A critical review on applications of artificial intelligence in manufacturing

Mypati, Omkar; Mukherjee, A. K.; Mishra, Debasish; Pal, Surjya K.; Chakrabarti, P. P.; Pal, Arpan

doi:10.1007/s10462-023-10535-y

Cited by 21 publications

(2 citation statements)

References 381 publications

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“…The primary objective of the project is to combine machine learning and programming mathematics to optimize electrically terminated machining (EDM) processes [1]. This study manages the need to grow the effectiveness as well as efficiency regarding the EDM methodology by carefully looking at the relevant variables charming the operation of machining [2]. The aim is to develop a sweeping framework that utilizes mathematical optimization techniques in conjunction with datadriven artificial intelligence algorithms to ascertain the optimal EDM parameter settings [3].…”

Section: A Project Specificationmentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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This study explores the combination of machine guidance and several developing approaches to enhance both precision and effectiveness during Electricity-discharged Machining (EDM) business operations. The studies on quality control, energy efficiency, sustainable development, mathematical modelling within EDM optimization, and machine learning applications in EDM optimisation are all examined in this study. It highlights significant gaps in scientific knowledge, providing a pathway for the development of state-of-the-art EDM methods. The outcomes show that material decrease, energy efficiency, along EDM technique optimisation can all be enhanced. This study offers valuable information for future research within the field and contributes to the ongoing conversation about advanced manufacturing techniques. This project intends to revolutionise EDM by merging mathematical programming and machine learning. Three primary topics are investigated machining parameter optimisation, efficiency improvement using machine learning and environmental effect assessment. The goals of the study are met by using the deductive method, which gives a formal setting in which to examine hypotheses. Descriptive research designs allow for in-depth analyses of previously published works, mathematical models and automated learning programs. Finding commonalities and trends in qualitative data is the goal of the thematic data analysis technique. The results of this study provide useful resources, standards and sustainable perspectives for enhancing EDM procedures in manufacturing settings.

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Section: A Project Specificationmentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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“…Therefore, it is necessary to ensure that the construction machinery is well lubricated and is reliable to ensure its regular operation [4]. In recent years, with the development of artificial intelligence, the internet, and other technologies, the manufacturing industry has been gradually changing in the direction of intelligence [5,6]. Construction machinery is essential to modern manufacturing, and its intelligent development is also attracting attention [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective-Based Intelligent Lubrication System Performance Evaluation Technology for Construction Machinery

Peng,

Chen,

Shangguan

et al. 2023

Applied Sciences

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The infrastructure construction process cannot be separated from construction machinery; it will inevitably produce wear and tear in the work. The level of wear and tear is severe and could cause mechanical accidents. There are safety hazards involved with wear and tear; thus, the study of the lubrication systems of construction machinery is crucial. This paper addresses the problems with the intelligent lubrication systems of construction machinery and establishes a performance evaluation index system for the intelligent lubrication systems of construction machinery by analyzing and selecting appropriate evaluation indexes. Based on the built evaluation system, a performance evaluation model was established based on the hierarchical analysis (analytic hierarchy process, AHP)–entropy weight method and a topological object element model. The feasibility of the model was tested using the example of an off-road mining dump truck. This model analyzes the performance strengths and weaknesses of smart lubrication systems and suggests improvement measures and recommendations for weak links. It also provides a reference for analyzing the performance of smart lubrication systems for other mechanical devices.

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Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control

Casini,

De Angelis,

Porrati

et al. 2024

Energy Efficiency

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With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

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A critical review on applications of artificial intelligence in manufacturing

Cited by 21 publications

References 381 publications

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Multi-Objective-Based Intelligent Lubrication System Performance Evaluation Technology for Construction Machinery

Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control

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