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Predicting machined surface roughness is critical for estimating a part’s performance characteristics such as susceptibility to fatigue and corrosion. Prior studies have indicated that power consumed at the tool-chip interface may represent an indicator for the surface integrity of the machining process. However, no quantita-tive association has been reported between the machining power and surface roughness due to a lack of data to develop predictive models. This paper presents a data synthesis method to address this gap. Specifically, a conditional generative adversarial network (CGAN) is developed to synthesize power signals associated with varying process parameter combinations. The quality of the synthesized signals is evaluated against experimentally measured power signals by examining the consistency in: 1) the spatial pattern of the signals induced by the cutting process as shown in the frequency domain, and 2) the temporal pattern as shown in the clustering of the synthesized and measured signals corresponding to the same parameter combination. The synthesized signals are then used to augment the measured signals and develop a convolutional neural network (CNN) for predicting the machined surface roughness. Experiments performed using H13 tool steel have shown that data augmentation by CGAN has effectively reduced the error of the surface roughness prediction from 58 %, when no synthetic data is used for CNN training, to 9.1 % when 250 synthetic samples are used. The results demonstrate the effectiveness of CGAN as a data augmentation method and CNN for mapping machining power to surface roughness. 

In this work, we proposed a novel learning-based task to core mapping technique to improve lifetime and reliability based on advanced deep reinforcement learning. The new method is based on the observation that on-chip temperature sensors may not capture the true hotspots of the chip, which can lead to sub-optimal control decisions. In the new method, we first perform data-driven learning to model the hotspot activation indicator with respect to the resource utilization of different workloads. On top of this, we proposed to employ a recently proposed, highly robust, sample-efficient soft-actor-critic deep reinforcement learning algorithm, which can learn optimal maximum entropy policies to improve the long-term reliability and minimize the performance degradation from NBTI/HCI effects. Lifetime and reliability improvement is achieved by assigning a reward function, which penalizes continuously stressing the same hotspots and encourages even stressing of cores. The proposed algorithm is validated with an Intel i7-8650U four-core CPU platform executing CPU benchmark workloads for various hotspot activation profiles. Our experimental results show that the proposed method balances the stress between all cores and hotspots, and achieves 50% and 160% longer lifetime compared to non-hotspot-aware and Linux default scheduling respectively. The proposed method can also reduce the average temperature by exploiting the true-hotspot information.