# Prediction of Shrinkage Behavior of Stretch Fabrics Using Machine-Learning Based Artificial Neural Network

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials

#### 2.2. Methodology

#### 2.2.1. Process Sequence

#### 2.2.2. Artificial Neural Network to Predict Shrinkage

_{1}and M

_{2}, are considered:

_{1}= E

_{1}/I

_{2}M

_{2}= E

_{2}/I

_{1}

_{1}and I

_{2}can be measured by observing a pick in fabric, and E

_{2}and I

_{1}can be measured by observing ends in the repeat.

#### 2.2.3. Significance of Backpropagation

#### 2.2.4. Building a Machine Learning Model to Predict Shrinkage Behavior Using ANN

#### 2.2.5. Training the Artificial Neural Network Model

#### 2.2.6. Testing the Model on a Test Data Set

#### 2.2.7. Authentication and Using the Model for the Prediction of a New Data Set

## 3. Results and Discussion

#### 3.1. Error Backpropagation Algorithm for Shrinkage Percentage

_{j}(n) = d

_{j}(n) − y

_{j}(n).

_{j}(n) produced at the input v

_{j}(n) = ∑

_{i = 0}

^{p}w

_{ji}(n)y

_{i}(n).

_{j}(n) = φ

_{j}(v

_{j}(n)).

_{j}(n) − o

_{j}(n)] o

_{j}(n) [1 − o

_{j}(n)].

_{I}(n) [1 − y

_{I}(n)] ∑

_{k}δ

_{k}(n) w

_{kj}(n).

_{ji}(n) applied to the synaptic weight is Δw

_{ji}(n) = η × δj(n) × yi(n).

_{I}(n) = 1/(1 + exp(−v

_{j}(n)).

_{j}(v) = 2a/(1 − exp(bv)) − a.

_{ji}(n) = αΔw

_{ji}(n − 1) + η δj(n) y

_{i}(n).

#### 3.2. Correlation of Factors with Respect to Boil-Off Width

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Parameters | Minimum | Maximum | Average |
---|---|---|---|

Warp count (Ne) | 7 | 50 | 22 |

Weft count (Ne) | 6 | 50 | 18 |

Spandex in weft core (Ne) | 7 | 140 | 68 |

Ends/inch (EPI) | 50 | 186 | 101 |

Picks/inch (PPI) | 38 | 108 | 65 |

Greige width (cm) | 125 | 210 | 180 |

Boil-off width (cm) | 38 | 177 | 130 |

Areal density (g/m^{2}) | 96 | 327 | 199 |

Properties | R-Value | p-Value |
---|---|---|

Warp count | −0.089 | 0.002 |

Weft count | −0.048 | 0.088 |

Spandex% | −0.119 | 2.136 |

Ends/inch | −0.040 | 0.150 |

Picks/inch | 0.071 | 0.012 |

Greige width | 0.635 | 1.184 |

M1 | −0.168 | 2.042 |

M2 | −0.119 | 2.265 |

Errors | Values |
---|---|

Mean squared error | 48.64 |

Mean absolute error (%) | 4.21 |

Mean standard error | 48.60 |

Root mean squared error | 6.97 |

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**MDPI and ACS Style**

Ahirwar, M.; Behera, B.K.
Prediction of Shrinkage Behavior of Stretch Fabrics Using Machine-Learning Based Artificial Neural Network. *Textiles* **2023**, *3*, 88-97.
https://doi.org/10.3390/textiles3010007

**AMA Style**

Ahirwar M, Behera BK.
Prediction of Shrinkage Behavior of Stretch Fabrics Using Machine-Learning Based Artificial Neural Network. *Textiles*. 2023; 3(1):88-97.
https://doi.org/10.3390/textiles3010007

**Chicago/Turabian Style**

Ahirwar, Meenakshi, and B. K. Behera.
2023. "Prediction of Shrinkage Behavior of Stretch Fabrics Using Machine-Learning Based Artificial Neural Network" *Textiles* 3, no. 1: 88-97.
https://doi.org/10.3390/textiles3010007