A deep learning methodology for automatic extraction and discovery of technical intelligence

Xu, Jiuping; Guo, Lixiang; Jiang, Jiang; Ge, Bingfeng; Li, Mengjun

doi:10.1016/j.techfore.2019.06.004

Cited by 17 publications

(5 citation statements)

References 24 publications

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“…Deep learning is a widely used machine learning technique that combines underlying features to reveal complex patterns [18]. Harnessing high-capacity training algorithms on extensive datasets, this technique excels at discerning sophisticated relationships.…”

Section: Principle Analysis Of Lstmmentioning

confidence: 99%

A ball screw all-round error compensation technology based on novel hybrid deep learning for CNC machine tool

Zhou,

Chen,

Mao

et al. 2024

J. vibroeng.

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Considering the detrimental impact of thermal phenomena on the geometric precision of machine tools, a machine tool ball screw’s omni-directional error model is created using the LSTM neural network algorithm. Subsequently, the machine tool ball screw's omni-directional error compensation module is devised by combining the core functions of the Huazhong numerical control system with the visual programming environment of QT and the numerical computation capability of Matlab. To enhance the practicality and accuracy of the compensation model, this study has employed the Whale Optimization Algorithm (WOA) to optimize the parameters of the LSTM model. This has resulted in an improvement in the model's generalization ability and prediction performance, making it more effective. During the experimental validation phase, the Z-axis error of the machine tool was practically operated and analyzed using the compensation method. Results manifestly show that, after employing the compensation method, the peak amplitude of the Z-axis error fluctuations have been notably curtailed to ±0.006 mm – a considerable reduction compared to the initial error bandwidth of ±0.0145 mm. These empirical findings substantiate the efficacy of the proposed compensation strategy in substantially boosting the machining precision of products, thus furnishing a substantial and instructive benchmark for future inquiries into CNC machine tool error compensation technologies.

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Section: Principle Analysis Of Lstmmentioning

confidence: 99%

A ball screw all-round error compensation technology based on novel hybrid deep learning for CNC machine tool

Zhou,

Chen,

Mao

et al. 2024

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…We suggest that the key themes are improved access to healthcare, responsiveness and privacy design, and we systematize their use. These results are important for understanding the role of AI and other emerging technologies in transforming modern industries (Jiang et al, 2017;Li, 2020;Boneva, 2018;Xu et al, 2019;Kulkov, 2021). Companies working with improved access to the healthcare theme are mainly focused on developing solutions for diagnostics and therapy.…”

Section: Start-up's Value Creationmentioning

confidence: 99%

Next-generation business models for artificial intelligence start-ups in the healthcare industry

Kulkov

2021

IJEBR

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PurposeValue creation based on artificial intelligence (AI) can significantly change global healthcare. Diagnostics, therapy and drug discovery start-ups are some key forces behind this change. This article aims to study the process of start-ups' value creation within healthcare.Design/methodology/approachA multiple case study method and a business model design approach were used to study nine European start-ups developing AI healthcare solutions. Obtained information was performed using within and cross-case analysis.FindingsThree unique design elements were established, with 16 unique frames and three unifying design themes based on business models for AI healthcare start-ups.Originality/valueOur in-depth framework focuses on the features of AI start-up business models in the healthcare industry. We contribute to the business model and business model innovation by systematically analyzing value creation, how it is delivered to customers, and communication with market participants, as well as design themes that combine start-ups and categorize them by specialization.

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“…Deep learning can learn complex features by combining simple features learned from data. It takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches in various machine learning tasks [12]. The state of the bidirectional long short-term memory (BiLSTM) deep learning network changes with time.…”

Section: Introductionmentioning

confidence: 99%

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

Liu

et al. 2021

Adv. Manuf.

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The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

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A deep learning methodology for automatic extraction and discovery of technical intelligence

Cited by 17 publications

References 24 publications

A ball screw all-round error compensation technology based on novel hybrid deep learning for CNC machine tool

A ball screw all-round error compensation technology based on novel hybrid deep learning for CNC machine tool

Next-generation business models for artificial intelligence start-ups in the healthcare industry

Thermal error modeling based on BiLSTM deep learning for CNC machine tool

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