COMPARATIVE STUDY OF ANN AND ANFIS MODELS FOR PREDICTING TEMPERATURE IN MACHINING

The Mechanism of heat generation at the cutting region (tool-workpiece interface) during machining processes is a highly complicated phenomenon and depends on many process parameters. Elevated temperature during the machining process is a root cause of residual stress on the machined part as well as a cause of rapid tool wear. Although several methods have been developed to measure the temperature in machining, the in-situ application of these methods has many technical problems and restrictions. As a result, the utilization of computational methods to predict temperature in machining is very demanding. In this paper, the artificial intelligent models known as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to model and predict the temperature in machining. Several experiments were conducted to validate these models. These experiments were carried out on thin-walled AL7075 work pieces to investigate the effect of different machining parameters on temperature in turning. A thermal imaging Infrared (IR) camera was used to measure the temperature of the cutting area during machining. With respect to experimental data, the ANN and ANFIS models were developed and the results obtained from those models were then compared to the experimental results to evaluate the performance of the models. According to the results, the ANFIS model is superior to the ANN model in terms the accurate and reliable prediction of temperature in machining

Comparative study of ann and anfis models for predicting temperature in machining

The Mechanism of heat generation at the cutting region (tool-workpiece interface) during machining processes is a highly complicated phenomenon and depends on many process parameters. Elevated temperature during the machining process is a root cause of residual stress on the machined part as well as a cause of rapid tool wear. Although several methods have been developed to measure the temperature in machining, the in-situ application of these methods has many technical problems and restrictions. As a result, the utilization of computational methods to predict temperature in machining is very demanding. In this paper, the artificial intelligent models known as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to model and predict the temperature in machining. Several experiments were conducted to validate these models. These experiments were carried out on thin-walled AL7075 work pieces to investigate the effect of different machining parameters on temperature in turning. A thermal imaging Infrared (IR) camera was used to measure the temperature of the cutting area during machining. With respect to experimental data, the ANN and ANFIS models were developed and the results obtained from those models were then compared to the experimental results to evaluate the performance of the models. According to the results, the ANFIS model is superior to the ANN model in terms the accurate and reliable prediction of temperature in machining

Digital Twin Assisted Risk-Aware Sleep ModeManagement Using Deep Q-Networks

Base stations (BSs) are the most energy-consuming segment of mobile networks. To reduce BS energy consumption, different components of BSs can sleep when BS is not active. According to the activation/deactivation time of the BS components,  multiple sleep modes (SMs) are defined in the literature. In this study, we model the problem of BS energy saving utilizing multiple sleep modes as a sequential \ac{MDP} and propose an online \emph{traffic-aware} deep reinforcement learning approach to maximize the long-term energy saving.  However, there is a risk that BS is not sleeping at the right time and incurs large delays to the users. To tackle this issue, we propose to use a digital twin model to encapsulate the dynamics underlying the investigated system and estimate the risk of decision-making (RDM) in advance. We define a novel metric to quantify RDM and predict the performance degradation. The RDM calculated by DT is compared with a tolerable threshold set by the mobile operator. Based on this comparison, BS can decide to deactivate the SMs, re-train when needed to avoid taking high risks, or activate the SMs to benefit from energy savings. For deep reinforcement learning, we use long-short term memory (LSTM), to take into account the long and short-term dependencies in input traffic, and approximate the Q-function. We train the LSTM network using the experience replay method over a real traffic data set obtained from an operator’s BS in Stockholm. The data set contains data rate information with very coarse-grained time granularity. Thus, we propose a scheme to generate a new data set using the real network data set which 1) has finer-grained time granularity and 2) considers the bursty behavior of traffic data.  Simulation results show that using proposed methods considerable energy saving is obtained compared to the baselines at cost of negligible number of delayed users.  Moreover, the proposed digital twin model can predict the performance of the DQN proactively in terms of RDM hence preventing the performance degradation in the network in anomalous situations.QC 20220128AI4Gree

