Manufacturing Information Research Articles

Decision-making of Enterprise Greenwashing Behavior Under The Background of Carbon Tariff

Research background: As the EU deals with carbon leakage through carbon tariffs, export enterprises of some countries that do not meet the carbon emission standards face the problem of increasing export costs, so these enterprises have the tendency of "green washing". Therefore, how enterprises make decisions to deal with carbon tariffs and avoid the impact of changes in the terms of trade has become a hot topic. Based on the cost constraints of enterprises in developing countries, the information asymmetry between upstream and downstream enterprises, and the external environment constraints such as government and enterprise regulation and subsidy strategies, enterprises will make different decisions, including "brown enterprise" behavior that only considers economic benefits and "green enterprise" behavior that considers both economic benefits and environmental benefits. The research approach in this paper is based on game-theoretic analysis and adopts the supply chain game model proposed by Pretis (2022) [1]and Dong (2023[2]). This paper mainly discusses the different situations of maximizing the profit of individual enterprise and the profit of the whole supply chain through the game of the manufacturer's cost information advantage and the retailer's market information advantage. Finally, let business and government work together to get business to cancel the green washing practices that have been adopted. Conclusion: Based on the hypothesis that the EU is imposing carbon tariffs on developing countries, the greenwashing behavior of manufacturers and retailers is related to the green investment return rate of enterprises, the cost and rate of return of blockchain technology, and government subsidies.

Is Transparency Good in a Supply Chain? The Downside of Information Acquisition Transparency

ABSTRACTIn the supply chain, retailers usually have inherent advantages over manufacturers in obtaining demand information because they are closer to the final market and can directly interact with consumers. However, manufacturers cannot make sure whether their retailers have obtained extra demand information. The retailer's information acquisition state is also an important aspect of information asymmetry among supply chain participants, besides the demand information itself. We examine two modes observed in practice regarding the transparency of the retailer's information acquisition state: non‐transparency and full transparency. By incorporating a manufacturer's information belief adjustment strategy, our study thus addresses a retailer's acquisition and ex‐post sharing of information (i.e., the decision on how to share acquired information) under the two transparency modes. The main findings show that under the non‐transparency mode, the retailer shares low‐demand information but hides high‐demand information. With respect to the interim profit (i.e., after the acquisition cost is realized), the preferences of the manufacturer, retailer, and supply chain on the two modes depend on the information acquisition cost. With respect to the ex‐ante profit (i.e., before acquisition cost is realized), surprisingly, the manufacturer and supply chain always prefer the non‐transparency mode, whereas the retailer's preference for the two modes still depends on the acquisition cost. In addition, our equilibrium outcome reveals that the retailer and the manufacturer will never sign an internal information sharing agreement (i.e., never achieve the full transparency mode), whether before or after the realization of information acquisition cost. We also find that the expected consumer surplus under the non‐transparency mode is always higher than that under the full transparency mode, which is also a counterintuitive outcome. Finally, our main findings in the core model are relatively robust when considering acquisition cost sharing, general demand distribution, general acquisition cost distribution, ex‐ante informed retailer, and imperfect information acquisition.

A Study On Effects of Packaging of FMCG Products On the Buying Behavior of Consumers with Reference to Tirupattur District

The present investigation was carried out with 100 respondents from Tirupattur District. Convenience sampling technique was used for the present study for collection of data. The study was conducted by using both Primary and Secondary data. A structured questionnaire was prepared for the present study. The primary data for the present study was collected with the help of a questionnaire. The secondary data for the present study was collected from journals, magazines, researcher newspapers, and websites. Findings revealed that the impact of packaging and its elements consumer’s purchase decision is quite Signiant in respect to the consumer’s varied choice. For this purpose, the Package’s elements were identified on the basis of its graphics quality, color, size, form, and material of packaging being used on the product. Packaging is the prime force to provide information to the consumer and should satisfy legal requirements for product identification nutritional value, ingredient declaration, net weight, and manufacturer information. A package is the face of a product and the only product exposure consumers experience prior to purchase. Consequently, distinctive or innovative packaging can boost sales in a competitive environment. The concerned study has analyzed the consumer buying behaviour of food packaging products and has examined the customer preference to buy different food products. It has also analyzed the reasons for customer preferences over the commonly preferred food product brands in the market.

