Industry Efforts Research Articles

Estimating adaptation effort in industry 4.0-enabled systems: Introducing two complexity indices with an evolvable network graph approach

One of the key aims of Industry 4.0 is to create more responsive systems. Responsiveness enables coping with new market requirements or introducing new products, as demonstrated by the COVID-19 challenges. However, there are currently no effective methods for measuring the responsiveness or reconfigurability of a system, or for quantifying the effort required to adapt it from one state to another. Adapting a production cell from its current state to a new adapted state requires a significant amount of information about dismantling, reintegrating, and handling physical equipment, as well as updating the software controller. Practitioners often only consider adaptation options for simple process parametrization or at the end of a system's life cycle, overlooking many potential adaptation opportunities. This paper proposes an evolvable network graph approach for supporting reconfiguration decisions by estimating the effort required to adapt the physical structure. Two complexity indexes have been developed to quantify the adaptation activities. An estimation algorithm infers the effort from the difference in the adaptation graphs that represent alternative options. The approach is illustrated in a laboratory-scale cell and applied in two industrial-sized cells, quantifying adaptation times of approximately 58, 7, and 122 h, respectively. This is equivalent to £3129.6, £356.04, and £6118.8, utilizing average hourly rates for system integrators and equipment handlers. The results show that the approach can effectively quantify the adaptation effort for different equipment sizes and connections, estimating the adaptation cost and time from the graph change quickly at around a millisecond and with minimal computational resources.

Comprehensive analysis of dynamic topology sensor linkage for the internet of things

In today’s developing IoT world, wireless sensor interactions are critical. To ensure full system IP compatibility, industry efforts are focused on standardizing the WSN protocol suite and enabling the second generation of IoT devices to be produced. As a result of their robust communications and conformance to less verbose WSN features such as sleep cycles required to keep end nodes’ total energy consumption low, the WSN protocols are strong contenders for use as a highly useable implementation layer for the IoT. Existing smart systems make use of system assets for critical IoT functions. Compared to a wired configuration, wireless sensors are easier to install and provide more device flexibility. As sensor technology advances, WSNs will become a critical component of the IoT and an essential tool for achieving the IoT paradigm’s goal. It’s also crucial to think about whether or not a WSN’s sensors should be fully linked into the IoT’s. For WSNs, a multi-objective optimization technique for selecting the most energy-efficient CH is described. The CHs for each lattice were first selected using a candidate CH selection method. Energy waste and data redundancy were also decreased by using multi-hop networking. An inventive new method has also been devised to ensure perfect implementation. The Evolutionary Algorithm (EA) model and optimization techniques used in this work provided an energy-efficient Machine Learning (ML) strategy for selecting WSN CH. First, the CH of each lattice was calculated using a candidate CH selection method. According to the results of the simulation, the suggested model’s performance is superior to traditional approaches in terms of network lifespan, energy efficiency, and reduced consumption.

Heat integration, simultaneous structure and parameter optimisation, and techno-economic evaluation of waste heat recovery systems for petrochemical industry

Energy conservation in the petrochemical sector holds the key to its financial viability. The pervasive application of Heat Exchanger Networks (HEN) exemplifies the industry's efforts in heat recovery. Yet, despite its widespread adoption, an alarming quantum of low-grade heat remains squandered, underscoring the potential for augmenting energy savings through more effective utilization of this heat. Recognizing the pivotal role of the synergistic interaction between the process and the heat recovery system in maximizing energy retrieval, an innovative approach is proposed. This methodology initially entailed the development of a process model and an enhanced heat recovery system, the latter embodying the integration of an organic Rankine cycle (ORC) with HEN. Subsequently, a strategic diagnostic was proposed to incorporate these sub-systems into a cohesive, optimisation framework. Following this, an array of energy, exergy, and economic analyses were conducted to appraise the system's performance. The findings suggest a considerable improvement in heat recovery and an amplification in ORC efficiency from a mere 7.64%–11.38%. The enhanced heat recovery further translated into a reduced need for cold utility, thereby minimizing cooler deployment. Given the substantial exergy destruction associated with coolers, their lesser usage bolstered the system's exergy efficiency. Moreover, the optimised ORC manifested in heightened net power generation, consequently elevating electricity revenues. Despite a nominal surge in equipment costs, the aggregate profit witnessed a substantial hike from 165.19 M$/year to 178.79 M$/year. The proposed system substanitally improved thermodynamic and economic performance. This study offers valuable guidance for the design and operation of petrochemical industries, serving as a roadmap to energy conservation and enhanced profitability.

