater than the community consensus of <70% reproducibility based upon one to a few hundred genes. It describes mechanisms of transformation and identifies drug targets and combinations of targets to sensitize tumors to treatment. Now, in follow-up results from the trial we, first, show correct prospective prediction of the outcome of the five of the 79 patients who were alive four years earlier, at the time of first results. Two patients, who were predicted to have shorter survival, lived less than five years from diagnosis, whereas of the three patients predicted to have longer survival, one lived more than five, and the remaining two are alive >11.5, years from diagnosis. Second, we demonstrate 100%-precise clinical prediction for the 59 of the 79 patients with remaining tumor DNA, by using whole-genome sequencing in a Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologists (CAP) -regulated laboratory. Third, we establish that the risk that a tumor’s whole genome confers upon outcome, as is reflected by the predictor, among the 79 and, separately, 59 patients, is greater than that conferred by the patient’s Karnofsky performance score and access to chemotherapy and is surpassed only by the patient’s access to radiotherapy. This is a proof of principle that our AI/ML is uniquely suited for personalized medicine, that a patient’s survival and response to treatment are the outcome of their tumor’s whole genome, and that our AI/ML-derived whole-genome predictors can take the guesswork out of standard of care, clinical trials, and drug development. Citation Format: Sri Priya Ponnapalli, Penelope Miron, Kristy L. S. Miskimen, Kristin A. Waite, Nadiya Sosonkina, Sara E. Coppens, Anthony C. Bryan, Estevan P. Kiernan, Huanming Yang, Jay Bowen, Ghunwa A. Nakouzi, Jill S. Barnholtz-Sloan, Andrew E. Sloan, Tiffany R. Hodges, Orly Alter. Prospective and clinical prediction in a retrospective trial that experimentally validated an AI/ML-derived whole-genome predictor as the most accurate and precise predictor of survival and response to treatment in glioblastoma [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A031.

Contributions to the Analysis of Mining Equipment Fatigue Through the Dirlik Method and Spectral Analysis

Abstract The paper analyses the phenomenon of mining machinery fatigue, focusing on the Bucket Wheel Stacker-Reclaimer KsS 5600/3800x40 machine. To evaluate fatigue cycles under variable and non-periodic force conditions specific to this type of machine, Dirlik method and spectral analysis are applied. Dirlik method provides an innovative approach to stress spectrum analysis, taking into account the complex distribution of stress amplitudes during coal stacking operations and their deposition on the conveyor belt. Spectral analysis provides detailed insight into the frequency distribution in the dynamic response of Bucket Wheel Stacker-Reclaimer. This analysis allows the identification of the dominant frequencies associated with the charging and discharging cycles. The results obtained have significant implications for the optimisation of operations and the effective management of fatigue within this specific equipment.

Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

<p>With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.</p>

A model of the steam compression process in a piston reactor

The paper discusses the possible determination of steam parameters in a new type of piston machine for steam compression to generate supercritical water parameters. It presents a calculation model that allows one to simulate the process of steam compression in a cylinder with volume regulated by the piston position. In each calculation step, the steam parameters were determined on the basis of fast adiabatic changes which were corrected by the effect of leakage and heat transfer occurrence. The seal of the reactor was assumed to be a compression ring. Depending on the pressure drop on the seal, subcritical and supercritical flow was taken into account. The leak was corrected by the coefficient of flow contraction. Heat transfer was determined by equations for the Nusselt number for water and steam from the homogenous area. The programmed model allows one to simulate changes in the thermodynamic parameters of steam during the process of steam compression with any calculation step. The results presented in this paper show that the application of one compression ring allows us to obtain supercritical steam parameters. Various degrees of sealing leak tightness and their impact on the changeability of steam parameters were analyzed. Heat transfer was shown to have an impact not only on changes in steam temperature, but also on pressure. This paper analyzes the impact of the temperature of the walls of the compression chamber on the value and direction of heat transfer.

Unraveling the Mechanical Property Decrease of Electrospun Spider Silk: A Molecular Dynamics Simulation Study.

