Industrial Engineering Research Articles

Stochastic Gradient Descent with Pre-Conditioned Polyak Step-Size

Stochastic Gradient Descent (SGD) is one of the many iterative optimization methods that are widely used in solving machine learning problems. These methods display valuable properties and attract researchers and industrial machine learning engineers with their simplicity. However, one of the weaknesses of this type of methods is the necessity to tune learning rate (step-size) for every loss function and dataset combination to solve an optimization problem and get an efficient performance in a given time budget. Stochastic Gradient Descent with Polyak Step-size (SPS) is a method that offers an update rule that alleviates the need of fine-tuning the learning rate of an optimizer. In this paper, we propose an extension of SPS that employs preconditioning techniques, such as Hutchinson’s method, Adam, and AdaGrad, to improve its performance on badly scaled and/or ill-conditioned datasets.

Novel sparging strategies to enhance dissolved carbon dioxide stripping in industrial scale stirred tank reactors

Aerated stirred tank reactors are widely used in bio-process engineering and pharmaceutical industries. To supply the organisms with oxygen and control the pH value, oxygen is transferred from air bubbles into the liquid phase, and, at the same time, carbon dioxide is stripped from the liquid phase with the same gas bubbles. The volumetric mass transfer coefficients for oxygen and carbon dioxide are, therefore, of crucial importance for the design and scale-up of aerated stirred tank reactors. In this experimental work, the volumetric mass transfer coefficients for oxygen and carbon dioxide are investigated simultaneously to study their mutual influence. The mass transfer performance for oxygen and carbon dioxide is conducted in stirred tank reactors on the 3 L laboratory scale, 30 L pilot scale, and 15,000 L production scale. First, the influence of dissolved carbon dioxide on the oxygen mass transfer performance is investigated in a 30 L pilot scale stirred tank reactor. The results show that the volumetric mass transfer coefficient of oxygen is not affected by the concentration of dissolved carbon dioxide, but the total mass flux of oxygen decreases with increasing carbon dioxide concentration due to the decreasing partial pressure difference. With rising gassing rate and volumetric power input, both mass transfer coefficients for oxygen and carbon dioxide show the same increasing trend. Although this trend can also be observed when scaling down to the 3 L laboratory scale reactor, a significantly different effect must be considered for the scale-up to the 15,000 L industrial scale reactor. The limited absorption capacity for carbon dioxide of the gas bubbles during the long residence time in the industrial scale reactor is noticeable here, which is why the specific interfacial area is of negligible importance. This effect is used to develop a method for independent control of oxygen and carbon dioxide mass transfer performance on an industrial scale and to increase the mass transfer performance for carbon dioxide by up to 25%.

Earthwork Network Architecture (ENA): Research for Earthwork Quantity Estimation Method Improvement with Large Language Model

Accurate earthwork quantity estimation is essential for effective project planning and cost management in the Architecture, Engineering, and Construction (AEC) industry. Traditional methods for quantity takeoff are often time-consuming and susceptible to human error, particularly when working with unstructured datasets such as CAD drawings. This study introduces the Earthwork Network Architecture (ENA), a novel deep learning framework that incorporates Large Language Models (LLMs), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, and Transformers to automate and enhance the accuracy of earthwork quantity estimation. We assume that if LLMs can be trained effectively using such unstructured construction dataset, the effects such as improved accuracy and the challenges of LLMs can be clearly examined. Among the architectures tested, the LLM-based ENA demonstrated superior performance, achieving faster convergence, greater loss reduction, and higher classification accuracy, with a Quantity Takeoff Classification accuracy of 97.17%. However, the LLMs required significantly more computational resources compared with other models. These findings suggest that LLMs, typically used in natural language processing, can be effectively adapted for complex AEC datasets. This study lays the groundwork for future AI-driven solutions in the AEC industry, underscoring the potential of LLMs and Transformers to automate the quantity takeoff process and manage multimodal data in construction projects.

