Pub Date : 2023-01-01DOI: 10.1142/s2737599423400042
Rajeendra L. Pemathilaka, Nicole N. Hashemi
Striving for sustainable drug discovery, we have presented a proof-of-concept for studying the effects of pharmaceutical agents transported across the placental barrier on neural cells. The potential effects of pharmaceutical agents on fetus have made concerns about their use and require more studies to address these concerns. A placenta-on-a-chip model was fabricated and tested for transport of naltrexone (NTX) and its primary metabolite 6[Formula: see text]-naltrexol. The NTX/6[Formula: see text]-naltrexol transported from the maternal channel to the fetal channel was then collected from the fetal channel. To evaluate the behavior of neural cells following exposure to NTX and 6[Formula: see text]-naltrexol, perfusate from the fetal channel was directed toward the cultured N27 neural cells. Neural cells exposed to the transported NTX/6[Formula: see text]-naltrexol were then evaluated for gene expression and cell viability. Results showed significantly higher fold changes in IL-6 and TNF-[Formula: see text] expression when exposed to NTX/6[Formula: see text]-naltrexol. However, a lower fold change in IL-1[Formula: see text] expression was observed, while it remained the same in sphingosine kinase (sphk)1. Also, cell viability with NTX/6[Formula: see text]-naltrexol exposure was determined to be significantly lower ([Formula: see text]). This study has the potential to reveal the impact of pharmaceutical agents on the developing neural system of fetuses and their premature brains.
{"title":"Placenta-on-a-chip: Response of neural cells to pharmaceutical agents transported across the placental barrier","authors":"Rajeendra L. Pemathilaka, Nicole N. Hashemi","doi":"10.1142/s2737599423400042","DOIUrl":"https://doi.org/10.1142/s2737599423400042","url":null,"abstract":"Striving for sustainable drug discovery, we have presented a proof-of-concept for studying the effects of pharmaceutical agents transported across the placental barrier on neural cells. The potential effects of pharmaceutical agents on fetus have made concerns about their use and require more studies to address these concerns. A placenta-on-a-chip model was fabricated and tested for transport of naltrexone (NTX) and its primary metabolite 6[Formula: see text]-naltrexol. The NTX/6[Formula: see text]-naltrexol transported from the maternal channel to the fetal channel was then collected from the fetal channel. To evaluate the behavior of neural cells following exposure to NTX and 6[Formula: see text]-naltrexol, perfusate from the fetal channel was directed toward the cultured N27 neural cells. Neural cells exposed to the transported NTX/6[Formula: see text]-naltrexol were then evaluated for gene expression and cell viability. Results showed significantly higher fold changes in IL-6 and TNF-[Formula: see text] expression when exposed to NTX/6[Formula: see text]-naltrexol. However, a lower fold change in IL-1[Formula: see text] expression was observed, while it remained the same in sphingosine kinase (sphk)1. Also, cell viability with NTX/6[Formula: see text]-naltrexol exposure was determined to be significantly lower ([Formula: see text]). This study has the potential to reveal the impact of pharmaceutical agents on the developing neural system of fetuses and their premature brains.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"179 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77062645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423500020
Subhajit Kar, Madhabi Ganguly, Supratik Sen
The paper proposes a lifting scheme-based wavelet transform clustering method as a better alternative to traditional alignment-based virus genome classification and grouping techniques. The efficiency of the proposed alignment-free algorithm have been tested using Coronavirus datasets obtained from NCBI database, against established results from proven techniques. In the proposed approach, the nucleotide sequences are converted into numerical ones leveraging purine–pyrimidine mapping and a DNA walk is calculated to visually interpret them. Second-generation wavelet transform employing Cohen–Daubechies–Feauveau wavelet is applied to the numerical sequences of Coronavirus to determine the approximate coefficients. Approximate coefficients are used to cluster Coronavirus sequences using UPGMA phylogenetic tree for three different datasets of Coronaviruses comprising Coronavirus groups, Human Coronaviruses (HCoVs) and [Formula: see text]–[Formula: see text]–[Formula: see text]–[Formula: see text] Coronavirus genre. The proposed algorithm has successfully classified all the datasets with more than 97% of average accuracy compared in terms of complexity and accuracy against FFT, first-generation DWT, MEGA, and CLUSTAL-W. The obtained accuracy for Corona group is 100%, HCoV dataset is 100%, and for [Formula: see text]–[Formula: see text]–[Formula: see text]–[Formula: see text] CoV is 92%. The runtimes of the algorithm are 0.70, 1.22, and 0.63 sec for the respective Coronavirus datasets.
