Production Engineering Research Articles

Teaching People Management in Production Engineering Courses

Objective: The aim of the study was to analyze how courses related to personnel management and human resources are offered in Production Engineering programs at universities in the state of São Paulo. Theoretical Framework: The investigation encompasses the emergence of production engineering as a science and field of study, followed by a section on the practice of personnel management as a field of interest. Finally, it presents a topic related to the competencies required of production engineers in the job market. Method: The adopted methodology is qualitative, exploratory, descriptive, and methodological in nature, utilizing bibliographic research as the main procedure. The research methods include literature review to technically support the work, documentary research related to selected educational institutions, and a case study. Results and Discussion: Literature review results indicate that the historical evolution of Production Engineering and Personnel Management education reveals an adaptation to market demands, influenced by industrial and economic movements over time. The interdependence between these two areas reflects a strategy to address the challenges of emerging scenarios, highlighting a continuous pursuit of innovation, quality, and efficiency. Case study results reveal that some institutions offer a significant number of courses focused on personnel management. However, the credit allocation for these courses is generally low. Regarding the diversity between mandatory and elective courses, there is a balance, with a slight predominance in elective course offerings. Research Implications: The research aims to expand and enrich the theoretical framework related to production engineering and personnel management, with the goal of encouraging future research on the topics addressed at different levels of education. Originality/Value: The study initiates a discussion on the importance of production engineers in organizations and the relevance of personnel management practice in the workplace, as well as investigating the intersection of these two areas in professional development.

Modeling wax disappearance temperature using robust white-box machine learning

Wax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.

Blending biomass-based liquid biofuels for a circular economy: Measuring and predicting density for biodiesel and hydrocarbon mixtures at high pressures and temperatures by machine learning approach

Circular economy is an efficient approach to deal with the rapidly increasing demand for energy and its environmental impact. It involves switching to renewable and sustainable energy sources. By providing an alternative to traditional energy resources, biodiesel promotes a circular economy paradigm that embraces renewable energies rather than fossil fuels. As an alternative to fossil fuels, liquid biodiesel helps to improve energy efficiency and reduce pollution. Furthermore, blending biodiesel has proven to be economically and environmentally viable. Knowledge of the thermodynamic properties of biodiesel, such as densities and coefficients of expansivity and compressibility, plays an important role in understanding and determining the behavior of the fuel under different operating conditions. The blend densities of Soybean Oil Biodiesel (SOB) with hexane and Waste Cooking Oil Biodiesel (WCOB) with heptane in four volume ratios were experimentally studied. An Anton Paar vibrating tube densimeter HPM DMA has been used for accurate measurements of densities of the biofuels and their blends. Density measurements were performed at temperatures ranging from 298.15 K to 393.15 K, and pressures between 0.1 MPa and 140 MPa. This work also aims to develop accurate prediction models of the properties of biodiesel blends under various conditions, such as changes in temperature and pressure, which are essential for optimizing energy production processes and engine performance. In this context, the dataset was first investigated with Tait Equation of State (Eos) to determine thermodynamic parameters which including isothermal compressibility and isobaric expansivity. It showed generally low standard deviations. Furthermore, these new experimental densities were also modeled with Artificial Neural Network (ANN) as a machine learning method. This paper models proved that the machine learning method is a suitable tool to model the thermo-physical characteristics of liquid fuels. The findings of this study have the potential to provide valuable guidance to engine engineering applications developers regarding operating conditions and the selection of suitable biodiesel for their system.

Innovative Tools for Creative Metal-Cutting

Arno Werkzeuge is tool partner to subcontract manufacturer Otto Beckert, who services customers in the motorsport, medical engineering, drilling platform and chip production industries

Gene therapy for retinal diseases: From genetics to treatment.

