Abstract
The Big Data driven approach has become a new trend for manufacturing optimization. In this chapter, an innovative Big Data enabled Intelligent Immune System (I2S) has been developed to monitor, analyze and optimize machining processes over lifecycles in order to achieve energy efficient manufacturing. There are two major functions in I2S: (1) an Artificial Neural Networks (ANNs)-based algorithm and statistical analysis tools are used to identify the abnormal electricity consumption patterns of manufactured components from monitored Big Data. An intelligent immune mechanism is devised to adapt to the condition changes and process dynamics of machining systems; (2) a re-scheduling algorithm is triggered if abnormal manufacturing conditions are detected thereby achieving multi-objective optimization in terms of energy consumption and manufacturing performance. In this research, Computer Numerical Controlled (CNC) machining processes and industrial case studies have been used for system validation. The novelty of I2S is that Big Data analytics and intelligent immune mechanisms have been integrated systematically to achieve condition monitoring, analysis and energy efficient optimization over manufacturing execution lifecycles. The applicability of the system has been validated by multiple industrial trials in European factories. Around 30% energy saving and over 50% productivity improvement have been achieved by adopting I2S in the factories.
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Abbreviations
- \({E}_{\mathrm{waiting}}\left({M}_{i}\right)\) :
-
The energy consumption of Machine Mi during waiting.
- \({E}_{machining}({M}_{i},{J}_{j})\) :
-
The energy consumption of machining Component Jj by machine Mi.
- \({E}_{machining}\left({M}_{i}\right)\) :
-
The energy consumption of Machine Mi during machining.
- E total (M i ) :
-
The energy consumed during all the phases of Machine Mi.
- F 1 :
-
A measure of a test's accuracy.
- FN :
-
False Negative.
- FP :
-
False Positive.
- M :
-
The total number of components to be machined.
- MAPE :
-
Mean Absolute Percentage Error.
- MAPEL, MAPEU:
-
Lower bound and upper bound of Mean Absolute Percentage Error.
- ME :
-
The Mean Error between two patterns.
- N :
-
The bigger number of the samples of the two patterns.
- N 1 :
-
The total number of the measured power points in Smeasure.
- PL, PU:
-
Lower bound and upper bound of a standard energy pattern.
- Precision :
-
The proportion of all the positive predictions that are correct.
- Recall :
-
Proportion of all the real positive observations that are correct.
- S measure :
-
Measured energy pattern for machining a component.
- S standard :
-
Standard energy pattern for machining a component.
- \({S\mathrm{^{\prime}}}_{measure}\) :
-
The shifted Smeasure to Sstandard.
- t21:
-
Time delay.
- TP:
-
True Positive.
- Xcoef:
-
Cross-correction coefficient.
- \({z}_{i}^{N}\) :
-
The upper bound of the \({i}^{th}\) objective function.
- \({z}_{i}^{U}\) :
-
The lower bound of the \({i}^{th}\) objective function.
- \({\mu }_{measure}\), \({\mu }_{standard}\):
-
The means of time patterns.
- \({\sigma }_{Smeasure,Sstandard}\) :
-
The cross-covariance between the pair of patterns.
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Wang, S., Liang, Y.C., Li, W.D. (2021). Big Data Enabled Intelligent Immune System for Energy Efficient Manufacturing Management. In: Li, W., Liang, Y., Wang, S. (eds) Data Driven Smart Manufacturing Technologies and Applications. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-66849-5_3
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DOI: https://doi.org/10.1007/978-3-030-66849-5_3
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