{"title":"Lifting scheme-based wavelet transform method for improved genomic classification and sequence analysis of Coronavirus","authors":"Subhajit Kar, Madhabi Ganguly, Supratik Sen","doi":"10.1142/s2737599423500020","DOIUrl":"https://doi.org/10.1142/s2737599423500020","url":null,"abstract":"The paper proposes a lifting scheme-based wavelet transform clustering method as a better alternative to traditional alignment-based virus genome classification and grouping techniques. The efficiency of the proposed alignment-free algorithm have been tested using Coronavirus datasets obtained from NCBI database, against established results from proven techniques. In the proposed approach, the nucleotide sequences are converted into numerical ones leveraging purine–pyrimidine mapping and a DNA walk is calculated to visually interpret them. Second-generation wavelet transform employing Cohen–Daubechies–Feauveau wavelet is applied to the numerical sequences of Coronavirus to determine the approximate coefficients. Approximate coefficients are used to cluster Coronavirus sequences using UPGMA phylogenetic tree for three different datasets of Coronaviruses comprising Coronavirus groups, Human Coronaviruses (HCoVs) and [Formula: see text]–[Formula: see text]–[Formula: see text]–[Formula: see text] Coronavirus genre. The proposed algorithm has successfully classified all the datasets with more than 97% of average accuracy compared in terms of complexity and accuracy against FFT, first-generation DWT, MEGA, and CLUSTAL-W. The obtained accuracy for Corona group is 100%, HCoV dataset is 100%, and for [Formula: see text]–[Formula: see text]–[Formula: see text]–[Formula: see text] CoV is 92%. The runtimes of the algorithm are 0.70, 1.22, and 0.63 sec for the respective Coronavirus datasets.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89821881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423400066
Manoj Kumar, Ankit D. Oza, Kiran S. Bhole, Manoj Kumar, Manish Gupta, Sumit Das Lala
This study determined the optimum HSS cutting tool technique parameters for milling W-Al-Si-C rods using Taguchi methodology. This paper explains the empirical results of the selection of appropriate cutting settings that assure lower power consumption in high-end Computer Numerical Control (CNC) machines. An experiment employing the Taguchi methodology on an extruded W-Al- Si-C rod was performed on a CNC lathe with cutting speed, feed rate, and depth of cut as the process parameters. The performance characteristics (energy usage) were quantified by a data collection system. Minor energy process parameters were selected after data analysis. Experimental results are presented to demonstrate the worth of the chosen methodology. A total of 350[Formula: see text]rpm, 0.37[Formula: see text]mm/rev feed rate, and 1[Formula: see text]mm of cut depth produced the best MRR result. The maximum material removal rate (MRR) is obtained at lower levels of spindle speed and depth of cut, i.e., 1.452[Formula: see text]g/sec.
{"title":"Optimization and analysis of machining performance for the milling process during milling of W-Al-Si-C alloy material","authors":"Manoj Kumar, Ankit D. Oza, Kiran S. Bhole, Manoj Kumar, Manish Gupta, Sumit Das Lala","doi":"10.1142/s2737599423400066","DOIUrl":"https://doi.org/10.1142/s2737599423400066","url":null,"abstract":"This study determined the optimum HSS cutting tool technique parameters for milling W-Al-Si-C rods using Taguchi methodology. This paper explains the empirical results of the selection of appropriate cutting settings that assure lower power consumption in high-end Computer Numerical Control (CNC) machines. An experiment employing the Taguchi methodology on an extruded W-Al- Si-C rod was performed on a CNC lathe with cutting speed, feed rate, and depth of cut as the process parameters. The performance characteristics (energy usage) were quantified by a data collection system. Minor energy process parameters were selected after data analysis. Experimental results are presented to demonstrate the worth of the chosen methodology. A total of 350[Formula: see text]rpm, 0.37[Formula: see text]mm/rev feed rate, and 1[Formula: see text]mm of cut depth produced the best MRR result. The maximum material removal rate (MRR) is obtained at lower levels of spindle speed and depth of cut, i.e., 1.452[Formula: see text]g/sec.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135560616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423400017
Soni Kumari, Rakesh Gupta, Gopal Krishna, K. Abhishek, Naveenkrishna Alla, K. K. Saxena
Over the past few decades, manufacturing and production have undergone rapid development, particularly through the combination of additive manufacturing (AM) and other digitally driven manufacturing machines, creating hybrid additive manufacturing (hybrid-AM). However, despite significant growth, hybrid-AM has not yet gained acceptance at an industrial level due to certain limitations. This article aims to provide the latest information and discuss recent research trends, opportunities, challenges, and indicators in the field of hybrid-AM. Specifically, it will review and analyze literature related to the development of hybrid additives and subtractive processes known as HASPs, and identify future research avenues. Additionally, the article will identify key traits and research work in HASP systems, as well as present the future of HASPs and other types of hybrid machine tools based on recent trends.
