Cost-efficient Process Research Articles

Impedance measurement of rolling bearings using an unbalanced AC wheatstone bridge

Industry 4.0 drives the demand for cost-efficient and reliable process data and condition monitoring. Therefore, visualizing the state of tribological contacts becomes important, as they are regularly found in the center of many applications. Utilizing rolling element bearings as sensors and monitoring their health by the electrical impedance method are promising approaches as it allows, e.g., load sensing and detection of bearing failures. The impedance cannot be measured directly, but there are various methods available. This paper discusses advantages and disadvantages and suggests the AC Wheatstone bridge as a reliable way of measuring impedances with low phase angles at sampling rates in the kHz range. The corresponding equations are introduced, a simulation built, an uncertainty mode and effects analysis carried out and sample measurement results of real rolling elements shown. It can be demonstrated that the AC Wheatstone bridge meets the proposed requirements for sensory utilization and condition monitoring when the bearing is operated in the hydrodynamic regime.

Valorization of soybean by-products for sustainable waste processing with health benefits.

Soybean is a rapidly growing agricultural crop, fueled by the rising global demand for animal feed, plant-based proteins and essential nutrients for human consumption. Soybeans contain a wide range of essential nutrients that are vital for health and may play a significant role in disease prevention. Their nutritious composition has led to a diverse range of soy-based foods and derivatives available on the market. A substantial amount of soybeans is allocated to the animal feed sector, human consumption, nutraceuticals and other industrial applications. Soybean has superior protein quality and digestibility compared to other legumes. It also contains abundant amounts of isoflavones, phytosterols and minerals that augment its nutritional value as a constituent of the human diet. Many different by-products are produced during the processing of soy. Due to a lack of sustainable food manufacturing practices, a sustainable amount of these by-products is discarded as waste, posing environmental challenges. Developing an effective waste management system for soybean by-products can help address public health concerns and provide a cost-efficient way to repurpose valuable components of soy, such as soy meal, okara and soy whey. Such valorization can mitigate the environmental impact of waste and contribute to malnutrition. This study aims to evaluate the sustainable use of soy by-products to help preserve biodiversity and reduce food insecurity. This article thoroughly examines the fundamental components of soybean use in the fields of health and soybean by-product processing. It provides a summary of cost-efficient, feasible and ideal processing technologies. © 2024 Society of Chemical Industry.

Optimizing quality and cost in remanufacturing under uncertainty

AbstractIn the context of growing sustainability demands, businesses are increasingly adapting their production practices by integrating remanufacturing. However, companies often face challenges in profitably implementing remanufacturing due to complexities arising from uncertainties in processes, product quality, and market conditions. This highlights the need for effective decision support in remanufacturing processes. Addressing this challenge, our research introduces an algorithm designed to identify cost-efficient process plans that optimize order fulfillment while considering a company’s specific capabilities and inventory levels. By modeling the remanufacturing planning process as a Markov process, our algorithm comprehensively accounts for both process-related and quality-related uncertainties. This approach enables the evaluation of all Pareto optimal process plans in terms of cost efficiency and reliability. We validate our methodology through a real-world application in the automation industry, specifically focusing on the remanufacturing of variable speed drives. This case study demonstrates the practical relevance of our approach and a potential for significant cost reductions, enhanced process efficiency, and improved labor productivity. Overall, businesses gain critical insights into the financial prospects of their remanufacturing efforts, identifying opportunities for optimization and expansion into new product quality categories. This enhances their economic potential and aligns with consumer preferences for distinct product qualities.

Deciphering rock surface origins in a karst cave: insights from the Rača Cave, Lastovo, Croatia

This paper outlines a comprehensive fieldwork methodology for discerning the origin of rock surfaces within a karst cave environment. This methodology is particularly utilized in a cave with breakdown morphology. Using Rača Cave in Lastovo, Croatia as a case study, we explore geological and morphological features through advanced surface analysis. The approach involves meticulous measurement of rock discontinuities, joint patterns, and surface formations. Cost-efficient and time-efficient data collection and processing during field-work were undertaken with FieldClino Move and Polycam applications on smartphones. Visualization techniques were employed to elucidate the interplay between erosion, deposition, and speleogenetic processes.