Efficient annotation reduction with active learning for computer vision-based Retail Product Recognition

The retail industry encounters huge obstacles with computer vision (CV) technology due to frequent model retraining with changing products and time-consuming, costly data annotation. Previous research in this field has been primarily focused on optimizing model performance rather than minimizing annotation effort. Therefore, the main idea of this paper is to evaluate active learning as a method to minimize annotation effort in the retail industry. The MVTEC Densely Segmented Supermarket dataset is used to evaluate various active learning methods such as the Least Confident, Entropy and Cost-Effective Active Learning (CEAL) along with Mask R-CNN model. The results demonstrate that annotating only 20.83-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}24.34% of the data achieves 95% of the full dataset’s performance. When training, out-of-sample data share similar characteristics, the Least Confident and CEAL methods reduce annotation requirements by 7.7-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}15.7% while maintaining 95% and 97% of the full dataset’s performance. However, the Entropy method under-performs compared to the random selection baseline. Ultimately, none of the methods show a clear advantage when the data characteristics differ between training and out-of-sample data. Finally, the proposed active learning methods on an industry-specific retail dataset remarkably propels the development of highly efficient and cost-effective CV solutions meticulously tailored for the retail industry.

Effect of Heat Treatments on Microstructure and Mechanical Properties of Fe-Mn-Ni-Al-Gd Shape Memory Alloy

There are significant scientific and industrial efforts to develop and optimize Iron-based shape memory alloys (SMA) such as FeMnNiAl for cost-sensitive applications. This alloy system shows shape memory and superelastic properties across a large temperature range. However, many studies have pointed out the need for rather complex thermo-mechanical treatments for the optimization of the SMA properties. In addition, works considering the effects of alloying on the development of microstructures that are more conducive to pseudo-elasticity in this system remain limited. Hence, systematic studies aiming at the investigation of the microstructural evolution of the FeMnNiAl(Gd) system are of great interest. In this study, solution heat treatment is done to tune the microstructure for optimum mechanical properties. The effect of phase distribution on mechanical properties is investigated at different heat treatments. Whereas cyclic heat treatment induced abnormal grain growth (AGG) in all samples, so large grains were obtained. The phase variation and elemental composition are analyzed by X-ray diffraction and Energy Dispersive Spectroscopy, respectively. The microstructure and phase distribution are observed using Scanning Electron Microscope and then related to the microhardness results. The microstructure has a good correlation with mechanical properties where the fine distribution of phases results in a higher hardness number.

The Urbanization Process in Nha Trang City (Khanh Hoa Province,Vietnam) From 1989 To 1999

In 1989, Khanh Hoa province underwent restructuring to facilitate the full utilization of the region's potential. Despite initial hurdles during the renovation period, the urbanization of Nha Trang city progressed dynamically, spurred by the positive effects of renovation policies, industrialization, and modernization efforts. Transitioning from a type III urban area, by 1999, Nha Trang had advanced to a type II urban area. Industries and services took precedence in the economic landscape, while significant investments were made to upgrade urban infrastructure. Consequently, population density surged, accompanied by gradual enhancements in both the physical and psychological well-being of the populace. Drawing upon information provided by the Khanh Hoa Provincial Statistics Office, the Party Committee of Nha Trang city, and neighborhood administrations, this article employs historical and logical approaches intricately intertwined with analytical, synthetic, and comparative methods. Through this comprehensive framework, the aim is to elucidate the accomplishments and challenges encountered by Nha Trang city throughout its urbanization journey from 1989 to 1999 across various domains.

Facilitating the transition to sustainable propulsion in the shipping industry: An agent-based modelling analysis of retrofitting

In response to growing concerns about the environmental impact of the shipping industry, this study examines the potential impacts of retrofitting on the shipping industry and the barriers and incentives that may affect the adoption of low-emission technologies. The MATISSE-SHIP agent-based model of transitions is extended to include retrofits. The results suggest that retrofitting can lead to a rapid decline in the use of high-sulphur fuel oil, with wind-assisted technology emerging as a competitive and cost-effective option for shipowners.Additionally, retrofitting can accelerate the transition to a diverse and low-carbon portfolio of propulsion technologies. The findings indicate that the success of decarbonization efforts in the shipping industry depends on a range of factors, including reliable supply that affects the accessibility and affordability of alternative fuels. Policymakers should consider these factors when developing policies and incentives to encourage the adoption of low-emission technologies in the shipping industry.

