scholarly journals Trends and Opportunities of Industry 4.0 in Wood Manufacturing Processes

2021 ◽  
Author(s):  
Mario Ramos-Maldonado ◽  
Cristhian Aguilera-Carrasco
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Machine Learning Techniques ◽  
Manufacturing Processes ◽  
Manufacturing Plants ◽  
Intelligent Algorithms ◽  
Learning From Data ◽  
Learning Techniques ◽  
Intelligent Analysis ◽  
Data Engineering

Wood industry is key for sustainability and an important economic activity in many countries. In manufacturing plants, wood variability turns operation management more complex. In a competitive scenario, assets availability is critical to achieve higher productivity. In a new fourth industrial revolution, Industry 4.0, data engineering permits efficient decisions making. Phenomena difficult to model with conventional techniques are turned possible with algorithms based on artificial intelligence. Sensors and machine learning techniques allow intelligent analysis of data. However, algorithms are highly sensitive of the problem and his study to decide on which work is critical. For the manufacturing wood processes, Industry 4.0 is a great opportunity. Wood is a material of biological origin and generates variabilities over the manufacturing processes. For example, in the veneer drying, density and anatomical structure impact the product quality. Scanners have been developed to measure variables and outcomes, but decisions are made yet by humans. Today, robust sensors, computing capacity, communications and intelligent algorithms permit to manage wood variability. Real-time actions can be achieved by learning from data. This paper presents trends and opportunities provided by Industry 4.0 components. Sensors, decision support systems and intelligent algorithms use are reviewed. Some applications are presented.

scholarly journals Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5504
Author(s):  
Hyang-A Park ◽  
Gilsung Byeon ◽  
Wanbin Son ◽  
Hyung-Chul Jo ◽  
Jongyul Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Storage System ◽  
Research Field ◽  
Optimization Techniques ◽  
Virtual Space ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Digital Twin ◽  
Learning Techniques

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

Combined Use of Simulation and ΑΙ/Machine Learning Techniques In Designing and Manufacturing Processes and Systems

2000 ◽  
Vol 3 (2-4) ◽  
pp. 111-118 ◽  
Author(s):  
Laszlo Monostori, ◽  
Botond Kadar, ◽  
Zsolt Janos Viharosy, ◽  
lstvan Mezgar, ◽  
Peter Stefan,
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Manufacturing Processes ◽  
Learning Techniques ◽  
Combined Use

Machine learning techniques applied to industrial engineering: a multi criteria approach

2019 ◽  
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Giuseppina Piscitelli ◽  
Raffaele Cioffi ◽  
Antonella Petrillo
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Technological Development ◽  
Predictive Maintenance ◽  
Industrial Engineering ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The World ◽  
The Subject ◽  
To Receive

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

scholarly journals Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

2021 ◽  
Vol 13 (8) ◽  
pp. 4120
Author(s):  
Hail Jung ◽  
Jinsu Jeon ◽  
Dahui Choi ◽  
Jung-Ywn Park
Keyword(s):  
Machine Learning ◽  
Injection Molding ◽  
Industry 4.0 ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sustainable Growth ◽  
Machine Learning Techniques ◽  
Quality Prediction ◽  
Learning Techniques ◽  
Prediction Problems

With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

scholarly journals SSL-VFC4.5: An approach to adapt Very Fast C4.5 classification algorithm to deal with semi-supervised learning

2021 ◽  
Author(s):  
Carlos Eduardo Nass ◽  
Agustín Alejandro Ortíz Díaz ◽  
Fabiano Baldo
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Training Time ◽  
Predicting Performance ◽  
Learning Techniques

The growing popularity of audio and video streaming, industry 4.0 and IoT (Internet of Things) technologies contribute to the fast augment of the generation of various types of data. Therefore, to analyze these data for decision-making, supervised machine learning techniques need to be fast while keeping a suitable predicting performance even in many real-life scenarios where labeled data are expensive and hard to be gotten. To overcome this problem, this work proposes an adaptation to the Very Fast C4.5 (VFC4.5) algorithm implementing on it a semi-supervised impurity metric presented in the literature. The results pointed out that this adaptation can slightly increase the accuracy of the VFC4.5 when the datasets have the presence of a very few amount of labeled instances, but it increases the training time, especially when the number of labeled instances in the datasets increase.

scholarly journals LITERATURE SURVEY ON MACHINE LEARNING BASED TECHNIQUES IN MEDICAL DATAANALYSIS

2019 ◽  
pp. 1-4
Author(s):  
Lavanya Vemulapalli
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Intelligent Algorithms ◽  
The Past ◽  
Disease Prognosis ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Comprehensive Survey

Machine Learning plays a significant role among the areas of Artificial Intelligence (AI). During recent years, Machine Learning (ML) has been attracting many researchers, and it has been successfully applied in many fields such as medical, education, forecasting etc., Right now, the diagnosis of diseases is mostly from expert's decision. Diagnosis is a major task in clinical science as it is crucial in determining if a patient is having the disease or not. This in turn decides the suitable path of treatment for disease diagnosis. Applying machine learning techniques for disease diagnosis using intelligent algorithms has been a hot research area of computer science. This paper throws a light on the comprehensive survey on the machine learning applications in the medical disease prognosis during the past decades

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