Manufacturing Stage Research Articles

Statistical Methods for Detecting Recycled Electronics: From ICs to PCBs and Beyond

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Globalization of semiconductor industry has led to unprecedented challenges in design, verification, test, security and trustworthiness of contemporary electronics. With parts sourced from various suppliers across the globe, who are beyond the auspices of the original designers and system integrators, serious concerns arise regarding integrity and trustworthiness of electronic circuits, which can be compromised by malicious actors at any stage of the component manufacturing and system assembly process. Among the variety of such concerns, recycled electronics sold as new by malicious suppliers/vendors pose a key threat to mission-critical end applications such as military systems, financial institutions, transportation security, and poser distribution infrastructure. Recycled electronics may experience performance degradation due to aging and, more importantly, may have shorter-than-expected lifetime, thereby raising serious reliability and safety issues. To combat this problem, statistical methods have been proposed in the past decade as a time-efficient and cost-effective approach to identify recycled electronics sold as new. Initially demonstrating in the context of detecting recycled Integrated Circuits (ICs), such methods are based on using statistics and machine learning to outline the parametric signatures of known brand-new ICs and, thereby, weed out ICs that do not fit the expected profile. This article summarizes the current state of knowledge in recycled IC detection schemes and discusses how these methods have inspired, over time, the development of similar solutions in a plethora of domains, including identification of recycled Field Programmable Gate Arrays (FPGAs), recycled hardware components in a network, and recycled Static Random-Access Memories (SRAMs)/Flash memories</i> .

Route to the Adoption of Quantitative NMR in the Japanese Pharmacopoeia

Quantitative NMR (qNMR) is employed to determine the purity of reagents used as standards for HPLC quantification in the Japanese Pharmacopoeia (JP) and has become recognized as a new absolute quantification method in various fields such as pharmaceuticals, foods, and food additives. This report outlines how and why qNMR has been adopted as an official method in the JP and introduces its progression from JP16 to JP18. The results of a survey of companies in the Japan Pharmaceutical Manufacturers' Association regarding how and when to use qNMR from development to manufacturing stages are introduced. The issues involved in the expansion of the use of qNMR in the field of chemical pharmaceuticals in 2017 are discussed and how these were resolved.

Research on the Accounting and Prediction of Carbon Emission from Wave Energy Convertor Based on the Whole Lifecycle

Wave energy, as a significant renewable and clean energy source with vast global reserves, exhibits no greenhouse gas or other pollution during real-sea operational conditions. However, throughout the entire lifecycle, wave energy convertors can produce additional CO2 emissions due to the use of raw materials and emissions during transportation. Based on laboratory test data from a wave energy convertor model, this study ensures consistency between the model and the actual sea-deployed wave energy convertors in terms of performance, materials, and geometric shapes using similarity criteria. Carbon emission factors from China, the European Union, Brazil, and Japan are selected to predict the carbon emissions of wave energy convertors in real-sea conditions. The research indicates: (1) The predicted carbon emission coefficient for unit electricity generation (EFco2) of wave energy is 0.008–0.057 kg CO2/kWh; when the traditional steel production mode is adopted, the EFco2 in this paper is 0.014–0.059 kg CO2/kWh, similar to existing research conclusions for the emission factor of CO2 for wave energy convertor (0.012–0.050 kg CO2/kWh). The predicted data on carbon emissions in the lifecycle of wave energy convertors aligns closely with actual operational data. (2) The main source of carbon emissions in the life cycle of a wave energy converter, excluding the recycling of manufacturing metal materials, is the manufacturing stage, which accounts for 90% of the total carbon emissions. When the recycling of manufacturing metal materials is considered, the carbon emissions in the manufacturing stage are reduced, and the carbon emissions in the transport stage are increased, from about 7% to about 20%. (3) Under the most ideal conditions, the carbon payback period for a wave energy convertor ranges from 0.28 to 2.06 years, and the carbon reduction during the design lifespan (20 years) varies from 238.33 t CO2 (minimum) to 261.80 t CO2 (maximum).

