Cognitive Decision-making Systems for Scraps Control in Aerospace Turbine Blade Casting☆

Abstract The competitiveness of a casting system in modern lost wax production of superalloy turbine blades strongly depends on the reduction of scraps, which commonly affect superalloy cast parts. In order to achieve a focused goal of competitiveness, some key and vital parameters (Key Process Variables) have to be continuously taken under control to make very accurate predictions of Target Variables, which represent, as mapped KPVs domain, the ultimate performance of the entire production link. Such an approach is based on the development of robust control monitoring of the ceramic shell manufacture, which is specifically conceived to foster a possible reduction of scraps in the production if superalloy components. The concerned control will take into consideration data coming from both sensors and measured values in laboratory. The sensor data, which is originated from both new adopted inline and offline equipments at Europea Microfusioni Aerospaziali S.p.A. (EMA) and data measured in the EMA laboratories, will be merged into a sensor pattern vector which represents the basis to develop the EMA demonstrator within the Intelligent Fault Correction and self Optimizing manufacturing systems EU project funded in FP7. The sensor pattern vector will be used to feed an automatic system for the prediction of the process vital parameters. An automated system, based on artificial intelligence paradigms, in particular neural networks, will be fed with the data coming from the sensor pattern vector in order to produce an optimal multi-object output

Prediction of Dressing in Grinding Operation via Neural Networks

Abstract In order to obtain a modelling and prediction of tool wear in grinding operations, a Cognitive System has been employed to observe the dressing need and its trend. This paper aims to find a methodology to characterize the condition of the wheel during grinding operations and, by the use of cognitive paradigms, to understand the need of dressing. The Acoustic Emission signal from the grinding operation has been employed to characterize the wheel condition and, by the feature extraction of such signal, a cognitive system, based on Artificial Neural Networks, has been implemented

Facial Recognition and Face Mask Detection Using Machine Learning Techniques

Facial recognition, as a biometric system, is a crucial tool for the identification procedures. When using facial recognition, an individual\u27s identity is identified using their unique facial features. Biometric authentication system helps in identifying individuals using their physiological and behavioral features. Physiological biometrics utilize human features such as faces, irises, and fingerprints. In contrast, behavioral biometric rely on features that humans do, such as voice and handwritings. Facial recognition has been widely used for security and other law enforcement purposes. However, since COVID-19 pandemic, many people around the world had to wear face masks. This thesis introduces a neural network system, which can be trained to identify people’s facial features while half of their faces are covered by face masks. The Convolutional Neural Network (CNN) model using transfer learning technique has achieved remarkable accuracy even the original dataset is very limited. One large Face mask detection dataset was first used to train the model, while the original much smaller Face mask detector dataset was used to adapt and finetune this model that was previously generated. During the training and testing phases, network structures, and various parameters were adjusted to achieve the best accuracy results for the actual small dataset. Our adapted model was able to achieve a 97.1% accuracy

Intelligent pattern recognition of a SLM machine process and sensor data

Selective Laser Melting is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Therefore, new manufacturing parameters have been monitored raising the number of sensors in the machines. Consequently, it leads to a bigger amount of data and difficulties to perform manual data analysis. In order to improve the analysis, this paper illustrates a possibility of pattern recognition using a different historical process and sensors data from a SLM machine. The results are evaluated using an intelligent tool for algorithms configuration and data analysis developed at Fraunhofer IPK

Tool wear monitoring using an online, automatic and low cost system based on local texture

[EN] In this work we propose a new online, low cost and fast approach based on computer vision and machine learning to determine whether cutting tools used in edge pro le milling processes are serviceable or disposable based on their wear level. We created a new dataset of 254 images of edge pro le cutting heads which is, to the best of our knowledge, the rst publicly available dataset with enough quality for this purpose. All the inserts were segmented and their cutting edges were cropped, obtaining 577 images of cutting edges: 301 functional and 276 disposable. The proposed method is based on (1) dividing the cutting edge image in di erent regions, called Wear Patches (WP), (2) characterising each one as worn or serviceable using texture descriptors based on di erent variants of Local Binary Patterns (LBP) and (3) determine, based on the state of these WP, if the cutting edge (and, therefore, the tool) is serviceable or disposable. We proposed and assessed ve di erent patch division con gurations. The individual WP were classi ed by a Support Vector Machine (SVM) with an intersection kernel. The best patch division con guration and texture descriptor for the WP achieves an accuracy of 90.26% in the detection of the disposable cutting edges. These results show a very promising opportunity for automatic wear monitoring in edge pro le milling processes. Keywords: Tool wear, texture descriptionS

Smart process monitoring of machining operations

The following thesis explores the possibilities to applying artificial intelligence techniques in the field of sensory monitoring in the manufacturing sector. There are several case studies considered in the research activity. The first case studies see the implementation of supervised and unsupervised neural networks to monitoring the condition of a grinding wheel. The monitoring systems have acoustic emission sensors and a piezoelectric sensor capable to measuring electromechanical impedance. The other case study is the use of the bees' algorithm to determine the wear of a tool during the cutting operations of a steel cylinder. A script permits this operation. The script converts the images into a numerical matrix and allows the bees to correctly detect tool wear

Recent advances in the theory and practice of logical analysis of data

Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning methods by the fact that it analyzes a significant subset of combinations of variables to describe the positive or negative nature of an observation and uses combinatorial techniques to extract models defined in terms of patterns. In recent years, the methodology has tremendously advanced through numerous theoretical developments and practical applications. In the present paper, we review the methodology and its recent advances, describe novel applications in engineering, finance, health care, and algorithmic techniques for some stochastic optimization problems, and provide a comparative description of LAD with well-known classification methods

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201