Photodiode-based focus monitoring in ultrashort-pulsed laser structuring of graphite anodes for lithium-ion batteries

In recent years, there has been an increased demand for elaborate monitoring techniques in laser material processing. This has been driven by the need for fast and cost-efficient quality assurance processes. At the same time, ultrashort-pulsed (USP) laser radiation has emerged as a promising technology for creating intricate microstructures in lithium-ion battery graphite anodes due to its high precision and negligible thermal impact. However, the integration of process monitoring in USP laser applications for graphite anode structuring is still unexplored. There is a lack of clarity on suitable sensors, observable parameters, and extractable process-relevant insights. The presented study addressed this gap by demonstrating the capability of state-of-the-art photodiode-based monitoring systems in collecting process-relevant data and deriving valuable insights. A sensor equipped with three photodiodes was employed to address these challenges. Exploratory data analysis and machine learning methodologies were leveraged to develop a data pipeline for processing the acquired information. The data were used to train convolutional neural networks that could accurately predict the focal position. At the same time, the limitations of traditional regression approaches could be shown. The findings advanced the understanding of the possibilities of process monitoring in USP laser applications and emphasized the significance of data-driven approaches in optimizing manufacturing processes.

Antioxidant and anti-inflammatory activity of gold nanoparticles synthesized employing fruit peel extract of Citrus sinensis L.Osbeck

Abstract Nanoparticle biosynthesis has attained a lot of attention because of its cost-efficiency and eco-friendly process. Only limited studies are available which explain the interrelationship between physical parameters (pH, gold concentration, extract concentration, and temperature) and biological synthesis mechanism. The goal of this study was to predict the antioxidant as well as anti-inflammatory activity of biosynthesized gold nanoparticles utilizing Citrus sinensis peel extract. The initial colour shift from yellow to ruby red served as proof that nanoparticles had been successfully synthesized. The nanoparticles that were synthesized underwent various characterization techniques such as AFM (Atomic Force Microscope), FTIR (Fourier Transform Infrared Spectroscopy), FESEM (Field Emission Scanning Electron Microscope), XRD (X-Ray Diffraction), and UV–vis spectroscopy to examine their properties. Further, the synthesis method was optimized for different pH, various gold concentrations, extract concentrations, and temperatures. The nanoparticles displayed good activity as a powerful antioxidant and anti-inflammatory agent. Advanced studies are necessary to explore their therapeutic property.

Preparation of V2O5 using Pb5(VO4)3OH recovered from Bayer vanadium sludge through H2SO4 pre-treatment, H2O2 leaching, V5O12∙6H2O and V10O24∙12H2O precipitation and oxidative roasting at 400 °C

Bayer vanadium sludge (BVS), discharged during the Bayer alumina production, is a promising secondary resource for the supply of vanadium. Previous vanadium extraction process involving Pb5(VO4)3OH precipitation by PbSO4, H2SO4 and (NH4)2SO3 leaching, (NH4)2S2O8 oxidation followed by ammonium vanadate precipitation, suffered from large amounts of reagent consumption and discharge of ammonia-containing waste. In this study, a cleaner and cost-efficient process based on H2SO4 pre-treatment, H2O2 leaching, hydrolytic precipitation, and preparation of V2O5 through oxidative roasting was proposed to reduce reagent consumption and waste. Results show that the maximum vanadium leaching efficiency reached 99.7 % under the most suitable conditions of H2SO4 pre-treatment with H2SO4/Pb5(VO4)3OH molar ratio of 6.5, pulp density of 250 g/L at 90 °C for 4 h followed by H2O2 leaching with H2O2/V molar ratio of 3.0 at 15 °C for 10 min. Furthermore, 96.5 % of vanadium was precipitated in the forms of V5O12∙6H2O and V10O24∙12H2O by heating the leach liquors to 100 °C for 60 min, followed by the oxidative roasting at 400 °C for 2 h to prepare V2O5 product with a purity of 99.2 %, with only H2O as a by-product. This research provides new insights into vanadium extraction in a cleaner, cost-efficient, and sustainable industrial process which saves 147.4 $/t Pb5(VO4)3OH.

