Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Optimization of Blast Furnace Parameters using Artificial Neural Network

Inside the blast furnace (BF) the process is very complicated and very tough to model mathematically. Blast furnace is the heart of the steel industry as it produces molten pig iron which is the raw material for steel making. It is very important to minimise the operational cost, reduce fuel consumption, and optimise the overall efficiency of the blast furnace and also improve the productivity of the blast furnace. Therefore a multi input multi output (MIMO) artificial neural network (ANN) model has been developed to predict the parameters namely raceway adiabatic flame temperature (RAFT), shaft temperature and uptake temperature. The input parameters in the ANN model are oxygen enrichment, blast volume, blast pressure, top gas pressure, hot blast temperature (HBT), steam injection rate, stove cooler inlet temperature, & stove cooler outlet temperature. For the optimisation of the predictive output back propagation ANN model has been introduced. In this present work, Artificial Neural Network (ANN) has been used to predict and optimise the output parameters. All the input data were collected from Rourkela steel plant (RSP) of blast number IV during the one month of operation

Deep Learning-Guided Prediction of Material’s Microstructures and Applications to Advanced Manufacturing

Material microstructure prediction based on processing conditions is very useful in advanced manufacturing. Trial-and-error experiments are very time-consuming to exhaust numerous combinations of processing parameters and characterize the resulting microstructures. To accelerate process development and optimization, researchers have explored microstructure prediction methods, including physical-based modeling and feature-based machine learning. Nevertheless, they both have limitations. Physical-based modeling consumes too much computational power. And in feature-based machine learning, low-dimensional microstructural features are manually extracted to represent high-dimensional microstructures, which leads to information loss. In this dissertation, a deep learning-guided microstructure prediction framework is established. It uses a conditional generative adversarial network (CGAN) to regress microstructures against numerical processing parameters. After training, the algorithm grasps the mapping between microstructures and processing parameters and can infer the microstructure according to an unseen processing parameter value. This CGAN-enabled approach consumes low computational power for prediction and does not require manual feature extraction. A regression-based conditional Wasserstein generative adversarial network (RCWGAN) is developed, and its microstructure prediction capability is demonstrated on a synthetic micrograph dataset. Several important hyperparameters, including loss function, model depth, number of training epochs, and size of the training set, are systematically studied and optimized. After optimization, prediction accuracy in various microstructural features is over 92%. Then the RCWGAN is validated on a scanning electron microscopy (SEM) micrograph dataset obtained from laser-sintered alumina. Data augmentation is applied to ensure an adequate number of training samples. Different regularization technologies are studied. It is found that gradient penalty can preserve the most details in the generated microstructure. After training, the RCWGAN is able to predict the microstructure as a function of laser power. In-situ microstructure monitoring using the RCWGAN is proposed and demonstrated. Obtaining microstructure information during fabrication could enable accurate microstructure control. It opens the possibility of fabricating a new kind of materials with novel functionalities. The RCWGAN is integrated into a laser sintering system equipped with a camera to demonstrate this novel application. Surface-emission brightness is captured by the camera during the laser sintering process and fed to the RCWGAN for online microstructure prediction. After training, the RCWGAN learns the mapping between surface-emission brightness and microstructures and can make prediction in seconds. The prediction accuracy is over 95% in terms of average grain size

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Book of abstracts of the 16th International Symposium of Croatian Metallurgical Society - SHMD \u272023, Materials and metallurgy

Book of abstracts of the 16th International Symposium of Croatian Metallurgical Society - SHMD \u272023, Materials and metallurgy, Zagreb, Croatia, April 20-21, 2023. Abstracts are organized into five sections: Anniversaries of Croatian Metallurgy, Materials - Section A; Process Metallurgy - Section B; Plastic Processing - Section C and Metallurgy and Related Topics - Section D

Material and energy flows of the iron and steel industry: status quo, challenges and perspectives

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies

Multi-objective coordinated development paths for China's steel industry chain based on “water-energy-economy” dependence

The water-energy crisis seriously affects the sustainable development of China's steel industry chain. To achieve a coordinated development new path from the perspective of circular economy, it is necessary to analyze “water-energy-economy” dependency relationship of the steel products. This study analyzes a variety of steel products from the perspective of industry chain and simulates the “water-energy-economy” potential changes of products under different scenarios by developing a multi-objective optimization model. In this model, Random Forest (RF) and GEne Network Inference with Ensemble of trees (GEINIE3) algorithms are combined to evaluate the 2013–2019 “water-energy-economy” dependency relationships firstly. Then, improved Quantum Particle Swarm Optimization (QPSO) algorithm is applied to dynamically simulate potential changes of water, energy and economic performance in the steel industry chain under different scenarios, and to design an optimal development path from the perspective of optimizing economic performance within minimum water and minimum energy use constraints. Results firstly point out the current “water-energy-economy” triple dimension dependency relationship of China's steel industry is weak. Secondly, the “water-economy” dependence has changed from one-way dependence to two-way dependence, and the “energy-economy” relationship still shows a one-way dependence. Finally, when improving resource utilization rate, assigning priority to the reuse of scrap steel, while restricting pig iron and primary steel use, may help maximize the coordinated development of “water-energy-economy” in the steel industry chain. Policy implications are proposed based on the results and provided decision-making basis for the country and relevant enterprises to promote sustainable development of the steel industry chain