The gene therapy approach for retinal disorders has been considered largely over the last decade owing to the favorable outcomes of the US Food and Drug Administration-approved commercial gene therapy, Luxturna. Technological advances in recent years, such as next-generation sequencing, research in molecular pathogenesis of retinal disorders, and precise correlations with their clinical phenotypes, have contributed to the progress of gene therapies for various diseases worldwide, and more recently in India as well. Thus, considerable research is being conducted for the right choice of vectors, transgene engineering, and accessible and cost-effective large-scale vector production. Many retinal disease-specific clinical trials are presently being conducted, thereby necessitating the collation of such information as a ready reference for the scientific and clinical community. In this article, we present an overview of existing gene therapy research, which is derived from an extensive search across PubMed, Google Scholar, and clinicaltrials.gov sources. This contributes to prime the understanding of basic aspects of this cutting-edge technology and information regarding current clinical trials across many different conditions. This information will provide a comprehensive evaluation of therapies in existing use/research for personalized treatment approaches in retinal disorders.

A multi-scale attention mechanism for detecting defects in leather fabrics

Defect detection is critical to industrial quality control in leather production engineering. The various sizes and locations of defects in leather, as well as significant differences within the same class and indistinctive variations between different classes of defects, contribute to the complexity of the problem. To address this challenge, we propose a Multi-Layer Residual Convolutional Attention (MLRCA) approach. MLRCA enhances its ability to capture both intra-class and inter-class differences by enhancing the semantic feature representation in the backbone network. To improve multiscale fusion effects, we also incorporate the MLRCA module into the feature pyramid network (FPN) and propose a new multi-layer residual convolution attention feature pyramid network (ML-FPN). This approach enables more accurate identification of leather defects at a more detailed level by selectively capturing contextual information from different domains. We then implement the Side-Aware Boundary Localization (SABL) detection head, which accurately locates defects and helps the network distinguish between similar defect categories for more precise positioning. To validate the effectiveness of our approach, we conducted ablation experiments on the created leather dataset. Comparative experiments demonstrate the excellent capability of our model to detect minor defects. The model achieved 83.4, 89.7, and 85.6 for the AP, AP50, and AP75 evaluation metrics. In addition, the model achieves 71.3, 89.9, and 88.9 for APS, APM, and APL. Our approach has been confirmed feasible through experimentation and provides new insights for automated leather defect detection methods.

Assessing the Consumer Properties of Engineering Products

Assessing the Consumer Properties of Engineering Products

Immunophenotyping of a population of cultured human umbilical cord cells from Wharton's jelly

BACKGROUND: Against the background of many existing methods of defect replacement in post-traumatic injuries the methods of repair of damaged tissues based on methods using products of tissue engineering and cultures of artificially cultured human cells are becoming more and more widespread in medical practice. The literature reports weak immunogenic activity of human umbilical cord tissues, which makes these cells promising components of regenerative medicine products. Due to possible errors in explant selection and cell transformation in the process of cultivation, it is necessary to reliably determine the phenotype of cells obtained as a result of tissue explantation and their further cultivation. Thus, the specificity of obtaining multipotent mesenchymal cells from human umbilical cord Warton's stool tissues requires reliable identification of the type of the obtained cells. AIM: To obtain reliable features of mesenchymal stem cells in the cell population obtained from human umbilical cord stool tissue. MATERIAL AND METHODS: Methods of cell cultivation, flow cytofluorimetry, immunocytochemistry for determination of surface and intracellular markers of mesenchymal stem cells were used in the study. RESULTS: In the course of work on the identification of cells of the population obtained by culturing explants from human umbilical cord Wharton stool, the heterogeneity of the type of cells constituting the cell population was established. Most of them are mesenchymal stem cells carrying fluorescent markers CD45, CD73, CD34, CD29, CD90, CD44, CD105, which agrees with the immunophenotype of mesenchymal stem cells defined by the International Society for Cell Therapy. The ratio of the applied markers allows us to refer the population of cells obtained by direct explantation of Varton's gelatin tissue fragments to mesenchymal stem cells. Visual control confirmed the localisation of labelled antibodies on the surface of cultured cells. And also, it was shown that there were no vascular muscle cells in the obtained culture. CONCLUSION: As a result of experiments on identification of the cells obtained during explantation of Wharton's jelly tissue fragments and their further cultivation, their belonging to mesenchymal stem cells was established by immunofluorescence cytophotometry.