Life cycle assessment of solar PV water pumping system for carbon emissions reduction at a location: a case study

ABSTRACT The design of water supply systems powered by solar photovoltaic (PV) systems are installed for irrigation and other allied applications at remote rural areas which lack proper on-grid power systems. These systems have been operating successfully for several decades at locations receiving ample solar radiation, assisting in the reduction of carbon emissions. Hybrid life cycle assessment was performed in this study to determine the carbon emissions for a solar PV water pumping system (SPVWPS) in Xilin Gol League, Inner Mongolia. The carbon emission (g/mm3) produced by 1 m3 of water raised by 1 m was used as a reference in this assessment. The present analysis estimates the net CO2 emissions generated during the stages of production, transportation, construction, operation, maintenance, and recycling of SPVWPS. The results from SPVWPS were compared with CO2 emissions from diesel operated pumping systems. It was discovered that the carbon emission intensity produced during the 25-year operation period of SPVWPS was 0.80 g/mm3. The major CO2 emissions of SPVWPS were generated during the manufacturing stage totaled 2.02t, accounting for 55.19% of total emissions. When compared to the mobile diesel-powered water supply project, the CO2 emission intensity from SWPS was found to be about 0.55 times lower by about 1.46 g/mm3. As a result, the rapid development of SPVWPS in rural and pastoral areas represents the realization of rural low-carbon transformation and development, as well as the achievement of China’s “double carbon” reduction targets by 2050.

Life cycle environmental analysis of offshore wind power: A case study of the large-scale offshore wind farm in China

China's decarbonization is indispensable for the large-scale utilization of renewable energy. In this process, the development of offshore wind energy has become an important support, because in the eastern region of China, especially the coastal areas, there's the most load requirements but limited onshore renewable resources. Under the goals of both carbon neutral and sustainable development, however, there's still a research gap in evaluating the environmental impacts of large-scale offshore wind plants in China. In this study, the research performed a comprehensive process-based life cycle environmental analysis of a large-scale (400 MW) offshore wind farm with large wind turbine units (5 MW) in China. Global Warming Potential is 25.73 g CO2-eq/kWh and greenhouse gas payback time is calculated as 12.05 months. Fossil fuel consumption amounts to 0.31 MJ/kWh with an energy payback period of 25.56 months. Material resources and ecotoxicity to freshwater need more attention with the values of 1.68 CTUe/kWh and 3.32 10−6 kg Sb-eq/kWh, respectively. The manufacturing stage contributes most to all the impacts examined. The disposal and recycling process shows significant environmental benefits for most impacts but exerts burdens on the particulate matter. Focusing on Global Warming Potential, the wind turbine generation set has the highest effect, among which the blades contribute 73.2% of it. Further, the evolution of electricity and heating mix of China and use of biomass-based precursors could decrease 57% of the blades' greenhouse gas emissions in 2050. This study could provide guidance on the sustainable design and policy-making of offshore wind farms.

Applications of Life Cycle Assessment in the Chocolate Industry: A State-of-the-Art Analysis Based on Systematic Review.

Chocolate is a popular food for its unique flavor and taste, rich nutritional value, and the psychological values brought to people. The raw material production of chocolate, product manufacturing, sales and transportation have different degrees of environmental impact. This review explores the environmental hot spots in the life cycle of chocolate and puts forward corresponding suggestions for the improvement. By applying a systematic review method, this paper collected 25 articles on life cycle assessment (LCA) of the environmental impact of the chocolate industry. It is found that the life cycle of chocolate has the highest environmental impact in the raw material production and chocolate manufacturing stages (accounting for 77-97% of total impacts), among which milk powder, sugar and cocoa derivatives are the important contributors to significant environmental burden. Dark chocolate generates the lowest carbon emissions (1.67 kg CO2 eq/kg product) among existing chocolate categories, while the chocolate confectionery products release the highest carbon emissions (6.76 kg CO2 eq/kg product) among chocolate-containing products. Improvement measures are proposed for reducing environmental impacts and for selecting environmentally friendly product formulae. This study can provide benchmarking for the chocolate industry and improves the understanding of life cycle environmental impacts of chocolate products.

Study of methods of energy efficiency improvement considering operation modes of traction electric drive with use of the methods of virtual mathematical modelling

BACKGROUND: Currently, the problem of making wheeled vehicles more environmentally-friendly is one of the most relevant, as using electric vehicles with on-board traction batteries becomes the most popular technical solution. However, the batteries capacity remains relatively low, so efficiency of using them depends on selection the most optimal components of traction electric equipment and implementing the most advanced algorithms of traction electric drive control. Therefore, development of the methods helping to achieve maximal energy efficiency at all stages of design, manufacturing and operation is highly important. AIM: Development of fundamentals and methods of improvement of wheeled vehicles energy efficiency at the design stage with the use of virtual mathematical modelling. METHODS: The study was conducted with the MATLAB/Simulink software package. RESULTS: Theoretical basis of the improvement methods with the use of mathematical modelling of virtual operation of the vehicle’s digital twin in the MATLAB/Simulink is given in the paper. CONCLUSIONS: The practical value of the study lies in ability of using the proposed methods in development of prosperous wheeled vehicles.