Q.ANTUM NEO with LECO Exceeding 25.5 % cell Efficiency

In recent years, we have developed the Q.ANTUM NEO solar cell, a rear-passivated double-sided contact solar cell that achieves power conversion efficiencies exceeding 25.5 %. Our streamlined and cost-efficient process leverages the proprietary Laser Enhanced Contact Optimization (LECO) technology. The LECO process has undergone extensive optimization and has been successfully implemented in both our pilot line and mass production facilities, resulting in significant improvements in solar cell efficiency. LECO facilitates a novel formation of the contact interface between the silicon surface and metallization, leading to remarkably low j0,met values (as low as 160 fA cm−2). The contact resistivity of 20 μm wide contacts on a high sheet resistance emitter is below 1 mΩ cm2, with minimal impact on fill factor (FF) and overall cell efficiency. Rigorous reliability tests, including TC600 and DH3000, demonstrate module degradation well below 5%rel. Our champion cell conversion efficiency achieved in the pilot line using LECO technology reaches 25.6 %, accompanied by a notable open-circuit voltage exceeding 730 mV.

Wire arc additive manufacturing of a high-strength low-alloy steel part: environmental impacts, costs, and mechanical properties

Additive manufacturing (AM) technologies have demonstrated a promising material efficiency potential in comparison to traditional material removal processes. A new directed energy deposition (DED) category AM process called wire arc additive manufacturing (WAAM) is evolving due to its benefits which include faster build rates, capacity to build large volumes, and inexpensive feedstock materials and machine tools compared to more technologically mature powder-based AM technologies. However, WAAM products present challenges like poor surface finish and lower dimensional accuracy compared to powder-based processes or machined parts, prevalence of thermal distortions, residual stresses, and defects like porosity, cracks, and humping, often requiring post-processing operations like finish machining and heat treatment. These post-processing operations add to the production cost and environmental footprint of WAAM-built parts. Therefore, considering the opportunities and challenges presented by WAAM, this paper analyses the environmental impact, production costs, and mechanical properties of WAAM parts and compares them with those achieved by laser powder bed fusion (LPBF) and traditional computer numerical control (CNC) milling. A high-strength low-alloy steel (ER70S) mechanical part with medium complexity was fabricated using WAAM. Based on the data collected during this experiment, environmental impact and cost models were built using life cycle assessment and life cycle costing methodologies. WAAM was observed to be the most environmentally friendly option due to its superior material efficacy than CNC milling and has a better energy efficiency than LPBF. Also, WAAM was the most cost-friendly option when adopted in batch production for batch sizes above 3. The environmental and cost potential of WAAM is amplified when used for manufacturing large products, resulting in significant material, emission, and cost savings. The fabricated WAAM part demonstrated good mechanical properties comparable to that of cast/forged material. The methodology and experimental data presented in this study can be used to calculate environmental impacts and costs for other products and can be helpful to manufacturers in selecting the most ecofriendly and cost-efficient manufacturing process.

Development of a porcine decellularized extracellular matrix (DECM) bioink for 3D bioprinting of meniscus tissue engineering: formulation, characterisation and biological evaluation

ABSTRACT This study aims to present an easily scalable, cost-efficient process of dECM extraction from porcine meniscus, dedicated to bioink preparation and 3D bioprinting. Due to its cartilage like structure and mechanical robustness, the meniscus is an exceptionally demanding tissue for extraction and decellularisation of its ECM. Its processing poses a great difficulty and renders the methods previously developed for soft tissues useless. A process, combining homogenisation, hydrolysis, supercritical CO2 (scCO2) extraction and lyophilisation, was developed to meet this challenge. This protocol allows for retaining its native compounds and biocompatibility while offering good printability and providing a stimulatory environment for cell proliferation and differentiation towards a meniscus-like phenotype. Also, this process is economically and ecologically friendly since it doesn't require the use of high amounts of solvents, detergents or expensive enzymes (DNase). The decellularisation process has been meticulously studied, demonstrating a substantial reduction in DNA content but still exceeding accepted thresholds. The study further explores the biocompatibility of the dECM, demonstrating no detrimental effects of remnant DNA on cell survival during extended in vitro culture, indicating excellent biocompatibility. These findings challenge the current definition of decellularisation effectiveness based solely on DNA content, proposing a broader assessment of biological effects.

