Industrial Manufacturing Processes Research Articles

Interfacial energy-band engineering for high open-circuit voltage of rear emitter p-type crystalline silicon solar cells

Rear emitter p-type crystalline silicon (c-Si) tunnel oxide passivating contact (P-TOPCon) devices with low-resistivity (≤1 Ω.cm) c-Si wafer and high-conductivity phosphorus doped (n-type) polysilicon passivating contact (n-poly-Si PC) are expected to achieve high built-in potential (Vbi) and thus open-circuit voltage (Voc). However, the results indicate that the high conductivity of n-poly-Si PC considerably deteriorates the interface passivation quality and lowers the device's Voc. A crucial factor to consider is that the hole and electron can tunnel and recombine together at the c-Si/n-poly-Si PC contact, resulting in a decrease in the quality of interfacial passivation. To inhibit carrier tunnel diffusion and enable energy band engineering at the interface for achieving high Voc, a thin intrinsic poly-Si buffer layer (10 nm) is inserted between SiO2 and n-poly-Si PC. When compared to a device without a buffer, a device with the buffer exhibits a significant improvement in passivation quality and hence Voc. The device with buffer has a Voc of 705 mV and an efficiency of 23.5 %, while that without buffer is 681 mV and 22.6 %, respectively. The introduced buffer has little effect on the industrial manufacturing process, but it does significantly enhance Voc. It has the potential to improve the efficiency of the P-TOPCon devices.

Self-Assembled Molecular Fibers Aligned by Compression in Water.

Molecular self-assembly has attracted much attention as a potential approach for fabricating nanostructured functional materials. To date, energy-efficient fabrication of nano-objects such as nanofibers, nanorings, and nanotubes is achieved using well-designed self-assembling molecules. However, the application of molecular self-assembly to industrial manufacturing processes remains challenging because regulating the positions and directions of self-assembled products is difficult. Non-covalent molecular assemblies are also too fragile to allow mechanical handling. The present work demonstrates the macroscopic alignment of self-assembled molecular fibers using compression. Specifically, the macroscopic bundling of self-assembled nanofibers is achieved following dispersion in water. These fiber bundles can also be chemically crosslinked without drastic changes in morphology via trialkoxysilyl groups. Subsequently, vertically oriented porous membranes can be produced rapidly by slicing the bundles. This technique is expected to be applicable to various functional self-assembled fibers and can lead to the development of innovative methods of producing anisotropic nanostructured materials.

Quantitative shear stress imaging in high throughput in-line Si wafer inspection

Defect inspection methods are basic steps in electronic chip manufacturing and power device production processes in the semiconductor industry. Based on stress induced photoelastic anisotropy, in-line polarized stress imaging (PSI) and quantitative determination of the local shear stress component are possible in single crystalline silicon wafers. Hereafter, such polarization stress images are compared and validated quantitatively with results of finite element simulations based on different 3D elastic models. Uniquely, high resolution at high throughputs is achieved for 300 mm wafer size in the present quantitative stress imaging study.

Novel Triplet Loss-Based Domain Generalization Network for Bearing Fault Diagnosis with Unseen Load Condition

In the real industrial manufacturing process, due to the constantly changing operational loads of equipment, it is difficult to collect data from all load conditions as the source domain signal for fault diagnosis. Therefore, the appearance of unseen load vibration signals in the target domain presents a challenge and research hotspot in fault diagnosis. This paper proposes a triplet loss-based domain generalization network (TL-DGN) and then applies it to an unseen domain bearing fault diagnosis. TL-DGN first utilizes a feature extractor to construct a multi-source domain classification loss. Furthermore, it measures the distance between class data from different domains using triplet loss. The introduced triplet loss can narrow the distance between samples of the same class in the feature space and widen the distance between samples of different classes based on the action of the cross-entropy loss function. It can reduce the dependency of the classification boundary on bearing operational loads, resulting in a more generalized classification model. Finally, two comparative experiments with fault diagnosis models without triplet loss and other classification models demonstrate that the proposed model achieves superior fault diagnosis performance.

Analyzing Leak Detection Methods for Aluminum Cast Components in Automotive Manufacturing Processes

Evaluation of the effectiveness of the 'Cosmo Air Leak Tester' machine and the 'Waterleak' method in detecting leaks in aluminum casting transmission components in the manufacturing process in the automotive industry. Although the 'Cosmo Air Leak Tester' successfully detects large leaks, it has limitations in detecting small leaks such as air bubbles. The research proposed a combination of both methods to improve early leak detection and ensure higher product quality. The research findings highlight the importance of considering cost, cycle time, and safety aspects in the manufacturing process.