Diversifying Away from Extractives: The Belt and Road Initiative, Chinese Capital and Industrialisation in the Kyrgyz Republic

With its infrastructural offer and emphasis on connectivity, China’s Belt and Road Initiative (BRI) promises to support developing countries aiming to industrialise. The literature on industrialisation has identified a key role for infrastructure, markets and the state. This article looks at the case of Kyrgyzstan, one of the early members of the initiative, to understand the circumstances under which the BRI can facilitate manufacturing growth. Notwithstanding the presence of accessible markets and financial support for infrastructure, Kyrgyzstan has made limited headway in its industrialisation agenda. This can be attributed to the particular political economy of Kyrgyzstan, which predominantly relies on extracting rent from the mining and other sectors and consequently fails to allocate resources towards industrialisation. Though purportedly interested in revitalising the industrial sector, Kyrgyz politicians have primarily focused on extractive industries, hence impeding diversification and industrialisation efforts.

PROCESS DEVELOPMENT IN MECHANICAL ENGINEERING: INNOVATIONS, CHALLENGES, AND OPPORTUNITIES

Process development in mechanical engineering encompasses a broad spectrum of innovations, challenges, and opportunities that drive advancements in various industries. This review delves into the multifaceted landscape of process development within mechanical engineering, highlighting key themes and trends. Innovations in process development are propelled by the continuous quest for efficiency, sustainability, and enhanced performance. Advances in additive manufacturing, automation, and digitalization have revolutionized traditional manufacturing processes, enabling the creation of complex geometries with unprecedented precision and speed. Additive manufacturing techniques such as 3D printing offer versatility in material selection and design freedom, facilitating rapid prototyping and customization while minimizing material wastage. Automation technologies, including robotics and AI-driven systems, optimize production workflows, augmenting productivity and reducing human error. Moreover, digital twins and simulation tools empower engineers to simulate and optimize processes virtually, accelerating product development cycles and minimizing costly experimentation. However, alongside these innovations, mechanical engineers face a myriad of challenges. The integration of new technologies often requires substantial investment in infrastructure and workforce training. Quality control and assurance remain critical concerns, particularly in additive manufacturing, where ensuring part integrity and repeatability is paramount. Furthermore, the sustainability implications of emerging processes must be carefully evaluated to mitigate environmental impact and resource depletion. Despite these challenges, process development in mechanical engineering presents abundant opportunities for growth and advancement. The rise of Industry 4.0 initiatives fosters collaboration between academia, industry, and government, driving research and development efforts towards sustainable and interconnected manufacturing ecosystems. The advent of advanced materials, including composites and biomaterials, unlocks new possibilities for lightweight structures, enhanced performance, and tailored functionalities. Additionally, the pursuit of circular economy principles promotes resource efficiency and waste reduction throughout the product lifecycle. In conclusion, process development in mechanical engineering is characterized by a dynamic interplay of innovation, challenges, and opportunities. By embracing emerging technologies, addressing key challenges, and leveraging collaborative partnerships, mechanical engineers can drive transformative change across industries, paving the way for a more efficient, sustainable, and interconnected future.
 Keywords: Process, Development, Mechanical, Engineering, Innovation, Review.

Recent Advances in Controlled Production of Long-Chain Branched Polyolefins.

Polyolefins, composed of carbon and hydrogen atoms, dominate global polymer production. This stems from the wide range of physical and mechanical properties that various polyolefins can cover. Their versatile properties are largely tuned by chain microstructure, including molar mass distribution, comonomer content, and long-chain branching (LCB). Specifically, LCB imparts unique characteristics, notably enhances processability crucial for downstream applications. Tailoring LCB structural features has encouraged academic and industrial efforts, chronicle in this review from a chemistry standpoint. While encompassing post-reaction modification based traditional methods like peroxide grafting, ionizing beam irradiation, and coupling reactions, the main focus is given to catalyst-centric strategies and innovative polymerization schemes. The advent of single-site catalysts-metallocenes and late transition metals catalysts-amplifies interest in tailored chemical methods, but the progress in LCB formation flourishes via tandem catalytic systems and bimetallic catalysts under controlled reaction conditions. Specifically, the breakthrough in coordinative chain transfer polymerization unveils a novel avenue for controlled LCB synthesis by sequential chain propagation, transfer, liberation, and enchainment. This short review highlights recent approaches for the production of LCB polyolefins that can provide a roadmap crucial for researchers in academia and industry, steering their efforts toward further advancements in the production of tailored polyolefin.

