3,453 research outputs found

    Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

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    The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

    A neural network approach for chatter prediction in turning

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    [EN] Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit self-excited vibration. In this paper, an artificial neural network (ANN) is applied to model turning stability. The analytical stability limit is used to generate a data set that trains the ANN. It is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.The authors gratefully acknowledge financial support from the UNC ROI program. Elena Perez-Bernabeu and Miguel Selles also acknowledge support from Universitat Politenica de Valencia (PAID-00-17). Additionally, some of the neural net figures and the 10-fold cross validation figures are based on the TikZ codes provided on StackExchange-TeX by various users. Harish Cherukuri would like to thank them for their valuable advice.Cherukuri, H.; Pérez Bernabeu, E.; Sellés, M.; Schmitz, TL. (2019). A neural network approach for chatter prediction in turning. Procedia Manufacturing. 34:885-892. https://doi.org/10.1016/j.promfg.2019.06.1598858923

    Application of Audible Signals in Tool Condition Monitoring using Machine Learning Techniques

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    Machining is always accompanied by many difficulties like tool wear, tool breakage, improper machining conditions, non-uniform workpiece properties and some other irregularities, which are some of major barriers to highly-automated operations. Effective tool condition monitoring (TCM) system provides a best solution to monitor those irregular machining processes and suggest operators to take appropriate actions. Even though a wide variety of monitoring techniques have been developed for the online detection of tool condition, it remains an unsolved problem to look for a reliable, simple and cheap solution. This research work mainly focuses on developing a real-time tool condition monitoring model to detect the tool condition, part quality in machining process by using machine learning techniques through sound monitoring. The present study shows the development of a process model capable of on-line process monitoring utilizing machine learning techniques to analyze the sound signals collected during machining and train the proposed system to predict the cutting phenomenon during machining. A decision-making system based on the machine learning technique involving Support Vector Machine approach is developed. The developed system is trained with pre-processed data and tested, and the system showed a significant prediction accuracy in different applications which proves to be an effective model in applying to machining process as an on-line process monitoring system. In addition, this system also proves to be effective, cheap, compact and sensory position invariant. The successful development of the proposed TCM system can provide a practical tool to reduce downtime for tool changes and minimize the amount of scrap in metal cutting industry

    AI and OR in management of operations: history and trends

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    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

    Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution

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    The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds

    ANN Modelling to Optimize Manufacturing Process

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    Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted

    A machine-learning based solution for chatter prediction in heavy-dutymilling machines

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    The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine learning techniques with no need to calculate the two above-mentioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on AISI 1045 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and Random Forest. The results show that Random Forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10 × 10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, Random Forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.Hidrodamp Project (IDI-20110453) of the Centre for Industrial Technological Development (CDTI

    Monitoring of hybrid manufacturing using acoustic emission sensor

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    The approach of hybrid manufacturing addressed in this research uses two manufacturing processes, one process builds a metal part using laser metal deposition, and the other process finishes the part using a milling machining. The ability to produce complete functioning parts in a short time with minimal cost and energy consumption has made hybrid manufacturing popular in many industries for parts repair and rapid prototyping. Monitoring of hybrid manufacturing processes has become popular because it increases the quality and accuracy of the parts produced and reduces both costs and production time. The goal of this work is to monitor the entire hybrid manufacturing process. During the laser metal deposition, the acoustic emission sensor will monitor the defect formation. The acoustic emission sensor will monitor the depth of cut during milling machining. There are three tasks in this study. The first task addresses depth-of-cut detection and tool-workpiece engagement using an acoustic emission monitoring system during milling machining for a deposited material. The second task, defects monitoring system was proposed to detect and classify defects in real time using an acoustic emission (AE) sensor and an unsupervised pattern recognition analysis (K-means clustering) in conjunction with a principal component analysis (PCA). In the third task, a study was conducted to investigate the ability of AE to detect and identify defects during laser metal deposition using a Logistic Regression Model (LR) and an Artificial Neural Network (ANN) --Abstract, page iv
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