Structural Equation Modeling of Relationships Among Lean Operational, Financial Performance, and Customer Satisfaction

Objectives: The challenges of implementing a lean business system can be solved by Structural Equation Modeling. In this research, the link between lean operational, financial performance, and customer satisfaction has been investigated in the Akaki Basic Metal Industry in Ethiopia. Methods: A lean operational performance that met the needs of the industry was identified by conducting a structural survey questionnaire to collect feedback data from experts. The model was developed including 28 variables from the experts in the industry. The collected responses were tested for reliability and validity using Cronbach's alpha, composite reliability, convergent, and discriminant validity. Findings: From the use of Analysis of Moments and Structures (AMOS-SEM), the results confirmed that C.R. > 1.96, p < 0.001, and p < 0.05 on quality and delivery. This shows a significant positive direct effect on customer satisfaction by 25.8% on quality, 24.9% on delivery, and 10.3% on cost. Novelty: Even though LM has become a fundamental aspect of industrial manufacturing processes, lack of understanding (knowledge) and practicing how to implement the positive impact on lean operational, financial performance, and customer satisfaction are still not sufficiently exploited particularly in developing countries. This research adds values to the practical use of the concept by examining the relationship among lean operational, financial performance, and customer satisfaction within basic metal manufacturing industry in Ethiopia. In addition to this, the findings have demonstrated improved organizational performance through structural equation modeling. It also helps leaders to focus more on training employees on quality issues and developing an awareness of quality achievement as a core value in the industry. Keywords: Confirmatory factor analysis, Exploratory factor analysis, Lean operational performance, Structural equation modeling, Customer satisfaction

A novel industrial process situation awareness model based on multi‐time scale dynamic feature fusion with applications to float glass manufacturing

AbstractModern industrial manufacturing processes often focus on the changes of performance indicators and the evolution of operation conditions. However, the dynamics of variables are vague within processes and even more difficult to be characterised across different processes. The time‐series dynamic characteristics of the different scales among variables, processes, and production cycles develop and transfer with changes in parameters, equipment, and processes. It is difficult to accurately show the quality index and operating condition trend at the same time. To solve these problems, a situation awareness (SA) framework integrating multi‐time scale dynamic features for manufacturing processes is proposed. First, data are condensed and reconstructed through the denoising long short term memory autoencoder to reveal the time series dynamic characteristics to get neighbourhood features which contain features among neighbourhood samples. Second, the neighbourhood features divided into window blocks are fused into the stage features by statistical analysis. Finally, a multi‐scale isometric convolution network is designed to extract the local and global features, which can effectively show the development of dynamic features on a long time scale, and profoundly describe the influence of full cycle features on variables and operating conditions. The proposed model is verified on the real data set of a float glass manufacturing process, and the SA model can well predict the future trend of long time series.

Review on Advancements in the Production of Artificial Aggregates from Industrial Byproducts: A Sustainable Solution for Infrastructure Development

This review paper explores the burgeoning interest in artificial aggregates as sustainable alternatives to traditional construction materials, driven by concerns over resource depletion and environmental degradation. It comprehensively examines recent advancements, challenges, and future prospects in this field. Beginning with an overview of the imperative for alternative aggregates in construction, the paper discusses various types of artificial aggregates sourced from industrial by-products, recycled materials, and innovative manufacturing processes. It delves into their mechanical, physical, and environmental properties, contrasting them with natural aggregates, and evaluates the impact of production techniques like sintering, foaming, and chemical activation on their performance. Environmental sustainability aspects, including carbon footprint reduction and waste management, are rigorously analyzed, underscoring the potential of artificial aggregates to mitigate ecological impacts associated with conventional extraction and processing methods. Addressing hurdles to widespread adoption, such as technological barriers and economic viability, the review proposes strategies for overcoming these challenges and outlines future research directions to optimize artificial aggregates' properties and applications. In conclusion, this review offers valuable insights into the state-of-the-art developments in artificial aggregate technology, highlighting their pivotal role in advancing sustainability and resilience within the construction sector. Key Word: Artificial aggregates, Sustainable construction materials, Environmental impact, Recycling, Waste utilization.

Cadmium inhibits calcium activity in hippocampal CA1 neurons of freely moving mice.