Fire resistance time prediction and optimization of cold-formed steel walls based on machine learning

Many full-scale experiments and numerical studies have been conducted to determine the fire performance of cold-formed steel (CFS) walls, but these studies are expensive and time consuming. This study proposes a machine learning (ML) based framework aiming at accurately predicting the fire resistance time (FRT) and optimizing the design of CFS walls under ISO 834 fire condition. To overcome the limitation of 32 experimental data points in the literature, a validated numerical method was used to generate 592 data points to expand the dataset of CFS walls, considering different wall configurations, various sheathing board types and thicknesses. The XGBoost (eXtreme Gradient Boosting) model was trained with numerical data and tested with experimental data. The hyperparameter tuning of the XGBoost model was implemented with Bayesian optimization, and it was found that the XGBoost model accurately predicted the FRT of CFS walls, with R2 and MAPE values being 0.933 and 8.46 %, respectively. The prediction process of the XGBoost model was interpreted by the SHAP (SHapley Additive exPlanations) method to determine the relative importance of input variables. The NSGA-II algorithm was adopted to optimize the FRT-cost dual-objective of CFS walls and the Pareto front including optimal solutions was emerging. The cost for each solution of Pareto front sets was lower than that in the real dataset at same FRT level. This phenomenon implied that the ML-based optimization framework successfully identified the cost-efficient design process. The proposed ML-based optimization framework offers a promising alternative for engineers to design CFS walls effectively with both mechanical and economic objectives.

Exergo-economic approach for comprehensive evaluation of the performance of industrial-scale milk and coffee beverage process lines

This study aimed to perform comprehensive analyses of the performance of the industrial-scale production of three different beverages (whole milk, black coffee, and milk coffee) in carton aseptic and glass bottle packaging process lines from raw material to the secondary packed final product by using innovative exergo-economic approach to promote a detailed data for a sustainable, performance-efficient production and economy in beverage industry. System performance efficiencies (energy and exergy) for the whole of production lines and individual processing steps were determined, and the exergo-economic performance of the industrial productions of three different beverages was compared. The maximum overall exergy efficiencies for glass bottle and carton aseptic packaging lines were as 52.57 % and 55.90 %, respectively, in whole milk production whereas the minimum overall exergy efficiencies were for these lines were 47.39 % and 48.67 %, respectively, for milk coffee production. For whole milk, the process steps having the minimum exergy efficiency were the filling step in carton aseptic (23.14 %±1.25) and the seperation step (26.90 %±1.09) in glass bottle packaging lines. For black coffee and milk coffee productions, the process step with the highest improvement potential was the homogenization/deaeration step for both carton aseptic and glass bottle packaging lines. The highest exergy destruction costs were obtained for the milk coffee; 72.52±0.03$/MJ for carton aseptic package and 63.82±1.85$/MJ for glass bottle. The most cost-efficient process was determined to be the black coffee in the carton aseptic packaging line due to the minimum product cost rate (89.71±2.39 $/MJ). It is thought that this study contributes valuable information on the approach of the comparing the performance of different production lines using exergo-economic evaluation.

Triple-junction interfacial engineering Pt–CeO2/three-dimensional nitrogen-doped carbon frameworks electrocatalysts for methanol oxidation reaction