Developing a BIM-enabled robotic manufacturing framework to facilitate mass customization of prefabricated buildings

Industrialized construction has been accepted as an effective production method for building project stakeholders to improve installation quality. Recent advancements in industrialized construction have focused on parametric designs for manufacturing and assembly to ensure accurate information flows and workflows across different project stages, however, they have not adequately addressed the challenges in mass customization of building projects to meet the diverse needs of communities. This study develops a technological framework based on Building Information Modeling (BIM) processes for mass customization of prefabricated buildings, which consists of parametric design and robotic manufacturing (RM) information flows to improve design flexibility and manufacturing precision. A proof of concept case study of a single-family house built with Light Gauge Steel (LGS) wall frames was conducted to demonstrate the usability of the proposed framework. Findings show that the BIM-RM framework not only helps bridge the technological interoperability gap between BIM and RM programs but also contributes to improved scalability, efficiency, and cost-effectiveness of design-to-manufacturing processes in construction projects.

Intelligent manufacturing, auditor selection and audit quality

PurposeThe purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.Design/methodology/approachThis research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.Findings(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.Practical implicationsThis paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.Originality/valueThis study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.

Evaluation of Commercially Available Plant-based Food Serving Disposable Crockery for Quality Standards

Biomass-based disposable crockery products are in high demand as an alternative to the plastic-based products considering negative effects of plastic-based products on environment. Biomass-based cutleries are being made from locally available different sources such as leaves, sheaths, straws and other plant fibres. A study was conducted to assess the quality of the biomass-based utensil product and provide the standard technical requirements of selected biomass-based disposable products. The plant-based utensils made from corn starch, sugarcane bagasse, areca, paper and Sal (Shorea robusta) leaves were selected for this study. The available standard methods such as International Organization for Standardization (ISO), Technical Association of the Pulp and Paper Industry (TAPPI), National Standard of the People’s Republic of China, and Bureau of Indian Standards (BIS) were adapted, with suitable modifications wherever required, for conducting the experiment. The test included measurement of dimensional specifications, grammage, moisture content, temperature resistance test, load bearing test, high heat performance test, drop test, tensile test and three point bending rig test. The plant-based utensils were classified as very strong, strong and weak based on the strength characteristics test. The utensils made from sugarcane bagasse fall in all three categories (very strong, strong and weak), whereas areca-based lies between very strong and strong while corn starch- based fall between strong and weak category. Overall, the results showed a lot of variation in strength characteristics of biomass-based disposable crockery products. The data of this study can serve as a valuable information for manufacturers and suppliers of plant-based utensils to ensure and improve product quality while maintaining the technical standards.

Innovating Pharmaceuticals: The Rise of 3D printing in Drug Delivery.

Additionally known as three-dimensional (3D) printing, additive manufacturing has made major advancements possible in the fields of engineering, business, the arts, education, and medical. Thanks to recent developments, it is now possible to print three-dimensional to create complex, useful living tissues, biocompatible substances, cells, and supporting structures are combined. Regenerative medicine is utilizing 3D bioprinting. Additive manufacturing technology, or 3D printing, has been labelled the “next big thing” and is predicted to overtake cell phones in popularity. 3D printers use digital templates to produce actual, three-dimensional items. Adding layers to a print, commonly referred to as additive manufacturing, allows for the use of more than a hundred different materials, including nylon, metal, and plastic. Applications for 3D printing can be found in many different industries, such as industrial design, manufacturing, dental, automotive, aerospace, civil engineering, education, jewellery, footwear, and geographic information systems. It has shown to be a simple and affordable solution for a variety of use cases. Using computer-aided design tools and programming, three-dimensional printing is a sophisticated technique that adds material to a base surface to create three-dimensional things. Additive layer manufacturing, also referred to as 3D printing, is the technique of creating three-dimensional things by depositing or solidifying material one layer at a time. Using a computer-aided design module, pharmaceutical components are organized in a three-dimensional pattern. Afterwards, the constituents are converted into a machine-readable format resembling the surface of a three-dimensional dosage form. 3D printing has been used for jewelry, shoe-making, architecture, engineering & construction, the automotive industry as well as the aerospace field, dentistry and medicine, plus geographic information systems (GIS), civil engineering and education. After that stage of the process is completed, the surface transferred to the machine is then printed in different layers. Bioprinting is an interdisciplinary domain that integrates additive manufacturing with biology and material sciences to manufacture threedimensional structures representative of living organisms. The ability to create biological tissues and organs has attracted considerable attention in biomedical research owing to the rising demand for personalized medicine. This scenario propelled bioprinting forward which received much interest thus triggering comprehensive research efforts by various players such as companies, universities as well as research institutes. The goal of this book is to provide a thorough analysis of the complex and rapidly evolving field of bioprinting by critically analyzing and evaluating the existing scientific literature.

Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking

The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystallisation of the drug substance, granulation, drying, milling, mixing of the formulation, and compaction. Each step is fraught with manufacturing problems. Undesired adhesion of powders to the surface of the compaction tooling, known as sticking, is a frequent and highly disruptive problem that occurs at the very end of the process chain when the tablet is formed. As alternatives to the mechanistic approaches to address sticking, we introduce two different machine learning strategies to predict sticking directly from the chemical formula of the drug substance, represented by molecular descriptors. An empirical database for sticking behaviour was developed and used to train the machine learning (ML) algorithms to predict sticking characteristics from molecular descriptors. The ML model has successfully classified sticking/non-sticking behaviour of powders with 100% separation. Predictions were made for materials in the Handbook of Pharmaceutical Excipients and a subset of molecules included in the ChemBL database, demonstrating the potential use of machine learning approaches to screen for sticking propensity early during drug discovery and development. This is the first time molecular descriptors and machine learning are used to predict and screen for sticking behaviour. The method has potential to transform the development of medicines by providing manufacturability information at the drug screening stage and is potentially applicable to other manufacturing problems controlled by the chemistry of the drug substance.

Ground, Ceiling and Wall Effect Evaluation of Small Quadcopters in Pressure-controlled Environments

Multicopters are used for a wide range of applications that often involve approaching buildings or navigating enclosed spaces. Opposed to the open spaces in obstacle-free environments commonly flown by fixed-wing unmanned aerial vehicles, multicopters frequently fly close to surfaces and must take into account the airflow variations caused by airflow rebound. Such disturbances must be identified in order to design algorithms capable of compensating them. The evaluation of ground, ceiling and wall effects using two different test stands is proposed in this work. Different propellers and sensors have been considered for testing. The first test setup used was placed inside terraXcube’s large climatic chamber allowing a precise control of temperature and pressure of around 20°C and 1000 hPa, respectively. The second test setup is located at the University of Denver (DU) Unmanned Systems Research Institute (DU2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}SRI) laboratory with a stable pressure of around 800 hPa. Two different fixed 6 degrees of freedom force-torque sensors have been used for the experiments, allowing to sample forces and moments in three orthogonal axes. The tests simulate a hovering situation of a quadcopter at different distances to either the ground, the ceiling or a wall. The influence of the propeller size, rotation speed, pressure and temperature have also been considered and used for later dimensionless coefficient comparison. A thorough analysis of the measurement uncertainty is also included based on experimental evaluations and manufacturer information. Experimental data collected in these tests can be used for the definition of a mathematical model in which the effect of the proximity to the different surfaces is evaluated.

A Data-Driven Approach for Leveraging Inline and Offline Data to Determine the Causes of Monoclonal Antibody Productivity Reduction in the Commercial-Scale Cell Culture Process.

The monoclonal antibody (mAb) manufacturing process comes with high profits and high costs, and thus mAb productivity is of vital importance. However, many factors can impact the cell culture process, and lead to mAb productivity reduction. Nowadays, the biopharma industry is actively employing manufacturing information systems, which enable the integration of both online data and offline data. Although the volume of data is large, related data mining studies for mAb productivity improvement are rare. Therefore, a data-driven approach is proposed in this study to leverage both the inline and offline data of the cell culture process to discover the causes of mAb productivity reduction. The approach consists of four steps, namely data preprocessing, phase division, feature extraction and fusion, and cluster comparing. First, data quality issues are solved during the data preprocessing step. Next, the inline data are divided into several phases based on the moving window k-nearest neighbor method. Then, the inline data features are extracted via functional data analysis and combined with the offline data features. Finally, the causes of mAb productivity reduction are identified using the contrasting clusters via the principal component analysis method. A commercial-scale cell culture process case study is provided in this research to verify the effectiveness of the approach. Data from 35 batches were collected, and each batch contained nine inline variables and seven offline variables. The causes of mAb productivity reduction were identified to be the lack of nutrients, and recommended actions were taken according to the result, which was subsequently proven by six validation batches.