{"title":"Recent trends and research opportunities in hybrid additive manufacturing","authors":"Soni Kumari, Rakesh Gupta, Gopal Krishna, K. Abhishek, Naveenkrishna Alla, K. K. Saxena","doi":"10.1142/s2737599423400017","DOIUrl":"https://doi.org/10.1142/s2737599423400017","url":null,"abstract":"Over the past few decades, manufacturing and production have undergone rapid development, particularly through the combination of additive manufacturing (AM) and other digitally driven manufacturing machines, creating hybrid additive manufacturing (hybrid-AM). However, despite significant growth, hybrid-AM has not yet gained acceptance at an industrial level due to certain limitations. This article aims to provide the latest information and discuss recent research trends, opportunities, challenges, and indicators in the field of hybrid-AM. Specifically, it will review and analyze literature related to the development of hybrid additives and subtractive processes known as HASPs, and identify future research avenues. Additionally, the article will identify key traits and research work in HASP systems, as well as present the future of HASPs and other types of hybrid machine tools based on recent trends.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89444782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423400030
Akash D. Pandya, Ajay M. Patel, B. Hindocha, M. Kumar, Ankit D. Oza, K. Bhole, M. Kumar, Manish Gupta
In modern manufacturing industries, automated machining systems have become a necessity. However, optimizing resource utilization and achieving a good surface finish remain challenging tasks. Excessive tool usage and poor surface finish are common problems encountered in turning centers, which affect productivity and product quality. In this research, we propose an approach that leverages automation and machine learning techniques to maximize tool use and improve surface finish. Our objective is to investigate the relationship between tool life and surface roughness and to develop a method that can optimize cutting parameters for turning centers. We have conducted an experimental study to evaluate the proposed approach, which involves the automatic determination of cutting parameters based on machine learning algorithms, and concluded a cutting speed of 43.10[Formula: see text]m/min, the surface finish achieved for aluminum material was 1.98[Formula: see text][Formula: see text]m. In the case of mild steel material, the surface finish was 12[Formula: see text][Formula: see text]m at a cutting speed of 25.13[Formula: see text]m/min. Similarly, for cast iron material, the surface finish was 8.45[Formula: see text][Formula: see text]m at a cutting speed of 30.16[Formula: see text]m/min. Our results show that the proposed method outperforms the traditional manual method in terms of surface finish, tool usage, and machining time. Our approach can be applied to other machining systems, providing a practical and effective solution to improve the efficiency and quality of machining processes. This paper presents an experiment that explores the relationship between tool life and surface roughness. Furthermore, an automated approach is proposed for eliminating G code in machining, which can improve the efficiency of machine tools and result in a better surface finish. Objective: To maximize tool use and improve surface finish in turning centers by incorporating automation and machine learning. Idea: This research aims to explore the use of automation and machine learning in turning centers to optimize the cutting parameters and achieve a better surface finish. Description of the idea: The study was conducted by performing experiments on three different materials, i.e., aluminum, mild steel, and cast iron. The cutting parameters, including spindle speed, feed, and depth of cut, were controlled by a programmable logic controller (PLC) integrated with a tachometer and Vernier scale. The surface finish was measured using a surface roughness tester, and the data was analyzed using a supervised machine learning algorithm.