Green iron recovery through hydrogen-based mineral phase transformation using ultrafast air-cooling for heat recycling: A pilot-scale investigation

The utilization of ultrafast air-cooling has been proposed to reduce energy consumption in hydrogen-based mineral phase transformation (HMPT) technology for more sustainable iron recovery. Optimal conditions, including a grinding fineness of −74 μm accounting for 90 wt%, a reducing gas concentration of 20 vol%, a roasting temperature of 540 °C, and an air-cooling temperature of 300 °C, yielded an iron concentrate with a TFe grade of 59.29 wt% and a recovery of 89.40 wt%. The beneficiation indexes obtained by ultrafast air-cooling were comparable to those of the N2-cooling method. The reaction mechanisms were comprehensively studied to optimize the process, which revealed controlled oxidation of artificial magnetite to maghemite and hematite under ultrafast air-cooling conditions. The heat released during oxidation was recycled, leading to reduced energy consumption. Furthermore, ultrafast air-cooling increased particle porosity and cracks, thereby reducing subsequent grinding energy requirements. This research advances carbon reduction efforts in the steel industry, facilitating the transition to carbon neutrality.

MoS2 decorated carbon fiber yarn hybrids for the development of freestanding flexible supercapacitors

Academic and industrial efforts have focused on developing energy storage devices for wearable and portable electronics using low-cost, scalable, and sustainable materials and approaches. In this work, commercially available stretch-broken carbon fiber yarns (SBCFYs) were hybridized with mixed phases of 1 T and 2H MoS2 nanosheets via conventional and microwave-assisted heating (CAH, MAH) without the use of binders to fabricate symmetric freestanding 1D fiber-shaped supercapacitors (FSCs). Electrochemical characterization performed in a three-electrode configuration showed promising results with specific capacitance values of 184.41 and 180.02 F·g−1, at 1 mV·s−1 for CAH and MAH, respectively. Furthermore, after performing 3000 CV cycles at 100 mV·s−1, the capacitance retention was 79.5% and 95.7%, respectively. Using these results as a reference, symmetric 1D FSCs were fabricated by pairing hybridized SBCFYs with MoS2 by MAH. The devices exhibited specific capacitances of approximately 58.60 ± 3.06 F·g−1 at 1 mV·s−1 and 54.81 ± 7.34 F·g−1 at 0.2 A·g−1 with the highest power density achieved being 15.17 W·g−1 and energy density of 5.06×10–4 Wh·g−1. In addition, five 1D FSCs were hand-stitched and connected in series onto a cotton fabric. These supercapacitors could power a temperature and humidity sensor for up to six minutes, demonstrating the practicality and versatility of the prepared 1D FSCs for powering future electronic systems.

Sustainability disclosure in the pharmaceutical and chemical industries: Results from bibliometric analysis and AI-based comparison of financial reports

Climate change is a pressing global challenge, and companies are under increasing pressure to adopt sustainable practices and transparently disclose their environmental performance. This study focuses on the Italian pharmaceutical industry to gain insights into the level of stakeholder and management attention to climate change and the growth of academic research in this critical area. We employ a bibliometric analysis of scientific literature and a content analysis of financial statements to examine sustainability practices and reporting in both pharmaceutical and chemical companies.Our findings reveal a significant difference in stakeholder expectations and commitment to addressing climate change between the two industries. The chemical industry has demonstrated a stronger focus on sustainability, as evidenced by higher academic research and environmental disclosure levels. In contrast, the pharmaceutical sector has shown a comparatively lower level of environmental reporting and sustainability practices. However, our analysis shows a positive trend in pharmaceutical companies’ voluntary disclosure of environmental information in their financial reports.These results highlight the need for increased focus and effort in the pharmaceutical industry to address climate change and improve sustainability practices. Pharmaceutical companies can build stakeholder trust, improve environmental sustainability, increase transparency in reporting, align practices with global sustainability goals, enhance their reputation, and reduce their environmental impact. This research provides valuable guidance for policymakers, researchers and practitioners in promoting sustainable practices in the pharmaceutical industry and contributes to the broader understanding of ESG disclosure and climate change.