The consumer behavior in the context of counterfeit products: A bibliometric analysis

This article aims to identify the main areas of works devoted to the topic of counterfeit goods/brands consumption and to present the dynamics of changes in researchers’ interest in this subject. The research method used in the article was bibliometric analysis, complemented by content analysis of literature selected from the Scopus database. Research results: The main areas of research on the issue of counterfeit product consumption focused on 1) online consumer behavior and ethical aspects, 2) consumption of counterfeit products and brands, and 3) social aspects of consumption of counterfeit products. The earliest publications focus on purchasing behavior in the context of buying counterfeit products, followed by researchers who have focused on the ethical aspects of consuming counterfeit goods. More recent studies focus on the consumption and trade of counterfeit goods on the Internet. Conclusions: The ever-expanding global trade in counterfeit products poses a major challenge — it erodes business revenue, undermines innovation, and hampers economic growth. To effectively mitigate these phenomena, it is essential to have up-to-date knowledge of the latest scientific advances in the field. Research into the buyer behavior in the context of counterfeit goods can provide useful information for manufacturers about the scale of the problem and the possible need for remedial action.

Orthogonal characterization of rAAV9 reveals unexpected transgene heterogeneity

Recombinant adeno-associated virus (rAAV) is the most widely used viral vector for in vivo human gene therapy. To ensure safety and efficacy of gene therapy products, a comprehensive analytical profile of the rAAVs is needed, which provides crucial information for therapeutic development and manufacturing. Besides information on rAAV quantities and possible contaminating DNA and protein species, assessing rAAV quality is of utmost importance. In vitro biopotency and methods to determine the full/empty ratio of rAAV capsids are commonly applied, but methods to assess the integrity of the viral genome are still rarely used. Here we describe an orthogonal approach to characterize rAAV quality. Two biologically different rAAV9s from different stages of the bioprocess, generated each with two different transfection reagents, were investigated. In vitro biopotency tests in all cases demonstrated that rAAV9s generated with transfection reagent FectoVIR® possessed a higher biological activity. Mass-based analytical methods, such as sedimentation velocity analytical ultracentrifugation (AUC) and mass photometry, showed a high share of full capsids (>80 %) at late process stages but did not detect any differences in the rAAV9s from the different transfection reagents. Multiplex dPCR and Nanopore long-read sequencing both demonstrated that, also in late-stage process samples, sample heterogeneity was relatively high with a rather small share of full-length transgenes of ∼10–40 %. Intriguingly, both methods detected a higher share of complete transgenes in rAAV9 generated with transfection reagent FectoVIR® instead of Polyethylenimine (PEI), and thereby explain the differences already observed in the biopotency assays. This study therefore emphasizes the necessity to utilize multiple, orthogonal methods to gain a better understanding of recombinantly manufactured AAVs.

Exploring the effects of angle of incidence on stabbing resistance in advanced protective textiles: Novel experimental framework and analysis

Despite numerous research investigations to understand the influences of various structural parameters, to the authors' knowledge, no research has been the effect of different angles of incidence on stab response and performance of different types of protective textiles. Three distinct structures of 3D woven textiles and 2D plain weave fabric made with similar high-performance fiber and areal density were designed and manufactured to be tested. Two samples, one composed of a single and the other of 4-panel layers, from each fabric type structure, were prepared, and tested against stabbing at [0°], [22.5°], and [45°] angle of incidence. A new stabbing experimental setup that entertained testing of the specimens at various angles of incidence was engineered and utilized. The stabbing bench is also equipped with magnetic sensors and a UK Home Office Scientific Development Branch (HOSDB)/P1/B sharpness engineered knives to measure the impact velocity and exerted impact energy respectively. A silicon compound was utilized to imprint the Back Face Signature (BFS) on the backing material after every specimen test. Each silicon print was then scanned, digitized, and precisely measured to evaluate the stab response and performance of the specimen based on different performance variables, including Depth of Trauma (DOT), Depth of Penetration (DOP), and Length of Penetration (LOP). Besides, the post-impact surface failure modes of the fabrics were also measured using Image software and analyzed at the microscale level. The results show stab angle of incidence greatly influences the stab response and performance of protective textiles. The outcome of the study could provide not only valuable insights into understanding the stab response and capabilities of protective textiles under different angle of incidence, but also provide valuable information for protective textile manufacturer, armor developer and stab testing and standardizing organizations to consider the angle of incidence while developing, testing, optimizing, and using protective textiles in various applications.

