This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.
In this work, the implementation and test of an integrated assessment model (IAM) to aid governments to define their short term plans (STP) is presented. The methodology is based on a receding horizon approach where the forecasting model gives information about a selected air quality index up to 3 days in advance once the emission of the involved pollutants (control variable) are known. The methodology is fully general with respect to the model used for the forecast and the air quality index; nevertheless, the selection of these models must take into account the peculiarities of the pollutants to be controlled. This system has been tested for particulate matter (PM10) control over a domain located in Northern Italy including the highly polluted area of Brescia. The results show that the control system can be a valuable asset to aid local authorities in the selection of suitable air quality plans.
This paper presents an inverse kinematic solver for a robotic arm based on an artificial neural network, ANN, architecture. The motion of the robotic arm is controlled by the kinematics of the ANN. The novelty of the proposed method is the validation using a proprietary robot of a novel procedure that applies three networks in a sequential mode to predict one joint value at a time. The inclusion of the genetic algorithm in the ANN definition and the adoption of sequential technique significantly reduced the manual settings and increased the obtained accuracy with respect to the traditional network deployment. The simulated outcomes proved the efficacy of the proposed approach in robotic motion control. The final architecture has three hidden layers: {40 (tansig), 35 (tansig), 30 (tansig)}. The resultant MSE in joint space is close to 3.235*10-4 rad2 and 0.1318mm2 in Cartesian space for the testing dataset. The maximum trajectory error for the validation curves, a planar circle and a spatial spring, is lower than 0.27mm for each axis.
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