The influence of deformation affected region on microstructure and mechanical property of 316L fabricated by hybrid additive-subtractive manufacturing

The hybrid of additive manufacturing and subtractive manufacturing is promising in producing high-quality and high-accuracy parts made of hard-to-machine metallic materials with complex geometries. However, few studies have been conducted to analyze the influence of responses from subtractive manufacturing, especially the plastic deformation region induced by mechanical cutting, on the following additive manufacturing processes. This paper comprehensively investigates the impact of finishing milling on the mechanical properties and quality of 316L fabricated by hybrid additive-subtractive manufacturing. The whole manufacturing process consisted of a two-stage micro selective laser melting μSLM process with three scanning speeds and one finishing milling process. Typical machining responses including phase, cutting force, surface roughness at different manufacturing stages were analyzed; the mechanisms of how the deformation-affected region induced in subtractive manufacturing process affects the microstructure, hardness, and mechanical properties after the second-stage additive manufacturing were explored. Fine grains were formed after the second-stage μSLM in the connected region, because of the lower G/R ratio and the higher G × R value, whereas the hardness in this region was lower than the bulk material consisting of coarse grains, caused by the thermal softening effect induced in mechanical cutting. The elongation rate was increased due to the existence of the grain-boundary-rich connected region.

The feasibility of Raman spectroscopy for accurate assessment of essential criteria in pharmaceutical industry by investigation of Metformin hydrochloride tableting process

AbstractAnalytical and real‐time technology in pharmaceutical manufacturing process is an important need to ensure that manufactured drugs are safe and effective. Raman spectroscopy is an emerging technique that is able to perform quantitative analysis nondestructively due to the molecular structure of many drugs. Monitoring the content uniformity and quantification of active pharmaceutical ingredient (API) in the tablet preparation process, without the assistance of solvent, is one of the key concerns in the formulation design in order to provide stable, pure, and homogenous finished products. In this study, we investigated the possibility of using Raman data as an analytical method to quantify API, the intra‐ and inter‐sample uniformity of content in the tableting process of Metformin hydrochloride tablets (C4H11N5.HCl). Analysis of all standard samples for prediction of API uniformity represents an acceptable accuracy and precision with a relative standard deviation of 2.55%. Further investigation of tablets regarding to relative Raman intensity of some characteristic peaks demonstrates the amount of API content with an accuracy of ≥96%. These values have a good adaption with pharmacopeia monograph. Findings reveal that the Raman method can be routinely utilized for quantifying API, controlling the content uniformity and the stability of drugs in different stages of manufacturing.

Determination of Nitrosamines in Various Pharmaceuticals at Variable Temperatures

Nitrosamines have been classified as potent genotoxic agents for humans by the International Agency for Research on Cancer. Our study aimed to determine the levels of nitrosamine impurities that could be formed in the content of drugs under different temperature conditions during their shelf life using chromatographic analysis. Eleven drugs in pharmacies were subjected to long-term exposure at two different temperatures. Twelve nitrosamine impurities of all samples were performed using LC-MS/MS. When the impurity levels of the analyzed drugs were examined, no nitrosamine impurity was detected in any drugs. Our study revealed that if no impurity was detected under storage conditions, there was no impurity formation even when the temperature was increased. When impurity formation is effectively prevented during the manufacturing stage, the risk of impurity occurrence during the shelf-life of drugs belonging to the same group is estimated to be low.

Reducing the Environmental Impacts of Plastic Cosmetic Packaging: A Multi-Attribute Life Cycle Assessment

The global packaging industry has been growing significantly, resulting in an increase in waste and emissions. Social responsibilities, regulations and targets are shifting companies’ priorities to various sustainable practices. This study comprised a life cycle assessment (LCA) to quantify and compare key initiatives influencing the sustainability of plastic cosmetic packaging. The life cycle environmental effects of dematerialisation, recycled content, energy-saving initiatives and renewable energy powering the manufacturing processes, and the end-of-life (EoL) recycling rates of various scenarios, were evaluated. Moreover, a variety of fossil-based and bio-based polymers, such as acrylonitrile butadiene styrene (ABS), polypropylene (PP), polyethylene terephthalate (PET), wood–polymer composite (WPC) and polylactic acid (PLA), were considered. The study determined that dematerialisation and recycled content had the most beneficial impacts on packaging sustainability. When 100% recycled materials were used, an overall impact reduction of 42–60% was noted for all the materials considered. Using 100% renewable energy and applying measures to reduce the energy consumption in the manufacturing stage by 50% reduced the total impact by approximately 9–17% and 7–13%, respectively. Furthermore, it was concluded that PP had the lowest environmental impacts in the majority of the case scenarios considered, by an average of 46%.