Smart and user-centric manufacturing information recommendation using multimodal learning to support human-robot collaboration in mixed reality environments

The future manufacturing system must be capable of supporting customized mass production while reducing cost and must be flexible enough to accommodate market demands. Additionally, workers must possess the knowledge and skills to adapt to the evolving manufacturing environment. Previous studies have been conducted to provide customized manufacturing information to the worker. However, most have not considered the worker's situation or region of interest (ROI), so they had difficulty providing information tailored to the worker. Thus, a manufacturing information recommendation system should utilize not only manufacturing data but also the worker's situational information and intent to assist the worker in adjusting to the evolving working environment. This study presents a smart and user-centric manufacturing information recommendation system that harnesses the vision and text dual encoder-based multimodal deep learning model to offer the most relevant information based on the worker's vision and query, which can support human-robot collaboration (HRC) in a mixed reality (MR) environment. The proposed recommendation model can assist the worker by analyzing the manufacturing environment image acquired from smart glasses, the worker's specific question, and the related manufacturing document. By establishing correlations between the MR-based visual information and the worker's query using the multimodal deep learning model, the proposed approach identifies the most suitable information to be recommended. Furthermore, the recommended information can be visualized through MR smart glasses to support HRC. For quantitative and qualitative evaluation, we compared the proposed model with existing vision-text dual models, and the results demonstrated that the proposed approach outperformed previous studies. Thus, the proposed approach has the potential to assist workers more effectively in MR-based manufacturing environments, enhancing their overall productivity and adaptability.

Antenna and attitude modeling of modernized GLONASS satellites

Next to the legacy frequency division multiple access (FDMA) signals in the L1 and L2 band, modernized GLONASS satellites of the M+, K1, and K2 generation also transmit new code division multiple access (CDMA) signals on L3, and, in part, the L1 and L2 frequencies. Depending on the specific satellite platform, either a common antenna or two distinct antennas are used for the individual signals. As a novel feature, the new CDMA navigation messages provide information on the antenna phase center locations as well as the spacecraft orientation during rate-limited noon and midnight turns. We establish a comprehensive set of phase center positions for the individual GLONASS antennas from manufacturer data and information in the CDMA navigation messages, inferred from the comparison of FDMA and CDMA broadcast ephemerides, and obtained from antenna baseline estimates using a triple-frequency carrier phase combination. Based on these, reference values for the signal-specific PCOs of each block of modernized GLONASS spacecraft are derived that may be used to extend the current antenna model of the International GNSS Service (IGS) to all frequency bands. Complementary to the analysis of antenna phase center information, the new CDMA attitude model for GLONASS-K satellites is reviewed. Algorithms for computing relevant parameters of the rate-limited yaw slews including the ramp-up and -down phase based on the Sun/orbit geometry are derived that enable a concise attitude modeling in GLONASS orbit determination and precise point positioning. The model is validated through comparison with yaw angle estimates derived from triple-frequency observations showing consistency at the 1° level for the K2 satellite. On the other hand, unexpected accelerations during noon turns of GLONASS-M+ are identified, which may cause short-term yaw angle deviations of up to 20° relative to attitude models in common use within the IGS.

Effect of hemolysis on D-dimer testing measured with the Improgen kit: is all manufacturer information correct?