Bolstering circular economy in construction through digitalisation

Purpose The purpose of this study is to assess the potential benefits of digital technologies (DTs) in bolstering the circular economy (CE) transition in the construction industry, to speed up the attainment of sustainable development objectives. Design/methodology/approach A detailed literature review was undertaken to identify DTs that could influence CE transition and the benefits of these DTs in the CE transitioning efforts of the construction industry. Based on these, a survey questionnaire was formulated and administered to construction professionals using convenient sampling techniques. With a response rate of 49.42% and data reliability of over 0.800, the gathered data were analysed using frequency and percentage, mean item score, normalisation value, coefficient of variation, Kendall’s coefficient of concordance, analysis of variance and factor analysis. Findings This study found that the construction experts agreed that building information modelling, blockchain technology, RFID, drone technology and cloud computing are the leading DTs that have the potential to influence and speed up CE transition in construction. Also, six clusters of benefits of DTs in bolstering EC are quicken CE transition, proactive waste management, recycling and zero waste, data management and decision-making, enhance productivity and performance and resource optimisation. Originality/value Studies on the integration of DTs in CE transition are scarce and it is even lacking in the Nigerian context. To the best of the authors’ knowledge, this study is the first to assess the role of DTs in CE transitioning in the Nigerian construction industry.

On the Evidence of Things Not Seen: A Systems-Driven Analysis of LifeSkills Training, University and Tobacco Industry Efforts in School-Based Drug Prevention

ABSTRACT Background Despite recent declines, youth nicotine vaping remains seen by experts as a national public health epidemic. Few, if any, researchers have examined how related high-profile and school-based prevention programs such as LifeSkills Training (LST) are further disseminated with cooperation from research-to-practice entities as well as the tobacco industry. Purpose Provides a systems-driven documentary analysis of efforts pertaining to prevention entities at the University of Colorado, Boulder, as well as LST, and the tobacco industry. Methods Employs focused synthesis data collection with constant comparative analyses. Results Likely previously unseen and related to LST, is the degree to which prevention entities with a public mission appear to cooperate among themselves and with the tobacco industry as part of a longstanding, if not elaborate, school-based drug prevention social system characterized by patterned evidence of opaque or furtive representations and/or activities. Discussion Along with limitations, summarizes a research-to-practice social system likely compromising school-based prevention and, in turn, the health of the young people they serve. Theorizes a symbolic politics explanatory mechanism while providing recommendations toward transparency for more informed school-based prevention decision-making. Translation to Health Education Practice Emphasizes the National Commission for Health Education Credentialing Inc. (NCHEC) critical thinking and program evaluation competencies.

Effect of gasoline-ethanol blends on the performance of a four stroke engine using Ricardo Wave software

Purpose The purpose of this study is to assess the performance of fuel blends containing ethanol and gasoline in spark ignition engines. The aim is to explore alternative fuels that can enhance performance while minimizing or eliminating adverse environmental impacts, particularly in the context of limited fossil fuel availability and the need for sustainable alternatives. Design/methodology/approach The authors used the Ricardo Wave software to evaluate the performance of fuel blends with varying ethanol content (represented as E0, E10, E25, E40, E55, E70, E85 and E100) in comparison to gasoline. The assessment involved different composition percentages and was conducted at various engine speeds (1,500, 3,000, 4,500 and 6,000 rpm). This methodology aims to provide a comprehensive understanding of how different ethanol-gasoline blends perform under different conditions. Findings The study found that, across all fuel blends, the highest brake power (BP) and the highest brake-specific fuel consumption (BSFC) were observed at 6,000 rpm. Additionally, it was noted that the presence of ethanol in gasoline fuel blends has the potential to increase both the BP and BSFC. These findings suggest that ethanol can positively impact the performance of spark-ignition engines, highlighting its potential as an alternative fuel. Originality/value This research contributes to the ongoing efforts in the automotive industry to find sustainable alternative fuels. The use of Ricardo Wave software for performance assessment and the comprehensive exploration of various ethanol-gasoline blends at different engine speeds add to the originality of the study. The emphasis on the potential of ethanol to enhance engine performance provides valuable insights for motor vehicle manufacturers and researchers working on alternative fuel solutions.