{"title":"Using automation and machine learning to maximize tool use in turning centers for better surface finish","authors":"Akash D. Pandya, Ajay M. Patel, B. Hindocha, M. Kumar, Ankit D. Oza, K. Bhole, M. Kumar, Manish Gupta","doi":"10.1142/s2737599423400030","DOIUrl":"https://doi.org/10.1142/s2737599423400030","url":null,"abstract":"In modern manufacturing industries, automated machining systems have become a necessity. However, optimizing resource utilization and achieving a good surface finish remain challenging tasks. Excessive tool usage and poor surface finish are common problems encountered in turning centers, which affect productivity and product quality. In this research, we propose an approach that leverages automation and machine learning techniques to maximize tool use and improve surface finish. Our objective is to investigate the relationship between tool life and surface roughness and to develop a method that can optimize cutting parameters for turning centers. We have conducted an experimental study to evaluate the proposed approach, which involves the automatic determination of cutting parameters based on machine learning algorithms, and concluded a cutting speed of 43.10[Formula: see text]m/min, the surface finish achieved for aluminum material was 1.98[Formula: see text][Formula: see text]m. In the case of mild steel material, the surface finish was 12[Formula: see text][Formula: see text]m at a cutting speed of 25.13[Formula: see text]m/min. Similarly, for cast iron material, the surface finish was 8.45[Formula: see text][Formula: see text]m at a cutting speed of 30.16[Formula: see text]m/min. Our results show that the proposed method outperforms the traditional manual method in terms of surface finish, tool usage, and machining time. Our approach can be applied to other machining systems, providing a practical and effective solution to improve the efficiency and quality of machining processes. This paper presents an experiment that explores the relationship between tool life and surface roughness. Furthermore, an automated approach is proposed for eliminating G code in machining, which can improve the efficiency of machine tools and result in a better surface finish. Objective: To maximize tool use and improve surface finish in turning centers by incorporating automation and machine learning. Idea: This research aims to explore the use of automation and machine learning in turning centers to optimize the cutting parameters and achieve a better surface finish. Description of the idea: The study was conducted by performing experiments on three different materials, i.e., aluminum, mild steel, and cast iron. The cutting parameters, including spindle speed, feed, and depth of cut, were controlled by a programmable logic controller (PLC) integrated with a tachometer and Vernier scale. The surface finish was measured using a surface roughness tester, and the data was analyzed using a supervised machine learning algorithm.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75638492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423500044
P. Arockia Michael Mercy, K. S. Joseph Wilson
Recent advancements in medical technology impose a limited number of devices for biomedical applications. A variety of techniques are being proposed to improve the performance of novel antenna designs in response to the rapid development of modern wireless technologies. A miniaturised microstrip antenna structure based on metamaterial (MTM) is presented here. The objective of this work is to present a high-directive antenna for wireless systems utilising MTM properties. Directivity is improved by the incorporation of the MTM structure on the ground structure. In order to improve the performance parameters of the antenna for medical applications, this study provides the design and analysis of a multiband patch antenna employing split-ring MTM. The split-ring resonator (SRR) MTM structures are embedded in a unique and novel way in the ground structure of the antenna. So that subwavelength modes get introduced in the patch cavity and a good performance characteristics is obtained. The reference antenna is a rectangular microstrip patch antenna exhibiting a directivity of 1.1823[Formula: see text]dB that resonates at a frequency of 2.32[Formula: see text]GHz. The optimised SRR MTM is positioned in the ground plane of the suggested antenna to increase the directivity of the antenna. This technology covers the frequency range between 2.24 and 3.96[Formula: see text]GHz used for biomedical applications and the ultra-wideband (UWB) range from 4.48 to 9.08[Formula: see text]GHz used for medical applications, industrial and scientific areas. The number of gaps of the rectangular-shaped SRRs is a key component of the enhancement of directivity from 1.1823 to 8.88823[Formula: see text]dB.
