Enhancing cutting tool sustainability based on remaining useful life prediction

As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustainability. A non-linear cutting tool remaining useful life prediction model is developed based on tool wear historical data. Probability distribution function and cumulative distribution function are used to quantize the uncertainty of the prediction. Under a constant machining condition, a cutting tool life is extended according to its specific remaining useful life prediction, rather than a unified one. Under various machining conditions, machining parameters are optimized to improve efficiency or capability. Cutting tool sustainability is assessed in economic, environmental and social dimensions. Experimental study verifies that both material removal rate and material removal volume are improved. Carbon emission and cutting tool cost are also reduced. The balance between benefit and risk is achieved by assigning a reasonable confidence level. Cutting tool sustainability can be enhanced by improving cutting tool utilization at controllable risk.©2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

In-process tool condition forecasting based on a deep learning method

It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing

Air pollution control or economic development? Empirical evidence from enterprises with production restrictions

Production restriction is an environmental regulation adopted in China to curb the air pollution of industrial enterprises. Frequent production restrictions may cause economic losses for enterprises and further hinder their green transformation. Polluting enterprises are faced with the dilemma of choosing environmental protection or economic development. Using panel data on industrial enterprises in China from 2016 to 2019, this paper evaluates the impact of production restrictions on both enterprises' environmental and economic performance with regression models. The results show that production restrictions significantly drop the concentrations of SO2 and NOx emitted from polluting enterprises. Meanwhile, production restrictions have significant negative effects on operating income, financial expenses, net profit, and environmental protection investment. The mechanism analysis reveals that production restrictions mitigate air pollutant concentrations by increasing the number of green patents and improving total factor productivity, which also verifies the Porter hypothesis. However, there is a masking mediating effect of environmental investment, which indicates that the reduction of environmental investment hinders the enterprise's efforts to control air pollution. In addition, heterogeneous analysis shows that the economic shock on microenterprises is larger than that on small enterprises. Implementing production restrictions for microenterprises may be a way to eliminate their backwards production capacity

Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images

Generating 3D faces from textual descriptions has a multitude of applications, such as gaming, movie, and robotics. Recent progresses have demonstrated the success of unconditional 3D face generation and text-to-3D shape generation. However, due to the limited text-3D face data pairs, text-driven 3D face generation remains an open problem. In this paper, we propose a text-guided 3D faces generation method, refer as TG-3DFace, for generating realistic 3D faces using text guidance. Specifically, we adopt an unconditional 3D face generation framework and equip it with text conditions, which learns the text-guided 3D face generation with only text-2D face data. On top of that, we propose two text-to-face cross-modal alignment techniques, including the global contrastive learning and the fine-grained alignment module, to facilitate high semantic consistency between generated 3D faces and input texts. Besides, we present directional classifier guidance during the inference process, which encourages creativity for out-of-domain generations. Compared to the existing methods, TG-3DFace creates more realistic and aesthetically pleasing 3D faces, boosting 9% multi-view consistency (MVIC) over Latent3D. The rendered face images generated by TG-3DFace achieve higher FID and CLIP score than text-to-2D face/image generation models, demonstrating our superiority in generating realistic and semantic-consistent textures.Comment: accepted by ICCV 202

Simultaneous Formation of CH₃NH₃PbI₃ and electron transport layers using antisolvent method for efficient perovskite solar cells

A new antisolvent method was developed to prepare CH₃NH₃PbI₃ and electron transport layers for making efficient hybrid perovskite solar cells. By directly using [6,6]-phenyl-C61-butyric acid methyl ester in chlorobenzene solution as antisolvent, CH₃NH₃PbI₃ and electron transport layers were simultaneously formed in the films. This method not only simplifies the fabrication process of devices, but also produces uniform perovskite films and improves the interfacial structures between CH₃NH₃PbI₃ and electron transport layers. Large perovskite grains were observed in these films, with the average grain size of >1 μm. The so-formed CH₃NH₃PbI₃/electron transport layers demonstrated good optical and charge transport properties. And perovskite solar cells fabricated using these simultaneously-formed layers achieved a higher power conversion efficiency of 16.58% compared to conventional antisolvent method (14.92%). This method reduces nearly 80% usage of chlorobenzene during the fabrication, offering a more facile and environment-friendly approach to fabricate efficient perovskite solar cells than the conventional antisolvent method