D-dimer is a fibrin degradation product and its measurement is affected by hemolysis. This study was designed to reveal the value of hemolysis affecting D-dimer in our laboratory. In this study, hemolysate samples obtained by both mechanical and freezing methods were used. D-dimer levels of all plasmas were measured with Improgen Diagnostic kit by immune-turbidimetric method. Numerical change in hemolyzed samples was evaluated by calculating the percentage difference, and clinically significant differences were evaluated by calculating the maximum acceptable bias (MAB). In the hemolysate study prepared by both freeze-thaw and mechanical methods, it was observed that low D-dimer levels did not exceed the total allowable error (TAE) (30%) up to +2 hemolysis (corresponds to hemoglobin = 1.01-2 g/l) and did not exceed the MAB (65%) even at +4 hemolysis (corresponds to hemoglobin = 1.01-2 g/l). High D-dimer levels did not exceed the limit values of both TAE (30%) and MAB (68%) even in +4 hemolysis. The D-dimer test was affected by lower levels of hemolysis compared to both other studies and the values in the kit insert (hemoglobin >5 g/l corresponds to +4 hemolysis index). We verified the hemolysis interference in the D-dimer test, which we thought was not compatible with the kit insert, under our own laboratory conditions. This is the first hemolysis interference study performed with the Improgen brand d-dimer kit. In samples with a hemolysis rate of +2 and above, it would be more accurate to reject the D-dimer result as a 'hemolyzed sample'.

Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

Variable Layer Heights in Wire Arc Additive Manufacturing and WAAM Information Models

In Wire Arc Additive Manufacturing (WAAM), variable layer heights enable the non-parallel or non-planar slicing of parts. In researching variable layer heights, this paper documents printing strategies for a reference geometry whose key features are non-orthogonal growth and unsupported overhangs. The complexity of 3D printing with welding requires parameter optimization to control the deposition of molten material. Thus, 3D printing with welding requires the precise deposition of molten material. Currently, there is no standard solution for the customization of process parameters and intelligent collection of data from sensors. To address this gap in technology, this research develops an Internet of Things (IoT)-enabled, distributed communication protocol to control process parameters, synchronize commands, and integrate device data. To intelligently collect sensor information, this research creates a query-able database during pre-planning and production. This contributes to fundamental research in WAAM by documenting strategies for printing variable layer heights, the customization of control parameters, and the collection of data through a WAAM Information Model (WIM).

Inclusivity of thermal performance parameters for evaluation of solar cookers and the necessity of uniformity in the appraisal using generalized test procedure for different designs

Solar cookers play a vital role in decentralized energy solutions. Therefore, their appropriate rating is essential. The present work reviews and emphasizes the usefulness of thermal performance parameters (TPPs) for assessing solar cookers, such as box type, parabolic dish, panel type, Fresnel lens-based, and their hybrids. Although the TPPs are extensively used for testing solar cookers, several research studies depicted ambiguities, non-uniformities, and inconsistencies in their use. Interestingly, Cooker Opto-Thermal Ratio (COR) is one of the most generalized and, hence, useful TPP for the optical and thermal performance evaluation and rating/grading of multiple designs of solar cookers. In view of non-uniformities and inconsistencies found in determining TPPs, current work provides crucial insights into the existing TPPs, including COR, corresponding test standards/protocols, and quantification of test load(s) for solar cookers. Also, this work justifies the appropriate use of the different TPPs for solar cookers, and the usefulness and robustness of COR determined using linear regression fit of experimental data and derivate COR-dependent objective parameters. Finally, it includes insightful information for researchers, designers, and manufacturers on the future research pathways based on COR and corresponding test procedure and provide useful visions to avoid its deceptive, inconsistent, and inappropriate use.

Dynamic rheological behavior of high-amylose wheat dough during various heating stages: Insight from its starch characteristics

The objective of this study was to understand how the dynamic rheological behaviors of high-amylose wheat (HAW) dough during various heating stages measured using a mixolab were affected by the starch properties. At the heating stage of 30 °C - 90 °C, low minimum (C2) and peak (C3) torques were observed for HAW doughs, which resulted from their reduced starch granule swelling. During holding at 90 °C, HAW doughs had low minimum (C4) and C3 - C4 torques, indicating a good resistance to mechanical shear and endogenous enzyme degradation. HAW doughs also had low final (C5) and setback (C5 - C4) torques, consistent with their low starch swelling power and solubility. The increased amylose in HAW starch formed long-chain double-helical B-type polymorph and amylose-lipid complex, which resulted in high starch gelatinization-temperatures and enthalpy change, low swelling power and solubility, low pasting viscosity, and high resistance of swollen granules to mechanical shear and enzyme degradation. The overall patterns of dough-rheological behavior of HAW doughs during heating were similar to their respective starch pasting profiles, indicating that starch was the dominant contributor to the dough rheology during heating. This study provides useful information for food applications and manufacturing of HAW-based products, especially none-fermented products requiring firm texture and low viscosity.