Efficient Pt-based catalysts towards anodic methanol oxidation reaction (MOR) is crucial to direct methanol fuel cells (DMFCs). Pt-based catalysts with triple-junction interfaces of Pt, metal oxidizes (MOx) and carbon materials have broad engineering prospect due to the synergistic effect among different composites. However, in Pt-MOx/carbon material catalysts reported previously, Pt and MOx nanoparticles (NPs) are too large and the triple-junction interfaces are insufficient. Herein, we innovatively introduce oxidized carbon nanotubes (OCNTs) into nitrogen-doped reduced graphene oxide (NrGO) sheets and propose a cost-efficient self-assembly process for the construction of three-dimensional nitrogen-doped carbon frameworks (NrGO-OCNTs). Furthermore, a modified solvothermal method is developed to uniformly deposit ultrafine Pt and CeO2 NPs onto NrGO-OCNTs so that sufficient Pt–CeO2–C triple-junction interfaces can be introduced. The obtained Pt–CeO2/NrGO7-OCNTs3 catalyst with the NrGO/OCNTs ratio of 7:3 shows the optimum electrochemical surface area and mass activity of 154.9 m2·gPt−1 and 845 A·gPt−1, respectively, which are 2.3 and 3.2 times higher than those of a commercial Pt/C catalyst. It also possesses excellent stability and anti-CO poisoning performance. These improvements may be attributed to the unique architecture of the support and enhanced synergistic effect among Pt, CeO2 and NrGO7-OCNTs3. This approach is simpler and more efficient than other methods reported previously and suitable for engineering of efficient Pt-based catalysts.

Abstract PO5-01-09: Exploring cellular heterogeneity of localised breast cancers

Abstract Breast cancer is a heterogeneous disease at multiple levels, ranging from subtype differences between patients (inter-patient heterogeneity) to the diverse composition of malignant cells, heterogeneity of hormone receptor (HR) expression and cellular makeup within single breast cancer samples (intra-tumour heterogeneity). Despite an increase in the number of effective therapies available, many patients will experience an incomplete treatment response and subsequent relapse. These adverse treatment outcomes may be attributed to the often overlooked but critical factor of cellular heterogeneity. In order to gain deeper insights into intra-tumour heterogeneity, we have applied single-cell technologies on a cohort comprising 250 primary, untreated breast cancers. We have optimized methods for tissue cryopreservation, eliminating the need for fresh sample processing, and multiplex tissue profiling. Together, these are cost-efficient processes that allow for improved handling of small tissue sizes, such as biopsies, and reduce batch effect. To ensure accurate and reliable data processing, we have developed a scalable computational workflow that includes benchmarked methods for sample SNP-demultiplexing, doublet detection, high-resolution cell annotation and cellular integration. Finally, we are extending our existing methods1 to study the cellular heterogeneity of breast cancers. Our method, scSubtyper, explores the phenotypic differences between malignant cells within tumours, by comparing each single cell to distinct features associated with different molecular subtypes and assigning each cell to one of these subtypes. Our previous study1 and preliminary results of this project revealed that over 90% of the samples exhibit a mix of malignant cells of different subtypes, and 50% of samples contain cells that have characteristics of all subtypes, demonstrating cellular heterogeneity exists not only exists between malignant cells, but also within malignant cells of a tumour. Our second approach, known as ecotyping, assesses patterns of cell type frequencies across samples and groups them based on similarity of these co-occurences. Our preliminary results have revealed the existence of 5 ecotypes that lack significant associations with samples clinical subtypes. Applying the same approach exclusively within the HR-positive samples identified 4 ecotypes characterized by distinct abundances of immune and stromal cells. This analysis revealed that ecotypes are not a simple surrogate for clinical and molecular subtypes, but their presence could drive different response to treatment. Together, our high-throughput tissue processing and computational approaches to studying intra-tumour heterogeneity are now being applied to our large, well annotated, clinical cohort. Supported by the preliminary results, we hypothesize that this study will play a vital role in optimizing breast cancer patient stratification to improve treatment management and outcome. 1. Wu, Sunny Z., et al. "A single-cell and spatially resolved atlas of human breast cancers." Nature genetics 53.9 (2021): 1334-1347. Citation Format: Beata Kiedik, Daniel Roden, Kate Harvey, Ghamdan Al-Eryani, Sunny Wu, Mun Hui, Sandra O'Toole, Elgene Lim, Charles Perou, Alex Swarbrick. Exploring cellular heterogeneity of localised breast cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-09.