Cadmium (Cd) is a ubiquitous toxic heavy metal and a potential neurotoxicant due to its wide use in industrial manufacturing processes and commercial products, including fertilizers. The general population is exposed to Cd through food and smoking due to high transfer rates of Cd from contaminated soil. Cd has been shown to mimic calcium ions (Ca2+) and interfere with intracellular Ca2+ levels and Ca2+ signaling in in vitro studies. However, nothing is known about Cd's effects on Ca2+ activity in neurons in live animals. This study aimed to determine if Cd disrupts Ca2+ transients of neurons in CA1 region of the hippocampus during an associative learning paradigm. We utilized in vivo Ca2+ imaging in awake, freely moving C57BL/6 mice to measure Ca2+ activity in CA1 excitatory neurons expressing genetically encoded Ca2+ sensor GCaMP6 during an associative learning paradigm. We found that a smaller proportion of neurons are activated in Cd-treated groups compared with control during fear conditioning, suggesting that Cd may contribute to learning and memory deficit by reducing the activity of neurons. We observed these effects at Cd exposure levels that result in blood Cd levels comparable with the general U.S. population levels. This provides a possible molecular mechanism for Cd interference of learning and memory at exposure levels relevant to U.S. adults. To our knowledge, our study is the first to describe Cd effects on brain Ca2+ activity in vivo in freely behaving mice. This study provides evidence for impairment of neuronal calcium activity in hippocampal CA1 excitatory neurons in freely moving mice following cadmium exposure.

Development of a Lean and Green Indicators for Sustainability Assessment in Processing Manufacturing Industries: A Delphi approach

Sustainability has become a concern of academics and practitioners, where implementing sustainability can help reduce environmental impact, and for that, it is necessary to take steps to assess the level of sustainability. So to assess sustainability, it is necessary to discuss the indicators used to measure sustainability, where these indicators really need to be discussed in measuring sustainability manufacture. This paper aims to determine the selection of indicators used for sustainability assessment in the manufacturing industry by discussing lean and green indicators to measure sustainability. Given that lean focuses on operational performance while green focuses on environmental performance, these two dimensions will be examined for appropriate indicators to measure sustainability. The Delphi method is used in this study to select manufacturing sustainability indicators that are appropriate or relevant to the manufacturing industry. The findings of this study found 29 indicators suitable for measuring sustainability in the processing manufacturing industry, consisting of 13 indicators for the economic dimension, 9 for the environmental dimension, and 7 for the social dimension. The indicators obtained can be applied to measure sustainability in the cooking oil processing manufacturing industry.

Highly active ultralow loading Pt electrodes for hydrogen evolution reaction developed by magnetron sputtering

Developing cost-effective components for PEM electrolyzers is key to transforming renewable energy into H2. Platinum is a scarce and expensive material but also, is the most active known catalyst for hydrogen evolution reaction in PEM electrolysis; thus it is of utmost importance to focus on developing electrocatalysts that utilize the minimum amount of platinum without losses in cell performance to make PEM electrolysis affordable. In addition, and with the aim of scaling up electrode production, it is imperative to develop a robust automated industrial manufacturing process to avoid the waste of catalyst during electrode preparation. In this work, magnetron sputtering gas aggregation method is studied for developing three low loading Pt electrodes (0.105, 0.062, and 0.052 mg cm−2), achieving remarkable overpotentials as low as 8 mV at 10 mA cm−2 for HER, showing similar activity than commercial Pt electrodes with more than 4 times less Pt (compared to 0.3 mgcm−2 Pt commercial GDL).

Numerical simulation of molybdenum disulfide-carbon nanotubes/water hybrid nanofluid flow due to a stretching cylinder: Applications to industrial manufacturing processes

This study delves into the exploration of hybrid nanofluids within the context of a stretching cylinder, a domain that has captivated numerous researchers owing to its pivotal applications in industrial manufacturing processes, particularly in metal forming and stretch dies. The authors, recognizing the significance of these applications, have introduced a novel heat transfer fluid termed hybrid nanofluid, comprising molybdenum disulfide and carbon nanotubes suspended in the base liquid, water. The investigation focuses on the flow of the hybrid nanofluid within a stretching cylinder, considering various influential factors such as Joule heating, thermal radiation, porous medium, and magnetic field effects. To model this complex problem, we employed modified Navier–Stokes equations. Employing similarity transformations, assumptions, and non-dimensional parameters, the problem was effectively simplified. The MATLAB bvp4c technique, a well-established numerical approach, was then employed to solve the resulting mathematical formulation. Graphical representations are presented to illustrate various aspects of the flow, facilitating a comprehensive understanding of the system. Comparisons are drawn among the flow characteristics of mono nanofluid, and the developed hybrid nanofluid. It is noted from the current analysis that the temperature strength increases with higher values of the magnetic field parameter, curvature parameter, radiation parameter, and Eckert number in both fluid cases. The Nusselt number increases with higher values of Prandtl number and thermal relaxation parameter. The identified patterns in velocity distribution, temperature strength, and fluid behavior provide a valuable foundation for optimizing thermal efficiency in diverse industrial applications. By leveraging the insights gained from this research, manufacturers can make informed decisions to enhance heat transfer processes, particularly in areas such as metal forming and stretch dies.