A Review on the Implementation and Effectiveness of Lean Manufacturing Strategies for Industrial and Service Sectors

Ensuring a neat, clean, organized, safe, and productive workplace is essential for every organization. Waste and abnormalities may lead to a high loss of productivity and, consequently, capital, along with a significant compromise to the health and safety of the stakeholders. To overcome such challenges, Lean manufacturing is implemented worldwide. The main aim of this technique is to reduce or eliminate certain types of waste (such as those related to motion, transportation, overproduction, waiting time, etc.) that may contribute to financial loss for the companies. The Lean technique is hybridized with other industrial engineering methods such as DMAIC, Six Sigma, Kanban, etc., depending on the situation. This paper reviews some of the vital work in the last five years on implementing Lean manufacturing for various industrial, commercial, and service setups. It first introduces Lean manufacturing, discusses its implementation procedure and tools and techniques, and presents an analysis of previous attempts made by researchers and engineers using Lean to minimize waste, maximize productivity and efficiency, and enhance quality. The article ends with concluding remarks and highlighting the directions for future research. The novelty of this article lies in the fact that along with a basic introduction and review of the last five years’ literature, it also sheds light on current trends; recent developments; Lean integration with Six-Sigma, Kanban, Kaizen; automation and digital technology interventions to increase the effectiveness of Lean manufacturing; contribution of Lean in sustainable development.

Building Information Modeling Adoption in the Palestinian Construction Industry: Influencing Factors, External Determinants, and Technology Acceptance

ABSTRACT Building Information Modeling (BIM) is a worldwide spreading technology for the Architecture, Engineering, and Construction (AEC) industry that allows for continuous modification and improvement of building models, resulting in improved quality and cost savings in the project. The study aims to investigate the adoption pattern of BIM in the Palestinian market and identify significant factors that influence the adoption process, including top management support, organizational culture, and the number of experts and technicians. A structured questionnaire was developed, distributed electronically, and 168 valid responses were received, leading to a margin error of 4.4% and a response rate of 67.2%. A model, based on the Technology Acceptance Model (TAM) was developed and partial least squares structural equation modeling (PLS-SEM) was utilized to evaluate the measurement model’s reliability and validity, test hypotheses regarding the relationships between constructs, and assess the impact of various factors on the intention to adopt BIM within the construction industry. Six external influences on acceptance are highlighted. Individual and organizational acceptance drivers are identified, including top management support, ease of use, and competitive landscape. Government policies impact adoption and results stress internal and external considerations for BIM adoption.

Harnessing Extreme Internal Damping in Polyrotaxane‐Incorporated Liquid Crystal Elastomers for Pressure‐Sensitive Adhesives

AbstractLiquid crystal elastomers (LCEs) exhibit extraordinary energy dissipation due to their unique viscoelastic response, resulting from the rotation of mesogens under mechanical stress. While recent studies demonstrate the LCE‐based pressure‐sensitive adhesives (PSAs) by exploiting the enhanced damping, all previous studies have focused on LCEs with covalent crosslinks. Here, a new class of PSAs is developed by integrating movable polyrotaxane crosslinkers into an LCE matrix (PRx‐LCEs). Dynamic viscoelastic measurements reveal that PRx‐LCE exhibits a remarkably high energy dissipation, as indicated by a large tan δ. Interestingly, the secondary tan δ peak associated with LCE damping is more pronounced than the primary peak of the glass transition. The exceptional energy dissipation in PRx‐LCE results in superior adhesion strength (≈1864 N m−1), which is 3.5 times higher than conventional LCEs and 13 times higher than commercial PSAs in the peel test. Additionally, PRx‐LCEs demonstrate thermally reversible adhesion, enabling clean removal at elevated temperatures. Furthermore, the sliding effect in PRx‐LCE enhances both deformability and stress relaxation under load, resulting in deeper indentation, and superior adhesion during the probe tack test. The combination of LCE and slidable crosslinks provides robust and switchable adhesion, making them promising for applications in biomedical engineering, display, and semiconductor industries.

