Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Journal of Intelligent Manufacturing - In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber...     

The structure and analysis of nanotechnology co-author and citation networks

Research activities and collaborations in nanoscale science and engineering have major implications for advancing technological frontiers in many fields including medicine, electronics, energy, and communication. The National Nanotechnology Initiative (NNI) promotes efforts to cultivate effective research and collaborations among nano scientists and engineers to accelerate the advancement of nanotechnology and its commercialization. As of August 2008, there have been over 800 products considered to benefit from nanotechnology directly or indirectly. However, today’s accomplishments in nanotechnology cannot be transformed into commercial products without productive collaborations among experts from disparate research areas such as chemistry, physics, math, biology, engineering, manufacturing, environmental sciences, and social sciences. To study the patterns of collaboration, we build and analyze the collaboration network of scientists and engineers who conduct research in nanotechnology. We study the structure of information flow through citation network of papers authored by nano area scientists. We believe that the study of nano area co-author and paper citation networks improve our understanding of patterns and trends of the current research efforts in this field. We construct these networks based on the publication data collected for years ranging 1993 through 2008 from the scientific literature database “Web of Science”. We explore those networks to find out whether they follow power-law degree distributions and/or if they have a signature of hierarchy. We investigate the small-world characteristics and the existence of possible community structures in those networks. We estimate the statistical properties of the networks and interpret their significance with respect to the nano field.     

Machine fault diagnosis using a cluster-based wavelet feature extraction and probabilistic neural networks

In this paper, a cluster-based feature extraction from the coefficients of a discrete wavelet transform and probabilistic neural networks are proposed for machine fault diagnosis. The proposed approach first divides the matrix of wavelet coefficients into clusters, which are centered around the discriminative coefficient positions identified by an unsupervised procedure, based on the entropy value of coefficients from a set of representative signals. The features that contain the informative attributes of the signals are computed from the energy content of the obtained clusters. Then, machine faults are diagnosed based on these feature vectors using a probabilistic neural network. The experimental results from the application on bearing fault diagnosis have shown that the proposed approach is able to effectively extract important intrinsic information content of the test signals and increase the overall fault diagnostic accuracy, as compared to conventional methods.     

Neural networks and their applications in component design data retrieval

Neural networks have gained increased importance in the past few years. One of the basic characteristics of neural networks is the property of associative memory. In this paper we study the possibility of using the ideas of neural networks and associative memory in the manufacturing domain, with specific reference to design data retrieval in group technology. A two-layer feed-forward perceptron with backpropagation is simulated on a Vax-8550 to train example parts. The complete scheme along with the simulation results are explained and future directions indicated.     

Feature extraction from wavelet coefficients for patternrecognition tasks

An efficient feature extraction method based on the fast wavelet transform is presented. The paper especially deals with the assessment of process parameters or states in a given application using the features extracted from the wavelet coefficients of measured process signals. Since the parameter assessment using all wavelet coefficients will often turn out to be tedious or leads to inaccurate results, a preprocessing routine that computes robust features correlated to the process parameters of interest is highly desirable. The method presented divides the matrix of computed wavelet coefficients into clusters equal to row vectors. The rows that represent important frequency ranges (for signal interpretation) have a larger number of clusters than the rows that represent less important frequency ranges. The features of a process signal are eventually calculated by the Euclidean norms of the clusters. The effectiveness of this new method has been verified on a flank wear estimation problem in turning processes and on a problem of recognizing different kinds of lung sounds for diagnosis of pulmonary diseases     

FOURIER AND WAVELET TRANSFORM FOR FLANK WEAR ESTIMATION — A COMPARISON

This article presents potential sensor data representation schemes for force and vibration signals in the context of flank wear estimation in turning processes. In particular, the performances of methods based on fast Fourier transforms (FFTs) and fast wavelet transforms (FWTs) are compared using data from turning experiments. This research, for the first time, studies the performance of these modern sensor data representation schemes for flank wear estimation on a common platform and provides a useful insight into their merits and drawbacks. The flank wear estimates are computed continually from the features extracted through each representation scheme by using a simple recurrent neural network architecture. The results can be used for selecting correct data representation schemes for flank wear estimation.     

Function-to-structure transformation in conceptual design: An associative memory-based paradigm

The three most important phases of design are (1) conceptual phase; (2) configuration phase; and (3) parameterization phase. The second and the third phases are well researched. However, little work has been done in the conceptual design phase. In this paper the author deals with a different way of modeling the conceptual design phase. In this research the paradigm of function-to-structure transformation is used. One of the most difficult ideas of design is that of modeling the function-to-structure transformation process. The current research shows that the function-to-structure transformation is accomplished through a process of association. Whenever a designer is faced with finding a solution to a new functional requirement, the designer tries to associate this new function with known functions from his/her memory through a process of association. After having identified the closest function, an associated structure can be retrieved and mutated to form the design solution for the new problem. In essence, the designer associates the new functions with known functions and will try to retrieve the closest and most general design solution from his/her memory through a process of association. The author models the human associative memory through artificial neural networks (ANN) with back-propagation. A simple yet illustrative example of cups and containers is selected to model the function-to-structure transformation process at the conceptual design phase. In this paper the implementation aspects of the ANN are clearly explained. The robustness of the ANN through different schemes is also explored. A performance analysisvia simulation by varying the nodes of the hidden layer is also carried out.     

Wavelet networks for sensor signal classification in flank wear assessment

It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing flank wear. Based on this fact, this paper investigates a flank wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for flank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to flank wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed flank wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.     

Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning

Due to increasing environmental concerns, manufacturers are forced to take back their products at the end of products’ useful functional life. Manufacturers explore various options including disassembly operations to recover components and subassemblies for reuse, remanufacture, and recycle to extend the life of materials in use and cut down the disposal volume. However, disassembly operations are problematic due to high degree of uncertainty associated with the quality and configuration of product returns. In this research we address the disassembly line balancing problem (DLBP) using a Monte-Carlo based reinforcement learning technique. This reinforcement learning approach is tailored fit to the underlying dynamics of a DLBP. The research results indicate that the reinforcement learning based method is able to perform effectively, even on a complex large scale problem, within a reasonable amount of computational time. The proposed method performed on par or better than the benchmark methods for solving DLBP reported in the literature. Unlike other methods which are usually limited deterministic environments, the reinforcement learning based method is able to operate in deterministic as well as stochastic environments.