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Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4.0

Abstract

With the rise of Industry 4.0-related technology in the plastic and composite industry, a new wealth of data from the production process is becoming available to manufacturers. The effective utilization of this data towards improving quality and output is therefore of critical importance but requires knowledge of the data that is truly useful and the application of that data to pre-developed models or trained algorithms. Accordingly, in this research, 12 different online data sources in the injection molding process are evaluated to determine their relative degree of importance in predicting variations on final part quality indices, namely part weight, thickness, and diameter. These data are obtained during each injection molding cycle using a data acquisition system connected to eight in-mold sensors and four machine data sources. Three distinct types of perturbations are introduced into the process to challenge the range of detection capacities of these various data sources: shot size variations, material disturbances, and shutdown of the mold cooling system. The resultant curves from these studies are then analyzed for critical values, and partial least square (PLS) regressions performed using the extracted values as predictors and the final part quality indices as responses. Using the standard coefficients from the PLS analysis, rankings of the correlations between the extracted values and final part quality indices are generated, indicating which data sources best detected variations in the final produced parts for each of the three perturbations.

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Acknowledgments

The authors would like to acknowledge Kistler Instrument Corp. for providing the sensor system and technical support.

Funding

The authors wish to recognize the financial support of the South Carolina Research Authority (SCRA) under the SCRA-Academic Collaboration Team Feasibility Grants (Award# 2012732), Clemson Forward R-Initiatives Program: Clemson Research Fellows, the Robert Patrick Jenkins Professorship, and the Dean’s Faculty Fellow Professorship.

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Correspondence to Srikanth Pilla.

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Appendix

Appendix

Fig. 13
figure13

Demonstration of the extracted values from the graph of screw position vs. cycle time

Table 4 Definition and significance of the extracted values from the screw position graph
Fig. 14
figure14

Demonstration of the extracted values from the graph of clamping force vs. cycle time

Table 5 Definition and significance of the extracted values from the clamping force graph
Fig. 15
figure15

Demonstration of the extracted values from the graph of pressure captured by the direct sensor at the post-gate location

Table 6 Definition and significance of the extracted values from the post-gate pressure graph
Fig. 16
figure16

Demonstration of the extracted values from the graph of pressure captured by the direct sensor at the end-of-flow location

Table 7 Definition and significance of the extracted values from the end-of-flow pressure graph
Fig. 17
figure17

Demonstration of the extracted values from the graph of pressure captured by the direct sensor at the center of the cavity

Table 8 Definition and significance of the extracted values from the graph of pressure at the center
Fig. 18
figure18

Demonstration of the extracted values from the graph of pressure captured by the contactless sensor

Table 9 Definition and significance of the extracted values from the graph of contactless pressure sensor
Fig. 19
figure19

Demonstration of the extracted values from the graph of pressure captured by the indirect sensor

Table 10 Definition and significance of the extracted values from the graph of indirect pressure sensor
Fig. 20
figure20

Superposition of mold position and barrel pressure vs. cycle time

Fig. 21
figure21

Comparison of data captured by the strain gauge from several consecutive cycles with identical process parameters.

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Farahani, S., Brown, N., Loftis, J. et al. Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4.0. Int J Adv Manuf Technol 105, 1371–1389 (2019). https://doi.org/10.1007/s00170-019-04323-8

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Keywords

  • Industry 4.0
  • Process monitoring
  • Automatic quality control
  • Injection molding
  • In-mold sensors
  • Data analysis
  • Partial least square (PLS) regression
  • Predictive modeling