Search

Manufacturing has experienced tremendous changes from industry 1.0 to industry 4.0 with the advancement of technology in fast-developing areas such as computing, image processing, automation, machine vision, machine learning along with big data and Internet of things. Machine tools in industry 4.0 shall have the ability to identify materials which they handle so that they can make and implement certain decisions on their own as needed. This paper aims to present a generalized methodology for automated material identification using machine vision and machine learning technologies to contribute to the cognitive abilities of machine tools as wells as material handling devices such as robots deployed in industry 4.0. A dataset of the surfaces of four materials (Aluminium, Copper, Medium density fibre board, and Mild steel) that need to be identified and classified is prepared and processed to extract red, green and blue color components of RGB color model. These color components are used as features while training the machine learning algorithm. Support vector machine is used as a classifier and other classification algorithms such as Decision trees, Random forests, Logistic regression, and k-Nearest Neighbor are also applied to the prepared data set. The capability of the proposed methodology to identify the different group of materials is verified with the images available in an open source database. The methodology presented has been validated by conducting four experiments for checking the classification accuracies of the classifier. Its robustness has also been checked for various camera orientations, illumination levels, and focal length of the lens. The results presented show that the proposed scheme can be implemented in an existing manufacturing setup without major modifications. [ABSTRACT FROM AUTHOR]

We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network-oblivious andmakes a decision onlywhen a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network-aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network-oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and the decision delay that is, a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network-oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network-aware case, the optimal stopping problem is analyzed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient decision statistic is the network state and the posterior probability of change having occurred, given the measurements received and the state of the network. The optimal regimes are studied using simulation. [ABSTRACT FROM AUTHOR]