Создание и развитие высоконадежных информационно-управляющих систем с элементами искусственного интеллекта для перспективных технологических комплексов

Methods and means of improving complex technological equipment are shown through the operational assessment of the quality of implemented technologies realtime. This is proved by the fact that implementing technological operations on universal equipment each time requires modeling dynamic processes and taking into account a large number of uncertainty factors that affect the geometry generation quality. It is not possible to be a priori aware of these factors. There is a need to create new information technologies with the possibilities of universal application for immediate understanding of various dynamic processes in diagnostic, identification and control systems. Standard computer systems for statistical analysis and optimization of dynamic processes with the possibilities of universal application for various implementations of modern technologies have been introduced. The possibility of using integrative criteria and methods of artificial intelligence for diagnostic systems, identification and control of advanced technological complexes is shown. The implementation of information systems for the management of complex objects of various technological purposes is presented. The proposed modeling methods and approaches have been tested at various machine-building enterprises when processing parts on turning, milling and grinding machines, both universal and CNC. The research results made it possible to implement new principles of automated control and optimal adjustment of technological processes in real time and create an automated system for evaluating their quality, which allows increasing the efficiency and reliability of management decisions by conducting optimization directly on operating equipment. Based on the methods and approaches described above, new results have been obtained in the implementation of plasma technologies for the modification of geometrically complex surfaces of mechanical engineering products aimed at increasing wear resistance, hardness and other technical characteristics of the working surfaces of precision engineering products. A fairly complete approbation of methods, approaches, procedures and decision-making criteria for various technologies allows them to be recommended for universal applicability.

Description and Utilization of an Educational Platform for Clean Production in Mechanical Engineering

This study introduces a novel educational mentoring platform that emphasises clean manufacturing techniques and is intended to support engineering education and mentorship. The mentoring platform provides practical, project-based learning opportunities in an educational environment. The platform's foundation lies in a mentoring approach through conducting sessions and defined projects. This paper will outline the primary components of a new educational mentoring platform and highlight some of its most important use cases. The mentoring platform enables the creation of programs like the Sustainable Manufacturing Innovation Program, which is showcased in this paper, equipping students with the knowledge to enhance sustainability in mechanical engineering and foster cleaner production startups. The paper briefly overviews the Novel Educational Methodology (NEM) integrated into an e-learning platform for personalized and adaptive learning materials and courses. The combined use of e-learning and mentoring platforms represents a comprehensive educational system, enhancing developmental outcomes while promoting and achieving clean production practices. Both platforms, e-learning and mentoring, are highly adaptable, facilitating the creation of diverse online course materials and mentoring programs across various scientific disciplines.

Organic Combination of New Quality Productivity and Land Engineering

Organic Combination of New Quality Productivity and Land Engineering

Actual Problems of Creating Digital Twins of Machine Engineering Products in Terms of Durability Assessment

Actual Problems of Creating Digital Twins of Machine Engineering Products in Terms of Durability Assessment

What is Mechanical Engineering?

Abstract In a World of Diverse Challenges, Mechanical Engineers are Developing the Solutions. Their Contributions Have Never Been as Valuable As They are Today. “Almost everyone comes into contact with the products of mechanical engineering on a daily basis, but very few people—including some engineers themselves—can provide a succinct explanation of what mechanical engineering is. ”

An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production

Purpose: E-waste management (EWM) refers to the operation management of discarded electronic devices, a challenge exacerbated due to overindulgent urbanization. The main purpose of this paper is to amalgamate production engineering, statistical methods, mathematical modelling, supported with Machine Learning to develop a dynamic e-waste supply chain model. Method Used: This article presents a multidimensional, cost function-based analysis of the EWM framework structured on three modules including environmental, economic, and social uncertainties in material recovery from an e-waste (MREW) plant, including the production–delivery–utilization process. Each module is ranked using Machine Learning (ML) protocols—Analytical Hierarchical Process (AHP) and combined AHP-Principal Component Analysis (PCA). Findings: This model identifies and probabilistically ranks two key sustainability contributors to the EWM supply chain: energy consumption and carbon dioxide emission. Additionally, the precise time window of 400–600 days from the start of the operation is identified for policy resurrection. Novelty: Ours is a data-intensive model that is founded on sustainable product designing in line with SDG requirements. The combined AHP-PCA consistently outperformed traditional statistical tools, and is the second novelty. Model ratification using real e-waste plant data is the third novelty. Implications: The Machine Learning framework embeds a powerful probabilistic prediction algorithm based on data-based decision making in future e-waste sustained roadmaps.