{"title":"High-directive multiband microstrip patch antenna for biomedical applications, inspired by metamaterial","authors":"P. Arockia Michael Mercy, K. S. Joseph Wilson","doi":"10.1142/s2737599423500044","DOIUrl":"https://doi.org/10.1142/s2737599423500044","url":null,"abstract":"Recent advancements in medical technology impose a limited number of devices for biomedical applications. A variety of techniques are being proposed to improve the performance of novel antenna designs in response to the rapid development of modern wireless technologies. A miniaturised microstrip antenna structure based on metamaterial (MTM) is presented here. The objective of this work is to present a high-directive antenna for wireless systems utilising MTM properties. Directivity is improved by the incorporation of the MTM structure on the ground structure. In order to improve the performance parameters of the antenna for medical applications, this study provides the design and analysis of a multiband patch antenna employing split-ring MTM. The split-ring resonator (SRR) MTM structures are embedded in a unique and novel way in the ground structure of the antenna. So that subwavelength modes get introduced in the patch cavity and a good performance characteristics is obtained. The reference antenna is a rectangular microstrip patch antenna exhibiting a directivity of 1.1823[Formula: see text]dB that resonates at a frequency of 2.32[Formula: see text]GHz. The optimised SRR MTM is positioned in the ground plane of the suggested antenna to increase the directivity of the antenna. This technology covers the frequency range between 2.24 and 3.96[Formula: see text]GHz used for biomedical applications and the ultra-wideband (UWB) range from 4.48 to 9.08[Formula: see text]GHz used for medical applications, industrial and scientific areas. The number of gaps of the rectangular-shaped SRRs is a key component of the enhancement of directivity from 1.1823 to 8.88823[Formula: see text]dB.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135319310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423300039
Thamires Andrade Lima, Anh Fridman, Jaclyn McLaughlin, Clayton Francis, Anthony Clay, Ganesh Narayanan, Heedong Yoon, Mohanad Idrees, Giuseppe R. Palmese, John La Scala, Nicolas Javier Alvarez
Additive manufacturing (AM) has come a long way since its initial inception. Previously considered a fast prototyping method, it offers significant benefits for use as a method of producing user-end parts that are limited in quantity, customizable, and/or complicated geometries. For AM to be considered in high-performance applications, such as automotive and aerospace, we must consider AM technology and the available and compatible printing materials. Typically only thermoset plastic resins are capable of meeting high-performance specifications, such as sufficiently high strength, stiffness, and toughness, as well as excellent chemical and environmental resistance. This review presents a broad overview of the available high-performance thermoset chemistries and formulations, i.e., resin blends. The base resin chemistries that are covered are: vinyl, epoxy, imides, cyanate ester, urethanes, benzoxazine, and click chemistries (e.g., Michael addition). Subsequently, more application-relevant blends of these base resins are discussed. Each section focuses on resin details such as reaction mechanisms, typical monomer structure, mechanical properties, and applications specific to AM. The review is organized as follows. We begin with an introduction on the state-of-the-art, the challenges still faced by the field, and a benchmark definition of “high performance.” This is followed by a discussion of the available AM technologies for thermoset printing, with a focus on their advantages and disadvantages. Next, we cover the details of different resin chemistry, followed by their blends. The following section details the difficulties in developing AM technologies that allow for the incorporation of fillers, such as rheological modifiers and reinforcements. The review ends with a perspective on the future of AM technologies that would bridge the gap between pure resin printing and the much needed composite printing for high-performance applications.
{"title":"High-performance thermosets for additive manufacturing","authors":"Thamires Andrade Lima, Anh Fridman, Jaclyn McLaughlin, Clayton Francis, Anthony Clay, Ganesh Narayanan, Heedong Yoon, Mohanad Idrees, Giuseppe R. Palmese, John La Scala, Nicolas Javier Alvarez","doi":"10.1142/s2737599423300039","DOIUrl":"https://doi.org/10.1142/s2737599423300039","url":null,"abstract":"Additive manufacturing (AM) has come a long way since its initial inception. Previously considered a fast prototyping method, it offers significant benefits for use as a method of producing user-end parts that are limited in quantity, customizable, and/or complicated geometries. For AM to be considered in high-performance applications, such as automotive and aerospace, we must consider AM technology and the available and compatible printing materials. Typically only thermoset plastic resins are capable of meeting high-performance specifications, such as sufficiently high strength, stiffness, and toughness, as well as excellent chemical