This study investigated the impact of electric fields on Nephila clavipes spider silk using molecular dynamics modeling. Electric fields with varying amplitudes and directions were observed to disrupt the β sheet structure of spider silk and reduce its mechanical properties. However, a notable exception was observed when a 0.1 V/nm electric field was applied in the antiparallel direction, resulting in improvements in Young's modulus and ultimate tensile strength. The antiparallel direction was observed to be particularly sensitive to electric fields, causing disruptions in beta sheets and hydrogen bonds, which significantly influence the mechanical properties. This study demonstrates that spider silk maintains its structural integrity at 0.1 V/nm. Possibly, lowering the power levels of typical electrospinning machines can prevent secondary structural disruption. These findings provide valuable insights for enhancing silk fiber production and applications using natural silk proteins while shedding light on the impact of electric fields on other silk proteins. Finally, this study opens up possibilities for optimizing electrospinning processes to enhance performance in various silk electrospinning applications.

A new dispatching mechanism for parallel-machine scheduling with different efficiencies and sequence-dependent setup times

The Apparent Tardiness Cost (ATC) dispatching rule was initially developed to minimize tardiness in single-machine scheduling problems. ATC extensions have been frequently applied in other production settings, relying heavily on blocking idle machine capacity with a single-machine outlook; this approach may not result in the best outcomes, considering that machines have different efficiencies. This study develops a new dispatching rule for parallel-machine scheduling, considering different machine efficiencies, ready times, and sequence-dependent setup times to minimize the total weighted tardiness. The proposed method reduces the time interference factor of the denominator item in the dispatching rule and uses more effective methods for selecting the best processing machine for the jobs. The grid approach is used to evaluate the method against the state-of-the-art. The experimental results confirm that the developed method is superior regardless of the type of parallel machines, the problem scale, and other operational parameters. It is also shown that other ATC dispatching rules can be improved by applying the proposed approach. The proposed method could be incorporated into soft computing techniques for more effective and efficient scheduling.

Artificial intelligence empowered physical layer security for 6G: State-of-the-art, challenges, and opportunities

With the commercial deployment of the 5G system, researchers from both academia and industry are moving attention to the blueprint of the 6G system. The space–air–ground–sea integrated 6G system is envisioned to support various new applications with very diverse requirements in terms of quality of service and security. Due to the open nature of the wireless channel, highly dynamic network topology, and mission-critical applications, 6G will face various security threats. The conventional upper-layer cryptography-based security mechanisms face multiple challenges to resist physical layer attacks in the context of large numbers computing, power-limited low-cost Internet of Thing (IoT) devices. The Physical layer security (PLS) mechanisms by exploiting the random nature of the wireless channel and intrinsic hardware imperfection provide complementary approaches to secure the 6G system. Meanwhile, the quick development and successful application of artificial intelligence facilitate the advancement of PLS solutions. In this paper, we make a comprehensive overview of machine learning-empowered PLS techniques toward 6G. We first introduce the vision of the 6G system, typical applications, key enabling radio technologies, security threats, and security requirements to 6G. Then typical machine learning (ML) methods and the driving forces for introducing intelligent PLS in 6G are presented. Afterward, the major academic works on PLS oriented to key 6G radio techniques including cell-free massive MIMO, visible light communication, Terahertz technology, configurable intelligent surfaces, molecular communication, and ambient backscatter communication are discussed. Then, the latest works on ML-enabled PLS solutions including physical layer key generation, secure wireless transmission, physical layer authentication, intelligent anti-jamming, and ML-enabled attack and detection are systematically reviewed. Finally, we identify important open issues and future research opportunities to inspire further studies to realize a more intelligent and secure 6G system.

Performance of continuous controlled attenuation parameter and liver stiffness measurement by the novel SmartExam in metabolic dysfunction-associated steatotic liver disease.