Development of novel antimicrobials with engineered endolysin LysECD7-SMAP to combat Gram-negative bacterial infections

BackgroundAmong the non-traditional antibacterial agents in development, only a few targets critical Gram-negative bacteria such as carbapenem-resistant Pseudomonas aeruginosa, Acinetobacter baumannii or cephalosporin-resistant Enterobacteriaceae. Endolysins and their genetically modified versions meet the World Health Organization criteria for innovation, have a novel mode of antibacterial action, no known bacterial cross-resistance, and are being intensively studied for application against Gram-negative pathogens.MethodsThe study presents a multidisciplinary approach, including genetic engineering of LysECD7-SMAP and production of recombinant endolysin, its analysis by crystal structure solution following molecular dynamics simulations and evaluation of antibacterial properties. Two types of antimicrobial dosage forms were formulated, resulting in lyophilized powder for injection and hydroxyethylcellulose gel for topical administration. Their efficacy was estimated in the treatment of sepsis, and pneumonia models in BALB/c mice, diabetes-associated wound infection in the leptin receptor-deficient db/db mice and infected burn wounds in rats.ResultsIn this work, we investigate the application strategies of the engineered endolysin LysECD7-SMAP and its dosage forms evaluated in preclinical studies. The catalytic domain of the enzyme shares the conserved structure of endopeptidases containing a putative antimicrobial peptide at the C-terminus of polypeptide chain. The activity of endolysins has been demonstrated against a range of pathogens, such as Klebsiella pneumoniae, A. baumannii, P. aeruginosa, Staphylococcus haemolyticus, Achromobacter spp, Burkholderia cepacia complex and Haemophylus influenzae, including those with multidrug resistance. The efficacy of candidate dosage forms has been confirmed in in vivo studies. Some aspects of the interaction of LysECD7-SMAP with cell wall molecular targets are also discussed.ConclusionsOur studies demonstrate the potential of LysECD7-SMAP therapeutics for the systemic or topical treatment of infectious diseases caused by susceptible Gram-negative bacterial species and are critical to proceed LysECD7-SMAP-based antimicrobials trials to advanced stages.

Development & Validation of a Packed Bed Thermal Energy Storage Model

The rapid growth of economies and population around the world have exponentially increased the demand for energy. This demand has invoked increased annual global CO2 emissions and necessitated a transition to clean energy technologies. Reliable and affordable energy storage technologies are paramount for increasing renewable energy penetration onto the grid, supporting periods with reduced or no renewable energy generation. This project is aimed at developing a Thermal Energy Storage (TES) solution that can deliver heat to a heat engine for power production or to an industrial process for prolonged periods. TES long duration energy storage (LDES) presents many potential benefits, including 1) Low Cost 2) scalability 3) high energy density 4) low carbon footprint and 5) resiliency via ability to produce synchronous power (i.e., spinning turbomachinery). The broader objective of this project is to build, test, and model the TES concept at a 2 MWh scale to determine the economic and physics-based practicality of the system. The work presented here describes the first phase objectives of the project, including benchtop scale testing, model development, and Model Validation.

Unraveling the mystery of sandwich structure: Emerging photocatalytic hydrogen production by g-C3N5 coupled Nb2C MXene forming Schottky heterojunction