Rebaudioside D decreases adiposity and hepatic lipid accumulation in a mouse model of obesity

Overconsumption of added sugars has been pointed out as a major culprit in the increasing rates of obesity worldwide, contributing to the rising popularity of non-caloric sweeteners. In order to satisfy the growing demand, industrial efforts have been made to purify the sweet-tasting molecules found in the natural sweetener stevia, which are characterized by a sweet taste free of unpleasant aftertaste. Although the use of artificial sweeteners has raised many concerns regarding metabolic health, the impact of purified stevia components on the latter remains poorly studied. The objective of this project was to evaluate the impact of two purified sweet-tasting components of stevia, rebaudioside A and D (RebA and RebD), on the development of obesity, insulin resistance, hepatic health, bile acid profile, and gut microbiota in a mouse model of diet-induced obesity. Male C57BL/6 J mice were fed an obesogenic high-fat/high-sucrose (HFHS) diet and orally treated with 50 mg/kg of RebA, RebD or vehicle (water) for 12 weeks. An additional group of chow-fed mice treated with the vehicle was included as a healthy reference. At weeks 10 and 12, insulin and oral glucose tolerance tests were performed. Liver lipids content was analyzed. Whole-genome shotgun sequencing was performed to profile the gut microbiota. Bile acids were measured in the feces, plasma, and liver. Liver lipid content and gene expression were analyzed. As compared to the HFHS-vehicle treatment group, mice administered RebD showed a reduced weight gain, as evidenced by decreased visceral adipose tissue weight. Liver triglycerides and cholesterol from RebD-treated mice were lower and lipid peroxidation was decreased. Interestingly, administration of RebD was associated with a significant enrichment of Faecalibaculum rodentium in the gut microbiota and an increased secondary bile acid metabolism. Moreover, RebD decreased the level of lipopolysaccharide-binding protein (LBP). Neither RebA nor RebD treatments were found to impact glucose homeostasis. The daily consumption of two stevia components has no detrimental effects on metabolic health. In contrast, RebD treatment was found to reduce adiposity, alleviate hepatic steatosis and lipid peroxidation, and decrease LBP, a marker of metabolic endotoxemia in a mouse model of diet-induced obesity.

Sarong Bima: Elegance and Culture in Typical Cloth of West Nusa Tenggara

The industrial sector is one of the economic drivers in a region. Meanwhile, the industrial sector development in Bima Region is still poorly managed, especially the center of the Tembe Nggoli weaving industry. Therefore, there is a need for efforts by the NTB provincial government to properly manage it. Local economic development which can be done for the product is by using the industrialization concept. Thus, the purpose of writing this article is to examine the industrialization concept by the NTB provincial government as an effort to develop the local economy. The results showed that industrialization efforts by the NTB provincial government included trainings which are based on skills affirmation, business management, production equipment assistance, capital assistance, and even the value of packaging and marketing coverage workshop. The outcome of the training is to provide an expansion of employment opportunities, increase the quality and production capacity. This article used a literature approach, by examining several journals, books, electronic printed documents, and information relevant to the study. The discussion of the article is divided into several parts, 1) Characteristics of the typical woven pattern of Bima Tembe Nggoli; 2) Industrialization as an effort to develop the local economy; 3) Discussion; and 4) Conclusion.

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

Investigation of TiO2 replacement alternatives for water-based paints using styrene butyl acrylate

Purpose Titanium dioxide (TiO2) has high opacity, high brightness and whiteness, owing to its high refractive index value. It is mainly used in the coating industry and continuous efforts have been made to replace some of the TiO2 in paint with new pigments. This study aims to replace part of TiO2 pigment with various percentages of BaSO4, CaCO3 and kaolin in styrene butyl acrylate-based paint formulations, without changing the properties of paints using only titanium dioxide. Design/methodology/approach To determine the optimum use rate of new pigment mixing, opacity, gloss, scrub resistance and weather resistance properties have been investigated in the water-based paint formulation. The morphological properties of these samples were examined by scanning electron microscopy analysis. Findings In the total color change (ΔE) measurements, it was observed that the sample coded 85Ti/15Ba produced extremely similar results to the situation when TiO2 was used alone. It was seen that the best results were obtained when 85Ti/15Ba was used instead of TiO2. Originality/value Comparison research on the impact of replacing TiO2 with BaSO4, CaCO3 and kaolin on the performance characteristics of water-based styrene butyl acrylate-based paint formulations has not been done in the literature, according to the literature search.