Enhancing Mechanical Characteristics of 6061-T6 with 5083-H111 Aluminum Alloy Dissimilar Weldments: A New Pin Tool Design for Friction Stir Welding (FSW)

This research addresses the escalating need for lightweight materials, such as aluminum and magnesium alloys, in the aerospace and automotive sectors. The study explores friction stir welding (FSW), a cost-efficient process known for producing high-quality joints in these materials. The experiment involved the welding of dissimilar aluminum alloys (AA5086-H111 to AA6061-T6) using a novel pin tool design with welding parameters such as holding time, pin tool length, tool spindle speed, and linear speed fine-tuned through a design of experiment (DOE) approach. A comparative analysis of two tool designs revealed that the newly introduced design substantially improved mechanical properties, particularly tensile strengths, by 18.2% relative to its predecessor. It is noteworthy that FSW joint efficiency is 83% when using a normal tool design in comparison with 92.2% when using a new tool design at similar FSW parameters. The new tool achieved the parameter values leading to the maximum tensile strength of 317 MPa with 3 mm thickness (Th), 25 s holding time (Tt), 0.1 mm dimension (L), 1600 rpm spindle speed (SS), and 30 mm/min feed velocity (Fr). In comparison, the normal tool achieved a maximum UTS of 285 MPa, 5 mm Th, 25 s Tt, 0.3 mm L, 800 rpm SS, and 90 mm/min Fr. The new tool design, with longitudinal and circular grooves, improves heat input for plastic deformation and alloy mixing during welding. Subsequent analysis of the joint’s microstructure and microhardness shows its similarity to the original alloys.

Converting Bulk Sugars into Functional Fibers: Discovery and Application of a Thermostable β-1,3-Oligoglucan Phosphorylase.

Despite their broad application potential, the widespread use of β-1,3-glucans has been hampered by the high cost and heterogeneity associated with current production methods. To address this challenge, scalable and economically viable processes are needed for the production of β-1,3-glucans with tailorable molecular mass distributions. Glycoside phosphorylases have shown to be promising catalysts for the bottom-up synthesis of β-1,3-(oligo)glucans since they combine strict regioselectivity with a cheap donor substrate (i.e., α-glucose 1-phosphate). However, the need for an expensive priming substrate (e.g., laminaribiose) and the tendency to produce shorter oligosaccharides still form major bottlenecks. Here, we report the discovery and application of a thermostable β-1,3-oligoglucan phosphorylase originating from Anaerolinea thermophila (AtβOGP). This enzyme combines a superior catalytic efficiency toward glucose as a priming substrate, high thermostability, and the ability to synthesize high molecular mass β-1,3-glucans up to DP 75. Coupling of AtβOGP with a thermostable variant of Bifidobacterium adolescentis sucrose phosphorylase enabled the efficient production of tailorable β-1,3-(oligo)glucans from sucrose, with a near-complete conversion of >99 mol %. This cost-efficient process for the conversion of renewable bulk sugar into β-1,3-(oligo)glucans should facilitate the widespread application of these versatile functional fibers across various industries.

Development of Novel Cost Efficient Process for Extraction of Sterols and Tocopherols from Wild Plant Seed

The present work focuses on the compositional characterization of tamarind and sea buckthorn seed oils, which have a wide range of uses in the cosmetic, pharmaceutical and food sectors. The characterization includes the analysis of tocopherols, sterols and fatty acid profile in both oils. The tamarind and sea buckthorn oils were converted to fatty acid methyl ester and the fatty acid composition was analyzed by using gas chromatography technique. The quantitative and qualitative analysis of tocopherols performed using high performance liquid chromatography. This study reveals that the oil samples consist of a variety of saturated and unsaturated fatty acids, such as myristic acid (0 and 0.4%), palmitic acid (11 and 30.5%), palmitoleic (0 and 39.6%) stearic acid (0 and 0.5%), oleic acid (18.3 and 26.1%), linoleic acid (45.8 and 2.1%), linolenic acid (4.1% and 0.5%), arachidic acid (1.8% and 0%) behenic acid (9.6 and 0%) and lignoceric acid (9.4 and 0%). Additionally, the analysis detects various tocopherol components, including α-tocopherol (21.4 and 32.6%), β-tocopherol (14.49 and 7.24%), γ-tocopherol (53.72 and 45.66%), δ-tocopherol (9.64 and 9.11%) and α-tocotrienol (0 and 5.13%) in tamarind and sea buckthorn oil, respectively.