Digital Twin Assisted Risk-Aware Sleep ModeManagement Using Deep Q-Networks

Base stations (BSs) are the most energy-consuming segment of mobile networks. To reduce BS energy consumption, different components of BSs can sleep when BS is not active. According to the activation/deactivation time of the BS components,  multiple sleep modes (SMs) are defined in the literature. In this study, we model the problem of BS energy saving utilizing multiple sleep modes as a sequential \ac{MDP} and propose an online \emph{traffic-aware} deep reinforcement learning approach to maximize the long-term energy saving.  However, there is a risk that BS is not sleeping at the right time and incurs large delays to the users. To tackle this issue, we propose to use a digital twin model to encapsulate the dynamics underlying the investigated system and estimate the risk of decision-making (RDM) in advance. We define a novel metric to quantify RDM and predict the performance degradation. The RDM calculated by DT is compared with a tolerable threshold set by the mobile operator. Based on this comparison, BS can decide to deactivate the SMs, re-train when needed to avoid taking high risks, or activate the SMs to benefit from energy savings. For deep reinforcement learning, we use long-short term memory (LSTM), to take into account the long and short-term dependencies in input traffic, and approximate the Q-function. We train the LSTM network using the experience replay method over a real traffic data set obtained from an operator’s BS in Stockholm. The data set contains data rate information with very coarse-grained time granularity. Thus, we propose a scheme to generate a new data set using the real network data set which 1) has finer-grained time granularity and 2) considers the bursty behavior of traffic data.  Simulation results show that using proposed methods considerable energy saving is obtained compared to the baselines at cost of negligible number of delayed users.  Moreover, the proposed digital twin model can predict the performance of the DQN proactively in terms of RDM hence preventing the performance degradation in the network in anomalous situations.QC 20220128AI4Gree

Infrared temperature measurement and increasing infrared measurement accuracy in the context of machining process

One of the major challenges in the machining process is measuring the temperature accurately which has a considerable importance in calibrating finite element models and investigating thermodynamic of machining process. In the present paper, one of the effective methods for measuring temperature in the machining processes - i.e. infrared imaging - is used and effective parameters which increase measurement accuracy are investigated. One of the most effective parameter in the temperature measurement accuracy of infrared imaging is extracting and calibrating the emissivity coefficient for different temperature ranges. The obtained results show that the lack of precision calibration of the emissivity for different temperature ranges may cause high error in the measurement results. To measure temperature, several experiments are performed for turning a thin walled workpiece which is made of aluminium alloy Al-7075 and the effects of the machining parameters and tool material - polycrystalline diamond (PCD) and cemented carbide - are studied. Based on the achieved results, it can be concluded that the generated temperature in the cutting area can be decreased significantly by using PCD tools and selecting appropriate machining parameters

Reinforcement Learning for Traffic-Adaptive Sleep Mode Management in 5G Networks

The ever-increasing energy consumption of mobile networks has concerned operators about their growing network running costs. Among the different segments of mobile networks, base stations (BSs) have a major energy consumption share. To reduce BS consumption, BS components with similar (de)activation times can be grouped and put into sleep during their times of inactivity. The deeper and more energy-efficient a sleep mode (SM) is, the longer (de)activation time it takes to transition, which incurs a proportional service interruption. Therefore, it is challenging to timely decide on the best SM, bearing in mind the daily traffic fluctuation and imposed service level constraints on delay/dropping. In this study, we leverage an online reinforcement learning technique, i.e., SARSA, and propose an algorithm to opt SM given time and BS load. We use real mobile traffic obtained from a BS in Stockholm to evaluate the performance of the proposed algorithm. Simulation results show that considerable energy saving can be achieved at the cost of acceptable packet dropping level compared to two lower/upper baselines, namely, fixed (non-adaptive) SMs and optimal non-causal solution.QC 20200811AI5Gree

Reinforcement Learning for Traffic-Adaptive Sleep Mode Management in 5G Networks

The ever-increasing energy consumption of mobile networks has concerned operators about their growing network running costs. Among the different segments of mobile networks, base stations (BSs) have a major energy consumption share. To reduce BS consumption, BS components with similar (de)activation times can be grouped and put into sleep during their times of inactivity. The deeper and more energy-efficient a sleep mode (SM) is, the longer (de)activation time it takes to transition, which incurs a proportional service interruption. Therefore, it is challenging to timely decide on the best SM, bearing in mind the daily traffic fluctuation and imposed service level constraints on delay/dropping. In this study, we leverage an online reinforcement learning technique, i.e., SARSA, and propose an algorithm to opt SM given time and BS load. We use real mobile traffic obtained from a BS in Stockholm to evaluate the performance of the proposed algorithm. Simulation results show that considerable energy saving can be achieved at the cost of acceptable packet dropping level compared to two lower/upper baselines, namely, fixed (non-adaptive) SMs and optimal non-causal solution.QC 20211011AI5Gree