Numerical Computation and Statistical Interpretations of Heat Transfer of Tween-20/ethyl Acetate Nanofluid Flow with Melting Rheological Quality and Activation Energy

Tween-20 plays a significant role in the biological, food, and pharmaceutical industries. Additionally, it plays a vital role in improving the quality of reverse mechanisms of multi-drug resistance. This paper uses artificial neural computing to examine Tween-20 nanoparticles’ behaviors in a base fluid called ethyl acetate to enhance the applications in nanomedicine, drug delivery and biotechnology. The study focuses on the nonlinear model of a viscous fluid at the stagnation point, where mixed convection processes and activation energy are present. The study incorporates slip velocity and melting boundary conditions to examine heat and mass transfer, taking into account thermal and solutal stratification. Various fields of research and technology, specially in industrial engineering that include hydrodynamics, panto-graph systems, and biomedical mathematics, extensively utilize artificial computing. The datasets are based on velocity, temperature, and concentration outlines. The fourth-order Runge-Kutta method is used to generate the datasets. To validate the LMM-ABNNs, we have compared them with a numerical solution, showing a high level of agreement. We utilize the error histogram and mean square error results to validate the performance, scrutinize the training and testing methods, and explore the validity of the approximate answer. Furthermore, the quality characteristics such as skin friction, rate of heat, and mass movement (Nusselt and Sherwood numbers) are statistically analyzed to forecast the model’s durability. This article is the best example of investigating different fluid parameters with the latest artificial neural computing.

Transdisciplinary education for reducing environmental impact of AEC industry

The Architecture, Engineering, and Construction (AEC) industry plays a crucial role in shaping the built environment but contributes significantly to environmental impact, accounting for nearly 40% of global energy-related carbon dioxide emissions. An interdisciplinary systematic literature review (SLR) explores how transdisciplinary education (TDE) can be integrated into AEC curricula to address environmental sustainability challenges. TDE facilitates students’ understanding of the environmental impact (EI) of their design projects and equips them with the knowledge and skills to develop sustainable solutions. This review examines various pedagogical models, including problem-based learning, to evaluate their effectiveness in fostering TDE for reducing EI in the AEC sector. Findings from the literature are synthesized to present a framework aligned with sustainable development goals (SDGs) that promotes sustainability in AEC education. The review aims to inform future research and curriculum development, advancing the role of education in equipping future professionals to mitigate environmental impacts in the AEC industry.

Effects of hybrid work on psychological detachment

AbstractAfter the coronavirus pandemic, hybrid work has become firmly established in many companies. This study investigated the relation of hybrid work and relevant working conditions with the psychological detachment of employees. A written online survey was conducted among 250 employees of a company in the mechanical engineering industry in Germany who worked from home at least part of the time. Multiple hierarchical regression was used for the analysis. The results show that, as expected, the quality of manager-employee communication has a positive (β = 0.13, p < 0.05) and extended work availability a negative effect (β = −0.35, p < 0.001) on psychological detachment. Contrary to the assumptions, however, the proportion of work from home has a positive effect on detachment (β = 0.15, p < 0.05). Regarding the demographic variables, female employees are better able to detach from work (β = −0.36, p < 0.01). The quantitative demands initially show a significant effect, but this disappears as soon as the extended work availability and the proportion of work from home are included in the analysis. The included predictors explain 19% (p < 0.001) of the total variance.Practical Relevance Hybrid work offers the opportunity to organize work more flexibly and thus to adapt it more closely to the needs of employees. The results of this study show that work from home can foster psychological detachment from work, provided that health-promoting working conditions are in place.

Characteristics of pollutants emitted by motor vehicles and their impact on the environment and engine operation

Gaseous and solid pollutants (dusts) of atmospheric air have been defined. Dusts have been divided according to various criteria and their properties have been given. The sources and characteristics of anthropogenic and natural pollutants of atmospheric air have been presented. It has been shown that the main sources of anthropogenic pollutants, apart from industry, are motorization, and internal combustion engines of cars are a source of gaseous pollutants and solid particles, the emission of which has been significantly reduced. The originality of the article consists in conducting an extensive literature analysis and proving that the emission of "non-engine" pollutants in the form of dust from the wear of friction linings of brakes, clutches and from the wear of tires and roads, as well as mineral dust raised from the ground, is currently a greater threat to human health and the environment than engine emissions. The impact of particulate matter emissions from road transport and mineral dust, which is the basic component of road dust, on human health, vegetation and on the operation of engine and vehicle systems is presented.