Bioaerosol Emission from Vortex Liquids Contaminated with Escherichia coli Bacteria

Vortex fluids are often present in natural and artificial aquatic environments and are also widely used in industrial water treatment and product manufacturing processes. Vortex processes have been studied quite extensively; however, little attention has been paid to the potential release of biological aerosols to the ambient air in common situations involving microbial-contaminated vortex liquids. The model organism was Escherichia coli, a common Gram-negative coliform bacterium widely present in the aquatic and air environments. This study examines the influence of various parameters, including liquid rotation speed, column height, temperature, surface tension and vessel size, on the rate of bioaerosol formation. A commonly used single-stage bioaerosol impactor was employed to collect microbial aerosols at different process parameters under controlled laboratory conditions. The main results show that bioaerosol production increases markedly with increasing rotation speed, reaching a maximum rate at the highest value used in this project (1300 rpm). The tallness of the liquid column is strongly responsible for the bioaerosol production efficiency reaching a difference of almost one order of magnitude along the range between 45 mm (highest bioaerosol release) and 110 mm used in this research. Fluid temperature and surface tension are also very influential parameters responsible for bioaerosol generation during fluid vortex motion; corresponding results are discussed in this manuscript.

A Review of Waterjet Cutting Research towards microAWJ and the Definition of the Waterjet Digital Twin.

This review paper aimed to draw the red line passing through almost 25 years of research on waterjet cutting carried out at WJ_Lab, the waterjet laboratory of the Department of Mechanical Engineering of Politecnico di Milano. The purpose was not to just historically analyse the obtained scientific results by themselves but to make them even more useful by introducing the concept of the waterjet digital twin passing through the accuracy improvements due to microAWJ. This strategy effectively creates synergy among the topics and gives the opportunity to researchers in this field to both have an example of how research in industrial manufacturing processes can be guided by scientific and industrial needs, at least from the author's point of view, and to appreciate how it can be made useful for further improvements by introducing a powerful concept as the digital twin.

Developments in Atomistic and Nano Structure Evolution Mechanisms of Molten Slag Using Atomistic Simulation Methods.

Molten slag has different properties depending on its composition. The relationship between its composition, structure, and properties has been the focus of attention in industrial manufacturing processes. This review describes the atomistic scale mechanisms by which oxides of different compositions affect the properties and structure of slag, and depicts the current state of research in the atomic simulation of molten slag. At present, the research on the macroscopic properties of molten slag mainly focuses on viscosity, free-running temperature, melting point, and desulphurization capacity. Regulating the composition has become the most direct and effective way to control slag properties. Analysis of the microevolution mechanism is the fundamental way to grasp the macroscopic properties. The microstructural evolution mechanism, especially at the atomic and nanoscale of molten slag, is reviewed from three aspects: basic oxides, acidic oxides, and amphoteric oxides. The evolution of macroscopic properties is analyzed in depth through the evolution of the atomic structure. Resolution of the macroscopic properties of molten slag by the atomic structure plays a crucial role in the development of fundamental theories of physicochemistry.

The Chalkboard: An Introduction to Electrochemical Separations

Chemical separations are a cornerstone of industrial manufacturing processes that generate products and services that have improved the standard of living for humans across the globe. To give some context as to how ubiquitous separations are in the modern world, they are involved in the production of fuels, medicines, clean water, fertilizers, materials used in semiconductor chip manufacturing, and other goods. A 2019 report by the National Academies of Sciences, Engineering, and Medicine highlights that chemical separations account for about 10 to 15% of energy use in the United States. Of the four broad sectors (residential, transportation, industry, and commerce) that use energy in the United States, industry has the largest use at 32% and about half of the energy use in industry hails from separations. It is likely that the transportation and residential sectors will experience significant decarbonization in the next 25 years with the proliferation of wind and solar energy sources coupled with electrochemical energy storage and electrification of vehicles. Industrial decarbonization, on the other hand, is far more complex and challenging and it is imperative that future engineers and scientists work hard to devise alternative processes that can be powered on renewable electrons while generating little waste to produce the goods and services that make up our modern lives.

Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field

Implementing green and sustainable development strategies has become essential for industrial robot manufacturing companies to fulfill their societal obligations. By enhancing assembly efficiency and minimizing energy consumption in workshops, these enterprises can differentiate themselves in the fiercely competitive market landscape and ultimately bolster their financial gains. Consequently, this study focuses on examining the collaborative assembly challenges associated with three crucial parts: the body, electrical cabinet, and pipeline pack, within the industrial robot manufacturing process. Considering the energy consumption during both active and idle periods of the industrial robot workshop assembly system, this paper presents a multi-stage energy-efficient scheduling model to minimize the total energy consumption. Two classes of heuristic algorithms are proposed to address this model. Our contribution is the restructuring of the existing complex mathematical programming model, based on the structural properties of scheduling sub-problems across multiple stages. This reformation not only effectively reduces the variable scale and eliminates redundant constraints, but also enables the Gurobi solver to tackle large-scale problems. Extensive experimental results indicate that compared to traditional workshop experience, the constructed green scheduling model and algorithm can provide more precise guidance for the assembly process in the workshop. Regarding total energy consumption, the assembly plans obtained through our designed model and algorithm exhibit approximately 3% lower energy consumption than conventional workshop experience-based approaches.

Red phenanthrenequinone dyes with high thermal and photo-stability for LCD color filters

Color filters (CF) are essential integral parts of the liquid crystal display (LCD) and image sensor. Compared to traditional pigment colorants, organic dyes offer a promising alternative to realize CF with improved optical properties and higher resolution. However, organic dyes usually suffer from inferior thermal and photo-stability, limiting their applications in CF. Herein, four robust donor-acceptor organic dyes based on phenanthrenequinone chromophore were designed and synthesized by introducing aromatic amine derivatives as the electron donors. The strong donor-acceptor interactions in these dyes contribute to intense broad charge-transfer absorption bands from 375 nm to 600 nm with high molar extinction coefficients, thus realizing red color. Also, these dyes exhibit high thermal and photo-stability, good compatibility with the polymer binder and low aggregation tendencies, resulting in high thermal and photo-stability for the fabricated CF films. Remarkably, among these phenanthrenequinone dyes, DTPAP with multi-donor substitution and extended π-conjugation exhibited the best performance in terms of molar extinction coefficient, thermal and photo-stability, contributing to the highest thermal and photo-stability for the CF film with color difference values (ΔEab) below 0.75 under simulated industrial manufacturing processes. This work confirms that phenanthrenequinone is a robust chromophore and is conducive to developing thermal and photo-stable CF dyes, and paves a way to improve the optical properties and stability of donor-acceptor dyes for CF.

Simulation analysis of hazardous chemicals released from the furniture plant using ALOHA software.

The manufacturing of wooden furniture is extensive in Thailand's east. Hazardous chemicals were used in the wooden furniture industry's manufacturing process. Hazardous substances released into the surrounding atmosphere appear to have an impact on the environment and individuals. The ALOHA model is frequently used to assess hazardous chemicals released into the environment; this simulation model is an effective tool for modeling chemical compounds and detecting chemical disasters. It has a tremendous potential for preventing mishaps in potentially hazardous or emergency situations. Acetone and butyl acetate were extracted from the hardwood furniture business to identify accidents such as leaking, spillage, and evaporation. It is described as a highly poisonous, combustible, and explosive material. Toxic accident releases have negative implications for the surrounding areas. The goal of this work was to examine each accident using ALOHA software, and the computation of acetone and butyl acetate accidents was shown in this study. This project provides critical data for the furniture plant's chemical emergency rescue strategy as well as recommendations for emergency evacuation site decision-making.

Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing

AbstractNowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in industrial processes. In particular, reinforcement learning (RL) techniques have made significant advancements, enabling their application to data-driven problem-solving for the control of complex systems. Since industrial manufacturing processes can be treated as MIMO non-linear systems, RL can be used to develop complex data-driven intelligent decision-making or control systems. In this work, the workflow for developing a RL application for industrial manufacturing processes, including reward function setup, development of reduced order models and control policy construction, is addressed, and a new process-based reward function is proposed. To showcase the proposed approach, a case study is developed with reference to a wire arc additive manufacturing (WAAM) process. Based on experimental tests, a Reduced Order Model of the system is obtained and a Deep Deterministic Policy Gradient Controller is trained with aim to produce a simple geometry. Particular attention is given to the sim-to-real process by developing a WAAM simulator which allows to simulate the process in a realistic environment and to generate the code to be deployed on the motion platform controller.