Comparative analysis of artificial intelligence techniques for material selection applied to manufacturing in Industry 4.0

Merayo, David; Rodríguez-Prieto, A.; Camacho, Ana

doi:10.1016/j.promfg.2019.07.027

Cited by 29 publications

(28 citation statements)

References 24 publications

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“…Thousands of material data with adequate tools to extract trends enable the anticipation of the expected properties of a new material without the need for any type of test and limits errors [6]. In this way, research routes can be eliminated before they consume valuable resources that can be exploited in other more productive studies [7].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems [6]. These processes include learning (acquisition of information and rules for the use of information), reasoning (using the rules to reach approximate or definitive conclusions) and self-correction [15].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, AI is a general term that addresses automation of robotic processes to current robotics. AI has recently gained prominence due to the large volumes of data or to the increase in speed, size and variety of information collected by companies [6]. AI can perform tasks, such as identifying patterns in data more efficiently than humans, which enables users to obtain more information about their data [16].…”

Section: Introductionmentioning

confidence: 99%

“…Technology enables us to produce materials with an extensive variety of properties; these materials can work very differently depending on the environment and working conditions [6]. This finding implies that a large amount of data are available, and therefore, the situation will become unmanageable using only usual calculation methods.…”

Section: Introductionmentioning

confidence: 99%

“…As systems that are capable of learning, neural networks implement the principle of induction, which is learning by experience [6]. By confronting specific situations, neural networks infer an integrated decision system whose generic character is a function of the number of learning cases that are encountered and their complexity in relation to the complexity of the problem to be solved [39], [41].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks

2020

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In this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young's modulus with average confidences greater than 99% and 98%, respectively. INDEX TERMS Artificial intelligence, big data, material selection, multilayer feedforward networks, neural network, property prediction, software-based web browser control. LIST OF SYMBOLS AND ABBREVIATIONS ADAM Adaptive Moment Estimation AI Artificial intelligence ANN Artificial Neural Networks β n ADAM algorithm parameter ADAM stability factor ε Prediction error of a neural network η ADAM step size f Error function g Gradient of the error function HTML HyperText Markup Language m ADAM first moment estimate v ADAM second moment estimate w Weights vector

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks

2020

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A review of artificial intelligence applications in manufacturing operations

Plathottam,

Rzonca,

Lakhnori

et al. 2023

J Adv Manuf & Process

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Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

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Artificial intelligence in product lifecycle management

Wang

Liu

et al. 2021

Int J Adv Manuf Technol

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Comparative analysis of artificial intelligence techniques for material selection applied to manufacturing in Industry 4.0

Cited by 29 publications

References 24 publications

Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks

Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks

A review of artificial intelligence applications in manufacturing operations

Artificial intelligence in product lifecycle management

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