FibroScan® Expert 630 and FibroScan® Mini+430 are novel vibration-controlled transient elastography devices equipped with the same SmartExam software, which allows continuous measurement of controlled attenuation parameter (CAP) during the entire examination. This study aims to compare the CAP variabilities and the quantification for liver fibrosis and steatosis between the conventional FibroScan and the SmartExam-equipped machines in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). This retrospective study included 118 patients with biopsy-proven MASLD who underwent liver biopsy at two tertiary centres between 2021 and 2023. Liver stiffness and steatosis measurements were performed using both FibroScan machines and M and XL probes for each individual. Liver histology was used as the reference standard for liver fibrosis and steatosis staging. Standard deviations of continuous CAP (cCAP) were significantly lower than those of CAP for all probes (p < .0001). CAP variability was significantly associated with body mass index (p < .01), probe selection (p < .001) as well as the random effect of centre. Only the effect of probe selection (p < .001) was significantly associated with cCAP variability. No significant difference was found in the performance of staging liver fibrosis and steatosis between two types of machines at the same cut-offs. The SmartExam-based VCTE reduces the variability of CAP measurement and achieves a similar accuracy as the FibroScan 502 device for the estimation of both hepatic steatosis and fibrosis. Future studies should determine if cCAP is a better tool to monitor changes in steatosis than the original CAP.

The Bionic Body: Disability, Technology and Posthumanism

This paper looks at the new field of the critical posthuman disability studies and its potential to provide theoretical framework for critical theory’s engagement with modern technologies. How the concept of the bionic body affects representation of the disabled in contemporary culture and, in turn, how representation of the disabled body affects the changing boundaries of what is and what isn’t considered ‘human.’ Historically, the human body, as represented and defined on stage and in art, has maintained a strictly defined visual integrity. Anything not shaped as ‘human’ was typically deemed monstrous (from hybrid mythological creatures to severely disabled ‘elephant men’). Simultaneously, the category of ‘human’ was used to circumscribe the boundaries of belonging and the categories of valuation: some groups, including the disabled were deemed ‘sub-human’ and designated to either be disposed of (as the carrier of ‘life unworthy of life’) or, if possible, to approximate the ‘human’ body. (Romanska 2019: 92-93). Until very recently, the goal of the prosthetics industry was to create limbs that would serve as visual stand-ins for missing limbs. Similarly, the technological capacities of prosthetic limbs were delineated by human capacities: the disabled were to be given as much ‘abilities’ as the non-disabled, but no more. However, this perception of what the disabled body can and should do has changed with technological progress: not only do the newest prosthetics often look as ‘unhuman’ as possible, but their capacities put into question the capacities and limits of the non-disabled body. All of these and other issues that have emerged in recent years at the crossroads of posthumanism, disability, and biomimicry have led to the development of posthuman disability studies, which tries to untangle and reconceptualize the ethical, legal and philosophical boundaries of human enhancement, species belonging, life and death, and human rights. The posthuman biomimicry, and the prosthetic aspects of digital and AI technologies presupposes a form of disabling of the human body: body without any connection to some type of machine is an inferior body.&amp;nbsp;&amp;nbsp;In this context, understanding the historical dynamics, critical, philosophical, and ethical debates that have dominated disability studies can provide a framework to how we reconceptualize our posthuman, hybrid future in which our existence with the machines that redefine previous hierarchies is inevitable.&amp;nbsp;&amp;nbsp;

Математическая модель тренажера «конь – наездник» и ее программная реализация в системе компьютерной математики Wolfram Mathematica

