In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

2021 ◽  
Author(s):  
David Adeniji ◽  
Julius Schoop
Keyword(s):  
Process Improvement ◽  
Titanium Aluminide ◽  
Machine Learning Algorithms ◽  
Embodied Energy ◽  
Empirical Modeling ◽  
Optimization Approach ◽  
Gamma Titanium Aluminide ◽  
Process Characterization ◽  
Novel Approach

Abstract The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated for the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

IN-SITU CALIBRATED DIGITAL PROCESS TWIN MODELS FOR RESOURCE EFFICIENT MANUFACTURING

2021 ◽  
pp. 1-8
Author(s):  
David Adeniji ◽  
Julius Schoop
Keyword(s):  
Process Improvement ◽  
Titanium Aluminide ◽  
Machine Learning Algorithms ◽  
Embodied Energy ◽  
Empirical Modeling ◽  
Optimization Approach ◽  
Gamma Titanium Aluminide ◽  
Process Characterization ◽  
Novel Approach ◽  
Twin Models

Abstract The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated for the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of insitu process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

A novel approach for preparing in-situ nitrogen doped carbon via pyrolysis of bean pulp for supercapacitors

Energy ◽  
2021 ◽  
Vol 216 ◽  
pp. 119227
Author(s):  
Yan Ding ◽  
Yunchao Li ◽  
Yujie Dai ◽  
Xinhong Han ◽  
Bo Xing ◽  
...  
Keyword(s):  
Nitrogen Doped ◽  
Novel Approach ◽  
Nitrogen Doped Carbon

scholarly journals CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sima Ranjbari ◽  
Toktam Khatibi ◽  
Ahmad Vosough Dizaji ◽  
Hesamoddin Sajadi ◽  
Mehdi Totonchi ◽  
...  
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Semen Quality ◽  
Intrauterine Insemination ◽  
Machine Learning Algorithms ◽  
Semen Parameters ◽  
Feature Engineering ◽  
Operating Characteristics ◽  
Scoring Method ◽  
Novel Approach

Abstract Background Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11,255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome. Results Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

scholarly journals In Situ Generation of Green Hybrid Nanofibrillar Polymer-Polymer Composites—A Novel Approach to the Triple Shape Memory Polymer Formation

Polymers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1900
Author(s):  
Ramin Hosseinnezhad ◽  
Iurii Vozniak ◽  
Fahmi Zaïri
Keyword(s):  
Polymer Blends ◽  
Shape Memory ◽  
Shape Memory Polymer ◽  
Cellulose Nanofibers ◽  
Polymeric Materials ◽  
Novel Approach ◽  
Phase Transition Temperatures ◽  
Triple Shape Memory

The paper discusses the possibility of using in situ generated hybrid polymer-polymer nanocomposites as polymeric materials with triple shape memory, which, unlike conventional polymer blends with triple shape memory, are characterized by fully separated phase transition temperatures and strongest bonding between the polymer blends phase interfaces which are critical to the shape fixing and recovery. This was demonstrated using the three-component system polylactide/polybutylene adipateterephthalate/cellulose nanofibers (PLA/PBAT/CNFs). The role of in situ generated PBAT nanofibers and CNFs in the formation of efficient physical crosslinks at PLA-PBAT, PLA-CNF and PBAT-CNF interfaces and the effect of CNFs on the PBAT fibrillation and crystallization processes were elucidated. The in situ generated composites showed drastically higher values of strain recovery ratios, strain fixity ratios, faster recovery rate and better mechanical properties compared to the blend.

scholarly journals Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1955
Author(s):  
Md Jubaer Hossain Pantho ◽  
Pankaj Bhowmik ◽  
Christophe Bobda
Keyword(s):  
Power Consumption ◽  
High Speed ◽  
Image Sensor ◽  
Machine Learning Algorithms ◽  
Hierarchical Optimization ◽  
Computation Method ◽  
Optimization Approach ◽  
Efficient Computation ◽  
Feature Maps ◽  
Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

scholarly journals A Novel Cobalt Metallopolymer with Redox-Matched Conjugated Organic Backbone via Electropolymerization of a Readily Available N4 Cobalt Complex

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1667
Author(s):  
Mikhail Karushev
Keyword(s):  
Redox Reactions ◽  
Repeat Unit ◽  
Cobalt Complex ◽  
Radical Cations ◽  
Electrochemical Quartz Crystal Microbalance ◽  
Practical Applications ◽  
Sensing Applications ◽  
Novel Approach ◽  
Redox Switching

Fast and reversible cobalt-centered redox reactions in metallopolymers are the key to using these materials in energy storage, electrocatalytic, and sensing applications. Metal-centered electrochemical activity can be enhanced via redox matching of the conjugated organic backbone and cobalt centers. In this study, we present a novel approach to redox matching via modification of the cobalt coordination site: a conductive electrochemically active polymer was electro-synthesized from [Co(Amben)] complex (Amben = N,N′-bis(o-aminobenzylidene)ethylenediamine) for the first time. The poly-[Co(Amben)] films were investigated by cyclic voltammetry, electrochemical quartz crystal microbalance (EQCM), in situ UV‑vis-NIR spectroelectrochemistry, and in situ conductance measurements between −0.9 and 1.3 V vs. Ag/Ag+. The polymer displayed multistep redox processes involving reversible transfer of the total of 1.25 electrons per repeat unit. The findings indicate consecutive formation of three redox states during reversible electrochemical oxidation of the polymer film, which were identified as benzidine radical cations, Co(III) ions, and benzidine di-cations. The Co(II)/Co(III) redox switching is retained in the thick polymer films because it occurs at potentials of high polymer conductivity due to the optimum redox matching of the Co(II)/Co(III) redox pair with the organic conjugated backbone. It makes poly-[Co(Amben)] suitable for various practical applications based on cobalt-mediated redox reactions.

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