and environmental resistance. This review presents a broad overview of the available high-performance thermoset chemistries and formulations, i.e., resin blends. The base resin chemistries that are covered are: vinyl, epoxy, imides, cyanate ester, urethanes, benzoxazine, and click chemistries (e.g., Michael addition). Subsequently, more application-relevant blends of these base resins are discussed. Each section focuses on resin details such as reaction mechanisms, typical monomer structure, mechanical properties, and applications specific to AM. The review is organized as follows. We begin with an introduction on the state-of-the-art, the challenges still faced by the field, and a benchmark definition of “high performance.” This is followed by a discussion of the available AM technologies for thermoset printing, with a focus on their advantages and disadvantages. Next, we cover the details of different resin chemistry, followed by their blends. The following section details the difficulties in developing AM technologies that allow for the incorporation of fillers, such as rheological modifiers and reinforcements. The review ends with a perspective on the future of AM technologies that would bridge the gap between pure resin printing and the much needed composite printing for high-performance applications.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423300027
F. Aslani, Yifan Zhang, A. Valizadeh, Lendyn Philip
This study proposes the design of modular structural geopolymer concrete elements incorporating decommissioned flexible flowlines. To evaluate and assess the feasibility of the proposed modular structural elements, this study aims to investigate its feasibility from the perspectives of sustainability including cost analysis, circular economy (CE) analysis and CO2 emission estimate. Moreover, a series of numerical analyses using finite element modelling (FEM) is conducted to provide insight into the mechanical behaviour of such modular columns and beams. Apart from the cost-saving, CE and social impact benefits of the proposed elements, the results indicate that modular structural elements incorporating flowline have shown very high axial, shear and flexural capacities, which make them suitable to be used in high-rising buildings, bridges, etc. The proposed elements can be a solution to decommission and reuse the flexible flowline on a large scale in construction.
{"title":"Modular structural elements incorporating decommissioned flexible flowlines and geopolymer concrete","authors":"F. Aslani, Yifan Zhang, A. Valizadeh, Lendyn Philip","doi":"10.1142/s2737599423300027","DOIUrl":"https://doi.org/10.1142/s2737599423300027","url":null,"abstract":"This study proposes the design of modular structural geopolymer concrete elements incorporating decommissioned flexible flowlines. To evaluate and assess the feasibility of the proposed modular structural elements, this study aims to investigate its feasibility from the perspectives of sustainability including cost analysis, circular economy (CE) analysis and CO2 emission estimate. Moreover, a series of numerical analyses using finite element modelling (FEM) is conducted to provide insight into the mechanical behaviour of such modular columns and beams. Apart from the cost-saving, CE and social impact benefits of the proposed elements, the results indicate that modular structural elements incorporating flowline have shown very high axial, shear and flexural capacities, which make them suitable to be used in high-rising buildings, bridges, etc. The proposed elements can be a solution to decommission and reuse the flexible flowline on a large scale in construction.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88478065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423400029
Soni Kumari, K. Abhishek, Din Bandhu, Pardeep, B. Sunil, Manish Gupta
In the realm of manufacturing, the use of hybrid manufacturing has led to high-speed production by combining additive manufacturing (AM) with other digitally driven manufacturing machines. Despite its rapid growth over the past decade, the acceptance of hybrid AM within the industry has been limited due to various constraints. To achieve industrial acceptance, it is necessary to address the challenges and limitations of AM. As an effort to mitigate environmental concerns, manufacturers have recently started to explore integrating additional and secondary production methods into their manufacturing processes. Integrated production solutions have shown promise in overcoming present-day barriers in production systems by utilizing the best available integration technology. In this context, this article focuses on three critical points in the manufacturing sector. First, recent developments in the integration of AM processes have been significant. Second, integrated technical planning has been improved, which is essential for the successful implementation of hybrid manufacturing. Finally, there is a growing need to understand the mixed supplement production industry that combines both traditional and AM techniques. Thus, it is essential to emphasize advances in AM processes, integrated technical planning, and mixed supplement production industry to meet the demands of a rapidly evolving manufacturing sector.