Femtosecond laser-inscribed in-fiber Mach-Zehnder interferometer for ultra-sensitive small-angle torsion measurement

We propose and demonstrate a novel in-fiber Mach-Zehnder interferometer (MZI), using a femtosecond (FS) laser to inscribe two parallel straight waveguides along both sides of the core-cladding boundary of an optical fiber. Both simulations and experiments demonstrated that constructing a second straight waveguide (WG2) on the opposite side of the fiber core significantly enhances the mode coupling efficiency in fiber, resulting in ultra-sensitive small twist-angle torsion measurement by intensity interrogation. By finely adjusting the waveguide spacing between the WG2 and the fiber core during the FS fabrication process, a large tuning range of ∼ 200 nm for the designable interference wavelength with maximal fringe contrast has been achieved. Through comparative experiments, the proposed in-fiber MZI performs ultra-sensitivity with 17417.92 dB/(rad/mm) over small twist angles from −10° to 0° for the sample “WG1-4/WG2-4” and 19480.57 dB/(rad/mm) over small twist angles from 0° to 10° for the sample “WG1-4/WG2-6”. Besides, the device is insensitive to temperature and strain. The exhibited advantages of high sensitivity, high reproducibility, and low crosstalks promote the proposed in-fiber MZI to be applied in future industrial engineering monitoring and intelligent health monitoring.

Using generative neural networks when training digital engineers to improve the quality of their training

The problem and the aim of the study. Modernized higher education provides great prospects for artificial intelligence technologies to be introduced when training engineers for knowledge-intensive and high-tech industries. Applying generative neural networks makes it possible to meet the requirements both for programs to train relevant specialists and for the level of developed skills key to Industry 4.0 and for further Industry 5.0. The purpose of the study is to identify the possibilities of using generative neural networks when training digital engineers to improve the quality of their professional training. Research methods. The authors consider the generative neural network as an open resource with which it is possible to design, individualize and fill educational content in accordance with the professional interests of the participants in the interaction. The choice of service is justified by the type of information being processed and the engineer's work function being implemented. 68 bachelors of Vyatka State University are involved (Russian Federation). Training program: 08.03.01 – Construction. Focus: hydraulic engineering, industrial and civil engineering, Digital technologies for the examination of construction sites and real estate management. The assessment of the quality (level) of training is carried out with the help of materials from the fund of complex qualification tasks. For statistical processing of data, Pearson's chi-squared test is used. Results. The directions of work of students of the experimental group studying the discipline "Information Technology", including interactive communication supported by neural networks: generation of images and 3D-models, search for original titles, selection of a list of references, construction of diagrams and algorithms, etc. Statistically significant differences were revealed in the changes that occurred in the pedagogical system (χ2 = 9.095; p < 0.05). Conclusion. The summary are formulated that neural network services contribute to improving the quality of training due to the following didactic capabilities: presentation of information in various forms, automation of calculations, analysis of large amounts of data, decision support, etc. Difficulties are also highlighted: constantly updated features of services, the English-language interface, compliance with copyright and ethics of interaction on the network

Significance of heat transfer in a reversible esterification of Arrhenius activation energy with partial slip

The significance of heat transfer during a reversible esterification process in a magnetohydrodynamic boundary layer Casson fluid flow along a vertical stretching plate is examined. The multi-slip conditions are considered in a porous medium. The presence of chemical process requiring an activation energy is considered in the analysis. The study also investigates the hydromagnetic boundary layer Casson fluid flow alongwith partial slip conditions across a vertical stretching plate. The incorporation of multi-slip constraints in a porous medium, alongside magnetic fields and other parameters, highlights its relevance in diverse engineering fields such as thermal engineering, polymerization, and biodiesel industries. Understanding the characteristics of such fluids under complex conditions is vital for optimizing heat and mass transfer in industrial applications, making this investigation timely and valuable. The nonlinear differential set of equations are solved numerically involving Runge Kutta based shooting approach of fourth order and the results are verified with the bvp4c tool and the findings are explored using graphical plots. The predominance of significant factors on flow configurations are analyzed and presented in graphs and tables. A comprehensive analysis is provided on the effects on velocity, concentration, and temperature of diverse parameters such as reaction rate constant, magnetic parameter, suction parameter, mass Grashof number, Prandtl number, Casson parameter, thermal radiation parameter and slip parameters. The tabular representation of the adverse effects of drag coefficient, rate of mass transfer and Nusselt number on flow configurations for various significant parameters is presented. It is inferred that for the case of reversible and irreversible flows, the shear stress rate escalates by 29% when the magnetic parameter elevates from 0.5 to 1.5 and about 35% when the Casson parameter elevates from 0.5 to 1.5. For the suction parameter, the coefficient of drag increased by 27% and 26% for irreversible and reversible flows respectively. When the reaction rate increases from 0.5 to 1.5, the rate of shear stress elevates by 0.5% and 0.02% for irreversible and reversible flows in order. The Nusselt number decreased about 7% and 8% when the magnetic parameter and Casson parameter rises from 0.5 to 1.5 respectively, for irreversible and reversible flows. It is noteworthy that the previous studies are in precise agreement with the present investigation.