Riding simulation machines allow training horse-back riders regardless of environmental and weather conditions, horse fatigue, stable remoteness and other factors. Riders can practice complex movements without fear of potential injury related to sometimes unpredictable behavior of the horse. This type of training machines requires special software for selecting the relevant action mode for the person during the rider’s practice. Thus, the selected behavior of the horse can be simulated and the injuries of horse-back rider can be avoided. For this purpose, the model of horse-back rider, taking into account its training level and the horse motion, is required. The model of “person – simulation machine” combination is proposed in the article. The proposed model allows developing operating modes taking into account the sportsman-training machine interaction. The mechanical model is an exosuit with a mobile pole, consisting of four links, the rider’s feet, shins, hips, and the corps are attached to. The pole corresponds to the mass center of the horse, the rider interacts with by standing on the stirrups. The pole motion is implemented by a telescopic link attached to immobile base. The relative rotations of the links are implemented by cylindrical hinges with negligible friction. The proposed model has been implemented as software in the “Wolfram Mathematica 11.3” environment. It has been designed for training machine dynamics simulation of the “horse ­– rider” system. The software includes several modules: 1) the module for specifying the structure of training machine mathematical model, generalized coordinates, and for auto-compilation of the corresponding system of differential equations; 2) the module for specifying the programmed model motion and calculation of the required control torques in the hinges; 3) the module for the Cauchy problem numerical solution; 4) the module for the animated visualizing of the model motion, and for exporting the obtained graphic results and numerical calculations. The developed software allows conducting dynamics analysis of mathematical model for the considered system based on the solution of both direct and inverse dynamics problems. Also it can be suggested for designing training machines with programmed operation mode. It has been shown that application of this software accelerates the development of training machines.

Assessing quality assurance of multi-leaf collimator using the structural similarity index.

This study aims to evaluate the viability of utilizing the Structural Similarity Index (SSI*) as an innovative imaging metric for quality assurance (QA) of the multi-leaf collimator (MLC). Additionally, we compared the results obtained through SSI* with those derived from a conventional Gamma index test for three types of Varian machines (Trilogy, Truebeam, and Edge) over a 12-week period of MLC QA in our clinic. To assess sensitivity to MLC positioning errors, we designed a 1cm slit on the reference MLC, subsequently shifted by 0.5-5mm on the target MLC. For evaluating sensitivity to output error, we irradiated five 25cm×25cm open fields on the portal image with varying Monitor Units (MUs) of 96-100. We compared SSI* and Gamma index tests using three linear accelerator (LINAC) machines: Varian Trilogy, Truebeam, and Edge, with MLC leaf widths of 1, 0.5, and 0.25mm. Weekly QA included VMAT and static field modes, with Picket fence test images acquired. Mechanical uncertainties related to the LINAC head, electronic portal imaging device (EPID), and MLC during gantry rotation and leaf motion were monitored. The Gamma index test started detecting the MLC shift at a threshold of 4mm, whereas the SSI* metric showed sensitivity to shifts as small as 2mm. Moreover, the Gamma index test identified dose changes at 95MUs, indicating a 5% dose difference based on the distance to agreement (DTA)/dose difference (DD) criteria of 1mm/3%. In contrast, the SSI* metric alerted to dose differences starting from 97MUs, corresponding to a 3% dose difference. The Gamma index test passed all measurements conducted on each machine. However, the SSI* metric rejected all measurements from the Edge and Trilogy machines and two from the Truebeam. Our findings demonstrate that the SSI* exhibits greater sensitivity than the Gamma index test in detecting MLC positioning errors and dose changes between static and VMAT modes. The SSI* metric outperformed the Gamma index test regarding sensitivity across these parameters.

Remanufacturing a Synchronous Reluctance Machine with Aluminum Winding: An Open Benchmark Problem for FEM Analysis

The European Union’s increasing focus on sustainable and eco-friendly product design has resulted in significant pressure on original equipment manufacturers to adopt more environmentally conscious practices. As a result, the remanufacturing of end-of-life electric machines is expected to become a promising industrial segment. Identifying the missing parameters of these types of machines will play an essential role in creating feasible and reliable redesigns and remanufacturing processes. A few case studies related to this problem have been published in the literature; however, some novel, openly accessible benchmark problems can facilitate the research and function as a basis for comparing and validating novel numerical methods. This paper presents the identification process of an experimental synchronous machine. It outlines methodologies for identifying material properties, winding schemes, and other critical parameters for the finite element analysis and modelling of electric machines with incomplete information. The machine in question is intended for remanufacturing, with the plan to replace its faulty winding with an aluminium-based alternative. It also serves as an open benchmark problem for researchers, designers, and practitioners.