{"title":"Industrial and market opportunities in hybrid additive manufacturing","authors":"Soni Kumari, K. Abhishek, Din Bandhu, Pardeep, B. Sunil, Manish Gupta","doi":"10.1142/s2737599423400029","DOIUrl":"https://doi.org/10.1142/s2737599423400029","url":null,"abstract":"In the realm of manufacturing, the use of hybrid manufacturing has led to high-speed production by combining additive manufacturing (AM) with other digitally driven manufacturing machines. Despite its rapid growth over the past decade, the acceptance of hybrid AM within the industry has been limited due to various constraints. To achieve industrial acceptance, it is necessary to address the challenges and limitations of AM. As an effort to mitigate environmental concerns, manufacturers have recently started to explore integrating additional and secondary production methods into their manufacturing processes. Integrated production solutions have shown promise in overcoming present-day barriers in production systems by utilizing the best available integration technology. In this context, this article focuses on three critical points in the manufacturing sector. First, recent developments in the integration of AM processes have been significant. Second, integrated technical planning has been improved, which is essential for the successful implementation of hybrid manufacturing. Finally, there is a growing need to understand the mixed supplement production industry that combines both traditional and AM techniques. Thus, it is essential to emphasize advances in AM processes, integrated technical planning, and mixed supplement production industry to meet the demands of a rapidly evolving manufacturing sector.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"633 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76812120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1142/s2737599423500032
Priyank Gupta, N. Gupta, K. Saxena
The anaconda software required python code in order to run the utilized individual K-nearest neighbor (KNN), random forest regression (RFR), and linear regression (LR) models. The results show that RFR machine learning (ML) technique out of the other utilized models shows the best performance for a used dataset. The findings of this article indicate that the dataset utilized proposed model provides an acceptable algorithm for FACC design and optimization. In the current study of preparation of geopolymer concrete (GPC), relevant variables such as curing, fly ash, calcined clay, added water, super plasticizer, coarse aggregate, quarry stone dust, caustic soda, and water glass were used as input parameters. The ranges, mode, median, standard deviation, and other identifying details were checked using descriptive statistical analysis for the input parameters. The strength due to the compression of FACC GPC was predicted using RFR, LR, and KNN ML techniques, all based on Python coding. The ensemble ML technique, RFR outperformed the individual ML technique, KNN, in terms of prediction. The RFR indicates that the maximum amount of [Formula: see text] is 0.92, and LR provides 0.58, although the KNN was less accurate, with a coefficient of determination of 0.56. The RFR technique’s lower values of errors, mean absolute error (MAE), MSE, and root mean square error (RMSE) yield 1.99, 7.17, and 2.67[Formula: see text]MPa, respectively. The excellent accuracy of the RFR methodology is confirmed by a statistical analysis of errors. Curing temperature, curing hours, molarity of NaOH, and FACC ratio significantly affect the compressive strength (CS) of FACC GPC. The findings indicate that the proposed model provides an acceptable algorithm for FACC design and optimization using RFR among the three combinations of ML methods for a given dataset.
{"title":"Predicting compressive strength of geopolymer concrete using machine learning","authors":"Priyank Gupta, N. Gupta, K. Saxena","doi":"10.1142/s2737599423500032","DOIUrl":"https://doi.org/10.1142/s2737599423500032","url":null,"abstract":"The anaconda software required python code in order to run the utilized individual K-nearest neighbor (KNN), random forest regression (RFR), and linear regression (LR) models. The results show that RFR machine learning (ML) technique out of the other utilized models shows the best performance for a used dataset. The findings of this article indicate that the dataset utilized proposed model provides an acceptable algorithm for FACC design and optimization. In the current study of preparation of geopolymer concrete (GPC), relevant variables such as curing, fly ash, calcined clay, added water, super plasticizer, coarse aggregate, quarry stone dust, caustic soda, and water glass were used as input parameters. The ranges, mode, median, standard deviation, and other identifying details were checked using descriptive statistical analysis for the input parameters. The strength due to the compression of FACC GPC was predicted using RFR, LR, and KNN ML techniques, all based on Python coding. The ensemble ML technique, RFR outperformed the individual ML technique, KNN, in terms of prediction. The RFR indicates that the maximum amount of [Formula: see text] is 0.92, and LR provides 0.58, although the KNN was less accurate, with a coefficient of determination of 0.56. The RFR technique’s lower values of errors, mean absolute error (MAE), MSE, and root mean square error (RMSE) yield 1.99, 7.17, and 2.67[Formula: see text]MPa, respectively. The excellent accuracy of the RFR methodology is confirmed by a statistical analysis of errors. Curing temperature, curing hours, molarity of NaOH, and FACC ratio significantly affect the compressive strength (CS) of FACC GPC. The findings indicate that the proposed model provides an acceptable algorithm for FACC design and optimization using RFR among the three combinations of ML methods for a given dataset.","PeriodicalId":29682,"journal":{"name":"Innovation and Emerging Technologies","volume":"154 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86276045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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