A novel many-objective symbiotic organism search algorithm for industrial engineering problems

A novel many-objective symbiotic organism search algorithm for industrial engineering problems

Interdisciplinary Perspectives on Agent-Based Modeling in the Architecture, Engineering, and Construction Industry: A Comprehensive Review

This paper explores the transformative impact of agent-based modeling (ABM) on the architecture, engineering, and construction (AEC) industry, highlighting its indispensable role in revolutionizing project management, construction processes, safety protocols, and sustainability initiatives including energy optimization and occupants’ comfort. Through an in-depth review of 178 documents published between 1970 and 2024 on current practices and the integration of ABM with emerging digital technologies, this study underscores the critical importance of ABM in facilitating enhanced decision-making, resource optimization, and complex system simulations. For instance, ABM is shown to reduce project delays by up to 15% through enhanced resource allocation and improve safety outcomes by simulating worker behavior and identifying potential hazards in dynamic construction environments. The results reveal ABM’s potential to significantly improve construction methodologies, integrate technological advancements seamlessly, and contribute to the development of sustainable and resilient building practices. Furthermore, this paper identifies key areas for future research, including the exploration of ABM’s capabilities in conjunction with other digital innovations to unlock new avenues for efficiency and sustainability in construction. This study sets out a forward-looking agenda for providing this modeling approach to address contemporary challenges and harness opportunities for innovation and growth in the AEC sector.

Review Paper on Vibration Damping System Using Pendulum

Abstract: This paper explores the implementation and effectiveness of vibration damping systems utilizing pendulums, focusing on their ability to mitigate unwanted oscillations in various structures. Pendulum-based dampers operate on the principles of inertia and resonance, allowing them to counteract vibrations causedby dynamic loads, seismic events, and machinery operations. The study examines different configurations, including tuned mass dampers (TMDs) and nonlinear pendulum systems, highlighting their design, functionality,and specific applications in fields such as civil engineering, aerospace, and automotive industries. Through analytical modeling and experimental validation, the paper demonstrates the effectiveness of pendulum dampers in enhancing structural stability and occupant comfort. The results indicate significant reductions in vibration amplitude, showcasing the potential of these systems to improve the longevity andsafety of infrastructure. Furthermore, the discussion addresses the advantages of pendulum-based systems over conventional damping methods, emphasizing their adaptability and efficiency.In conclusion, this study underscores the importance of pendulum vibration damping systems as a viable solution for contemporary engineering challenges, paving the way for future research and development in vibration control technologies.

A Systematic Review of the Digital Twin Technology in Buildings, Landscape and Urban Environment from 2018 to 2024

Digital Twin (DT) technologies have demonstrated a positive impact across various stages of the Architecture, Engineering, and Construction (AEC) industry. Nevertheless, the industry has been slow to undergo digital transformation. The paper utilizes the Systematic Literature Review (SLR) approach to study a total of 842 papers on the application of DT in buildings, landscapes, and urban environments (BLU) from 2018 to 2024. Based on the research results, suggestions have been made for future research and practical directions. Meanwhile, it provides assistance to BLU’s designers, constructors, managers, and policymakers in establishing their understanding of the digital transformation of the AEC industry. The existing relevant research can be mainly divided into three categories: case study, framework study, and technology study. Compared with the buildings and urban environment industries, the number and depth of research in the landscape industry are relatively low. Through in-depth analysis of BLU projects, three research trends in the future are determined: (1) research and application of DT framework in the design and planning stage; (2) development of design tools and basic theory based on DT model; (3) application and exploration of DT technology in the landscape industry.