Numerical and Experimental Investigation of Nano zinc Oxide's Effect on the Mechanical Properties of Chloroprene and Natural Rubber (CR/NR) Composites

Nanocomposites, especially natural rubber (NR), have been extensively studied for their unique features and superior performance in tire applications. The present research investigated the impact of zinc oxide nanoparticles (ZnO) on the performance of typical rotary machine seals made of chloroprene rubber / natural rubber (CR/NR) composites. An ordinary standard rubber two-roll mill and hydraulic press were used to prepare high-temperature vulcanized CR/NR samples filled with ZnO nanoparticles. Tensile strength, tear resistance, abrasion resistance, resilience, and hardness were measured to determine the effects of nanoparticles on these physical and mechanical properties. Based on the various hyperelastic modeling schemes, enhancement in multiple characteristics of the control sample, such as overhaul properties, was observed. Furthermore, results show that increasing nanoparticle content in the vulcanisates increased the physicomechanical characteristics, such as hardness, resilience, tensile strength, and elastic Modulus at 200% strain. Moreover, hyperelastic analytical modeling shows that the differences with experimental results are less than 5%.

Optimum Electrical Machine Selection for Spacecraft Electro-Mechanical Battery

—Electro-Mechanical Batteries have important advantages comparing chemical batteries and using them is popularized specially in Low Earth Orbit satellites recently. Different type of Permanent Magnet machines are used in these systems as Motor/Generator. Simplified parametric design method is given for synchronous permanent magnet and brushless DC in this paper and finite element based simulations are used to confirm design presses. Finally proper machine is selected based on important parameters in spacecraft.

Mass spectrometry-based proteomics data from thousands of HeLa control samples

Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

Machine learning and IoT – Based predictive maintenance approach for industrial applications

Unplanned outage in industry due to machine failures can lead to significant production losses and increased maintenance costs. Predictive maintenance methods use the data collected from IoT-enabled devices installed in working machines to detect incipient faults and prevent major failures. In this study, a predictive maintenance system based on machine learning algorithms, specifically AdaBoost, is presented to classify different types of machines stops in real-time with application in knitting machines. The data collected from the machines include machine speeds and steps, which were pre-processed and fed into the machine learning model to classify six types of machines stops: gate stop, feeder stop, needle stop, completed roll stop, idle stop, and lycra stop. The model is trained and optimized using a combination of hyperparameter tuning and cross-validation techniques to achieve an accuracy of 92% on the test set. The results demonstrate the potential of the proposed system to accurately classify machine stops and enable timely maintenance actions; thereby, improving the overall efficiency and productivity of the textile industry.

Integration of digital lean principles and line balancing in apparel manufacturing

Purpose: The integration of digitalization into the apparel manufacturing sector has become a strategic imperative, providing companies with a unique competitive edge. This study explores the application of ITEX PMD, an Internet of Things (IoT) data collection device, and evaluates the ITEX digital line balancing program within the context of digital lean management. By leveraging real-time data tracking, big data utilization, and advanced data analysis techniques, companies aim to enhance flexibility, agility, and operational efficiency in their manufacturing processes. Design/methodology/approach: This research delves into operational definitions for men's hoody sweatshirts, detailing the types of machines used, specifying standard task times, and elucidating the precedence relations of operations. The study utilizes ITEX PMD as a real-time production data collection device, generating periodic line reports through the associated software program. Findings: The ITEX digital line balancing program shows promise in assisting shop floor managers to optimize sewing lines with efficiency and seamlessness, contributing to overall operational excellence. The program provides another layer of visibility into the factory, reducing non-value-added activities, and improving efficiency. The ITEX Soft algorithm facilitates an ideal assembly line layout, ensuring a balanced workload among workstations, aligned with the continuous flow principle in the sewing line. Originality/value: This shop floor and software solution not only enhance visibility but also offer a practical means to reduce non-value-added activities, thereby improving overall efficiency. The ITEX Soft algorithm emerges as a valuable tool, contributing to a more balanced workload among workstations and optimizing assembly line layout. This study sheds light on the potential of digital lean principles in reshaping manufacturing processes and fostering operational excellence in the apparel industry.