Uncovering the Dynamic Alterations of Volatile Components in Sweet and Floral Aroma Black Tea during Processing.

Aroma is an indispensable factor that substantially impacts the quality assessment of black tea. This study aims to uncover the dynamic alterations in the sweet and floral aroma black tea (SFABT) throughout various manufacturing stages using a comprehensive analytical approach integrating gas chromatography electronic nose, gas chromatography-ion mobility spectrometry (GC-IMS), and gas chromatography-mass spectrometry (GC-MS). Notable alterations in volatile components were discerned during processing, predominantly during the rolling stage. A total of 59 typical volatile compounds were identified through GC-IMS, whereas 106 volatile components were recognized via GC-MS throughout the entire manufacturing process. Among them, 14 volatile compounds, such as linalool, β-ionone, dimethyl sulfide, and 1-octen-3-ol, stood out as characteristic components responsible for SFABT with relative odor activity values exceeding one. This study serves as an invaluable theoretical platform for strategic controllable processing of superior-quality black tea.

What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing.

Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models, there are few that explain how to develop a robust model that extracts the highest possible performance from the available data, especially in support of pharmaceutical applications. This tutorial will describe pitfalls and best practices for developing and validating predictive models with a specific application to a monitoring a pharmaceutical manufacturing process. The pitfalls and best practices will be highlighted to call attention to specific points that are not generally discussed in other resources.

DEVELOPMENT OF GENERAL METHODOLOGICAL REQUIREMENTS FOR THE IMPLEMENTATION AND CONSTRUCTION OF TOOLS FOR ORGANIZATIONAL-TECHNOLOGICAL SUPPORT OF CONSTRUCTION PROJECTS

The article is devoted to the introduction and substantiation of an innovative methodological platform and scientific and applied tools, which are designed to ensure the successful administration of operational activities and the growth of qualifying assets of the enterprise-developer in construction as project management (as part of the enterprise's multi-project business portfolio) and administrative-regulatory activities in the micro-environment of institutional sub- construction sites and stakeholders. The rapid complication of construction production systems causes an increase in the number of sequentially connected elements (crews, mechanisms, vehicles, suppliers, etc.), which, according to the basic law of reliability theory, reduces the reliability of the entire system in proportion to the number of elements in a geometric progression. Consideration of reliability as the stability of numerous specific qualities of building systems in relation to all possible deviations of the given mode of operation of the system requires system-technical design, manufacturing, construction, operation and other stages of the life cycle of building systems. Most often, the definition of the roles of project executors corresponds to the life cycle model. On the basis of the methodology, as introduced in accordance with the new ideas about the essence of the developer's operating system in construction, a new scientific and applied toolkit for the administration of the operational activities of the enterprise executing construction projects and its transformations based on the application of modular construction technology - prefabrication was developed and substantiated. Prefabrication is the preliminary production of building structures in industrial production. Prefabricated structures are delivered to the construction site and used in the construction process. This approach to construction has been revolutionary because it enables standardization, increased quality control, and reduced overall construction time.

Evaluating the Atypical Aging Potential Development in Sparkling Wines Can Be Achieved by Assessing the Base Wines at the End of the Alcoholic Fermentation.

Traditional sparkling wine production is a lengthy and costly process, involving a double fermentation step and a period of aging sur lie; thus, monitoring quality during the key manufacturing stages is crucial. The effects of the second fermentation on the development of 2-aminoacetophenone (AAP), the main marker of the atypical aging (ATA) defect, were investigated on 55 base wines (BWs) and corresponding sparkling wines (SWs) produced in an experimental winery. While the AAP content of the SWs was observed to be higher than the BWs, it was found that an artificial aging test carried out on the BWs could be a good predictor of ATA development in SWs. Further, the antioxidant capacity of the SWs was noticed to correlate well with the potential AAP formed during accelerated aging. Finally, an analysis of covariance (ANCOVA) model of linearization capable of predicting AAP formation in SWs using the data obtained from the corresponding BWs was created.