Development, implementation, and evaluation of a 3D‐printed high‐fidelity pediatric mannequin with expected hard‐to‐intubate airway

AbstractClinical simulation is fundamental for the healthcare staff to learn and enhance their procedural skills without causing harm to the patients. Despite its importance, in literature appears a deficiency of pediatric pathological mannequins, especially those simulating difficult airway management due to the obstruction of the passage of tubes, fiberscopes, or catheters. Given the importance of simulating complex scenarios in the medical staff's training, the authors decided to realize a modular high‐fidelity pathological mannequin with nasal access using reverse engineering and additive manufacturing techniques within T3Ddy, a joint laboratory between Meyer Children's Hospital of Florence and the Department of Industrial Engineering of the University of Florence. The mannequin is developed from diagnostic images of a significant 30‐month‐old polymalformative patient also affected by Pierre‐Robin syndrome modifying the tracheobronchial tree to reproduce an abnormal status. Rigid parts and silicone cast molds are manufactured using 3D‐printed ABS/ASA while platinum‐cure‐silicones are used for the soft ones. Meyer's anesthesiologists collaborated to the realization providing feedback during design and production. The device is evaluated with a 5‐point Likert scale questionnaire and results in a useful tool for the training of procedural skills related to difficult intubation as its realism, anatomical geometry, and tactic feedback are positively evaluated.

An Analysis of Critical Processes and Employee Challenges at Velmurugan Heavy Engineering Industries (VHEI) Private Limited, Thanjavur

This study investigates the critical processes and challenges faced by employees at Velmurugan Heavy Engineering Pvt. Ltd., located in Sengipatti, Thanjavur. The aim is to identify areas for improvement and provide recommendations to enhance employee performance and organizational effectiveness. Employing a mixed-method approach combining qualitative and quantitative methods, data were collected extensively through surveys, interviews, and observations from a representative sample across departments and hierarchical levels. The findings highlight critical areas such as project management, quality control, communication channels, and performance evaluation. While the organization demonstrates competence in these areas, gaps exist, particularly in streamlined communication and efficient project management. The study also uncovers challenges including unclear role expectations, limited professional development opportunities, inadequate work-life balance, and insufficient recognition and rewards. Recommendations are offered to strengthen communication channels, enhance project management practices, implement employee development programs, promote work-life balance initiatives, and establish a robust recognition and rewards system. Addressing these challenges and implementing recommended strategies has led to improved employee satisfaction, productivity, and overall organizational performance. This study provides valuable insights for Velmurugan Heavy Engineering Pvt. Ltd. to cultivate a positive work environment and foster a highly engaged workforce, contributing to sustainable growth and success

Application of Building Information Modelling in Construction and Demolition Waste Management: Systematic Review and Future Trends Supported by a Conceptual Framework

The architecture, engineering, construction, and operations industry faces an urgent need to enhance construction and demolition waste management in urban areas, driven by increasing demolition and construction activities and a desire to align with sustainable practices and the circular economy principles. To address this need, a systematic literature review on the building information modelling methodology was conducted, employing a structured protocol and specific tools for the analysis of academic studies, based on PRISMA guidelines and StArt software (version 3.4 BETA). Ninety relevant studies published between 1998 and 2024, were analysed and selected from the Web of Science, Scopus, and Engineering Village databases. Findings indicate that China leads in publications with 34%, followed by Brazil (8%) and the United Kingdom (7%). The analysis emphasises the use of drones and LiDAR scanners for precise spatial data, processed by 3D reconstruction tools like Pix4D and FARO As-Built. Revit excels in 3D modelling, providing a robust platform for visualisation and analysis. Visual programming tools such as Dynamo automate processes and optimise material reuse. The study presents a conceptual framework that integrates these technologies with the principles of the circular economy, clarifying the interactions and practical applications that promote the sustainable management of demolition waste from urban buildings and process efficiency. Although the approach promotes material reuse and sustainability, it still faces barriers such as the need for waste segregation at the source, the adaptation of innovative technologies, like the iPhone 15 Pro LiDAR and thermal cameras, as well as associated costs. These factors may limit its adoption in larger-scale projects, particularly due to the increased complexity of buildings.