The development of new photocatalysts and photocatalytic systems is the essence of the development of photocatalytic hydrogen production engineering. Nitrogen-rich graphite carbon nitride (g-C3N5) has gradually replaced conventional graphite phase carbon nitride (g-C3N4) with a smaller bandgap and high-efficiency visible light utilization. In this study, g-C3N5 and Nb2C MXene coupled composition with sandwich structure were manufactured by a simple ultrasonic magnetic stirring method and used for photocatalytic hydrogen production. By adjusting the ratios of single components in the system, the hydrogen production rate of the optimal sample 4:1g-C3N5/Nb2C under visible light irradiation is 2529.5 μmol·g−1·h−1, which is 21 times higher than that of g-C3N5. The high apparent quantum yield (AQY) value of 17.3 % also proves a high light utilization rate. The atomic force microscopy (AFM) technique reveals variations in the thickness of the samples, while strongly demonstrating the formation of sandwich structure. In addition, X-ray photoelectron spectroscopy (XPS) analysis further reveals the electron transfer paths within the system, preliminarily verifying the formation of Schottky heterojunctions. Kelvin probe force microscopy (KPFM) tests not only re-validate the correctness of the electron transfer path but also demonstrate the accuracy of the energy bands. The density-functional theory (DFT) calculations corroborate that Nb2C MXene plays the role of a photoco-catalyst to form Schottky heterojunctions with g-C3N5 for efficient carrier separation and migration. This work highlights the key role of sandwich structure photocatalytic systems on photocatalytic performance.

Influence of Gelatin on Adhesion, Proliferation, and Adipogenic Differentiation of Adipose Tissue-Derived Stem Cells Cultured on Soy Protein-Agarose Scaffolds.

Scaffolds play a key role in cultured meat production by providing an optimal environment for efficient cell attachment, growth, and development. This study investigated the effects of gelatin coating on the adhesion, proliferation, and adipogenic differentiation of adipose tissue-derived stem cells (ADSCs) cultured on soy protein-agarose scaffolds. Gelatin-coated scaffolds were prepared using 0.5% and 1.0% (w/v) gelatin solutions. The microstructure, water absorption rate, mechanical strength, cytotoxicity, cell adhesion, proliferation, and differentiation capabilities of the scaffolds were analyzed. Field emission scanning electron microscopy revealed the porous microstructure of the scaffolds, which was suitable for cell growth. Gelatin-coated scaffolds exhibited a significantly higher water absorption rate than that of non-coated scaffolds, indicating increased hydrophilicity. In addition, gelatin coating increased the mechanical strength of the scaffolds. Gelatin coating did not show cytotoxicity but significantly enhanced cell adhesion and proliferation. The gene expression levels of peroxisome proliferator-activated receptor gamma, CCAT/enhancer-binding protein alpha, and fatty acid-binding protein 4 were upregulated, and lipid accumulation was increased by gelatin coating. These findings suggest that gelatin-coated scaffolds provide a supportive microenvironment for ADSC growth and differentiation, highlighting their potential as a strategy for the improvement of cultured meat production and adipose tissue engineering.

Addressing Circularity Strategies by Reconfiguring Smart Products during Their Lifecycle

Considering circularity aspects during the engineering of smart products implies a significantly increased complexity of engineering processes. Especially technical reconfiguration of smart products, offered as services in availability-oriented business models, enables the integration of circular economy aspects in sustainable products and lifecycles, through realizing several aspects of the 9R strategies. This paper introduces an analysis of interdependencies between 9R strategies potential reconfiguration options, technical characteristics of smart products, different maturity levels of smart product and abilities for circularity. Partial engineering models managed in different product lifecycle management systems provide a technical basis for systematization and evaluation of circular abilities of reconfigurable smart products in different lifecycle phases. The approach aims to improve circularity-related decision making in systems engineering processes in the early development phases and during the reconfiguration of smart products during utilization phase. An industrial use case considering a microelectronic-centered smart product used in e-mobility solutions validates the approach.

Multifunctional wood-bamboo hybrid materials for effective electromagnetic interference shielding

With the emergence of industries such as custom home furnishing and prefabricated construction, the demand for functional engineered wood products is increasing. However, traditional engineered wood products have low strength and limited functionality. To address concerns about electromagnetic radiation and other safety hazards in household environments, we developed a multifunctional material with a wood-bamboo multilayer structure, where a bamboo layer with excellent electromagnetic shielding and mechanical properties is bonded to the wood surface. Uniform integration of copper nanoparticles endows the bamboo layer with excellent electromagnetic interference (EMI, 40 dB), shielding effectiveness (SE) and good hydrophobic properties. The wood-bamboo hybrid materials demonstrate excellent flexural strength (70.1 MPa) and flame-retardant performance (self-extinguishing within 10 seconds after ignition). The potential applications of wood-bamboo hybrid materials can extend to various fields such as construction engineering, building decoration and electronic products.