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R2 while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications.

Comparison of life cycle assessment between hydrogen production from silicon waste and alkaline water electrolysis

With global warming becoming increasingly severe, environmental issues are receiving international attention. Crystalline silicon is an indispensable and important raw material for photovoltaic and semiconductor fields, but the cutting of crystalline silicon materials generates a large amount of silicon wastes. This article evaluates the environmental impact of a hydrogen production process using diamond-wire sawing silicon waste (DSSW) using the life cycle assessment (LCA) methodology. For comparison, it was also analyzed the environmental impact of the alkaline water electrolysis (AEL) hydrogen production route. In the DSSW alkaline catalyzed hydrolysis (DACH) hydrogen production route, the hydrogen production stage accounts for the main contribution of nine environmental impact indexes, including GWP, PED, ADP, AP, EP, ODP, ET, HT-cancer, and HT-non cancer, exceeding 56 %. Whereas for the AEL route, the environmental impacts of the electrolytic cell manufacturing stage can be neglected, and the operating stage contributes almost all the environmental impacts, contributing more than 92 % to the twelve environmental impact indexes. Compared to the AEL route, the DACH route has higher environmental impacts, with GWP index reaching 87.78 kg CO2 -eq/kg H2, PED index reaching 1772.90 MJ/kg H2, and IWU index reaching 622.37 kg/kg H2 which are 2.85, 4.07 and 7.56 times higher than the former, respectively. Although the environmental impact of the DACH route is significant, most of its indirect impacts were caused by the use of raw materials, and the energy consumption and direct environmental impact are both low. The environmental impact of the AEL route is mainly indirect effects generated due to the use of electricity. If clean renewable energy sources (e.g., solar PV, hydropower, geothermal or biofuels), were used for the AEL route, all twelve environmental impact indexes would be significantly reduced.

A.I. INTO FASHION PROCESSES

The article aims to provide a comprehensive understanding of Artificial Intelligence (AI) and its integration into fashion processes, focusing on the research, design, development, and manufacturing stages. First, it offers an overview of AI evolution, from its early developments to the contemporary advanced Machine and Deep learning models, attempting to tackle the challenge of ambiguous terminology and aiming to deal with the different interpretations of AI capabilities. Subsequently, a review of the perspectives on the integration of AI tools within fashion processes will be presented. This overview will underscore the growing need for industries to undergo a conscious technological transformation, adopting AI toward a more sustainable and responsible fashion evolution.

Analysis of gaseous emission and particle number size distributions in metal casting processes with binder jetting moulds

The impact on indoor air quality during metal casting processes using the 3D printing (3DP) binder jetting technique to manufacture moulds is analyzed in this research. This study investigates the particle size distribution and gas concentration during the manufacturing stages of 3DP moulds, with focus on particle deposition in the human respiratory system. The goal is to understand the risks associated with particle and gas emissions in binder jetting 3D printing, and thus improve worker safety. The results indicate that particle emission rates are lowest during printing (0.01 1011particles min−1) but increase during post-processing and melting-pouring phases (3.07 1011 particles min−1). During melting and pouring, the emission of particles was mainly in the range 7.9–71 nm, with values 10 times higher than in the previous phases. The study calculates the deposition of inhaled particles in different parts of the respiratory tract and reveals a 2.39 times higher extrathoracic mass deposition during 3DP mould manufacturing stages compared to melting-pouring stages. Additionally, alveolar mass deposition is higher during melting-pouring stages due to an increase in ultrafine emission rates. The study emphasizes the importance of insulation equipment and workplace ventilation in indoor spaces, particularly during heating processes that generate ultrafine particles through nucleation, so that health risks can be mitigated.

Cemented Carbide End-Mill Edge Preparation Using Dry-Electropolishing

Precision edge preparation techniques for cemented carbides enable optimization of the geometry of tools’ cutting edges. These techniques are frequently used in high-stress environments, resulting in substantial improvements in tools’ cutting performance. This investigation examined the impact and evolution of cutting edge parameters and resulting surface finishes as a function of dry-electropolishing time on an end-mill. Findings demonstrate enlargement of the cutting edge radius, a decrease in surface roughness, and the mitigation of defects induced during previous manufacturing stages (i.e., smashed ceramic particles, burrs, chipping, etc.). Additionally, a direct correlation between dry-electropolishing time and primary cutting edges’ micro-geometry parameters has been established.