A finite element model with multi-flow fields for the quenching process of mill liner component made of bainite and martensite: simulation and experimental validation

Controlling the type and location of microstructure transformation through heat treatment processes is the key to improving the property of workpieces, especially they are large-sized and highly thick irregular. Such as mill liner, the worst wear in mining machinery, of which microstructure inhomogeneity is key to developing cost-efficient mining process. In order for advanced high strength steel (AHSS) to be used in mining machinery, its microstructure uniformity must be optimized, usually by adjusting the process parameters of the heat treatment process to optimize the microstructure distribution uniformity. However, the research on the simulation of large-sized and highly thick irregular heat treatment process is limited. Previous studies usually controlled the heat transfer process by using the heat transfer coefficient as the boundary condition, but only applied to small size and simple structure of the workpiece. As the heat transfer coefficient is a process quantity, its size depends on the thermal property parameters of the fluid on both sides of the wall, the flow rate, the shape of the solid surface, the thermal property number of the material and other factors, which is not suitable for large-sized and highly thick irregular. Therefore, for the large-sized and highly thick irregular heat treatment simulation process, the equivalent heat transfer coefficient of flow field was introduced as the boundary condition, and the temperature field, microstructure field and stress field were coupled to improve the accuracy of the numerical model. The accuracy of the finite element model is verified by comparing the temperature simulation error of less than 10% through temperature measurement experiments of the quenched workpiece. The evolution process of heat transfer in large-sized and highly thick irregular water bath quenching is obtained through the finite element model data. The difference in the flow rate of the wall fluid is the fundamental reason for the difference in cooling effect, and the wetting process of the wall is the secondary reason. At the same time, the corresponding microstructure and surface hardness distribution of different cooling regions are obtained, which can provide more accurate guidance for the optimization of heat treatment process of large size and thick special-shaped parts.

PREDICTIVE MAINTENANCE OF A FLEXIBLE PRODUCTION LINE IMPLEMENTED IN MATLAB

In the latest years, the need of new technologies in the industrial environment has increased due to the need of a more cost efficient, resilient, and sustainable production process. One of the main issues that is addressed in this research work is the lack of a technology that allows prediction and prevention of the breakdowns of a production line using digital models and artificial intelligence to take the best decisions based on real-time data. This paper presents a method for collecting data from the production line and feed these data to a database used by the Digital Twin model created in Matlab Predictive Maintenance Toolbox in which, based on this data, a decision about the maintenance activities required will be taken. This approach generates significant value by assisting maintenance engineers in pinpointing the precise timing for equipment maintenance. This capability directly contributes to reducing production line downtime and minimizing failures. Also, the Digital Twin model can be used to enable and test adjustments to the parameters of the production line equipment in order to determine the impact of this changes of the production process without interfering with the production process itself.

An Intelligent E-commerce Platform for Affordable Student Book Exchange and Donation

Books are an integral part of a student's life, a timeless necessity whose demand remains steadfast. However, the traditional methods of selling or purchasing second-hand books often result in a time-consuming and cost-efficient process. Students seeking to either acquire or part with their educational materials must navigate the inconveniences of local bookshops, where books are frequently bought and sold at prices that may not align with students' financial constraints. These transactions are often intermediated by brokers, further inflating the costs associated with acquiring essential textbooks. In today's era, the internet has woven itself intricately into our daily lives, revolutionizing various aspects, and significantly impacting the way commerce is conducted. The e-commerce industry, in particular, has emerged as a transformative force in recent years, bringing about innovative solutions to age-old problems. Recognizing the challenges faced by students in accessing affordable educational resources, this research endeavors to create an intelligent e-commerce platform tailored to their unique needs. This platform aims to streamline the exchange, sale, lending, and donation of second-hand books among students, allowing them to acquire essential educational resources at more affordable rates from their seniors or willing sellers in the vicinity. By facilitating these transactions, the project seeks to not only alleviate the financial burdens borne by students but also contribute to sustainability by extending the lifespan of educational resources.