# An ANN-Based Temperature Controller for a Plastic Injection Moulding System

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. ANN Controller Design

#### 2.1. Reference Digital Controller Design

**A:**

**B**:

_{1}is controller output (could be described as an error at the beginning of the first control step multiplied by controller gain).

_{2}is controller output (could be described as an error at the beginning of the second control step multiplied by controller gain).

_{1}should be equal to 1 (1 means 100% of power applied to the load). Consequently, the speed of the temperature rise at the end of the first control step will reach the maximum. The second control step should perform a stabilisation of the temperature on the given level so the inertness of the heating system could be used for this purpose. The value of m

_{2}should be equal to 0 in this case (0 means no power applied to the load). However, a certain amount of power must be applied to the load after the end of the second control step to support the temperature at the desired level. The value of the further control steps can be calculated using the following formula:

_{1}and m

_{2}are known, then Equation (21) has two unknown variables—h

_{1}, h

_{2}. The resolution of Equation (21) for various temperatures T(0) determines the duration of the control steps h

_{1}, h

_{2}and, therefore, ensures the implementation of a digital controller operating under time-pulse modulation. Unfortunately, this system has no analytical solution and requires the application of a numerical method to obtain the control step durations. Figure 4 illustrates the transient of the system with the plant described by Equation (5) and control steps calculated using Equations (21) and (22).

#### 2.2. ANN Structure and Training

- Firstly, the maximum and minimum values of temperature should be selected;
- Then, the duration of both control steps is calculated using Equation (21) for the maximum given temperature;
- The plant response is calculated using transfer Equation (5) for each sampling period of the system. The error is calculated as a difference between the given and current temperatures. The speed of the error varying is calculated as a difference between the current and previous errors (the distance between error values used for speed calculation could be bigger than one sampling period, it depends on the sampling rate and plant dynamic properties);
- The values of errors and speed are stored in two-dimensional input array. The values of control signal, which are “1” for the first control step and “0” for the second, are stored in the output array;
- The target temperature is decremented in the given amount of degrees and process repeats from stage 2 of this algorithm, but on stage 4 the data is placed at the end of the input and output arrays;
- The algorithm stops when the minimum temperature chosen at the first stage is achieved.

## 3. Matlab Simulation and Experimental Validation

#### 3.1. Matlab Simulation

#### 3.2. Experimental Validation

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Block diagram of a close-loop temperature control with an ANN (artificial neural network) controller.

T_{target},°C | 150 | 200 | 300 | ||||||
---|---|---|---|---|---|---|---|---|---|

Control | (I) | (II) | (III) | (I) | (II) | (III) | (I) | (II) | (III) |

Bar | 872 | 604 | 693 | 1062 | 762 | 853 | 1513 | 1213 | 1186 |

Nozzle | 523 | 299 | 364 | 590 | 321 | 393 | 753 | 500 | 506 |

Cartridge | 423 | 126 | 327 | 458 | 148 | 227 | 500 | 192 | 244 |

T_{target},°C | 150 | 200 | 300 | ||||||
---|---|---|---|---|---|---|---|---|---|

Control | (I) | (II) | (III) | (I) | (II) | (III) | (I) | (II) | (III) |

Bar | 4.80 | 3.00 | 1.20 | 2.40 | 2.40 | 1.25 | 0.63 | 0.83 | 1.67 |

Nozzle | 2.07 | 1.53 | 0.20 | 1.40 | 1.40 | 0.15 | 0.67 | 0.33 | 0.10 |

Cartridge | 8.80 | 28.2 | 0.27 | 13.9 | 22.7 | 1.9 | 15.7 | 14.7 | 2.37 |

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**MDPI and ACS Style**

Khomenko, M.; Veligorskyi, O.; Chakirov, R.; Vagapov, Y.
An ANN-Based Temperature Controller for a Plastic Injection Moulding System. *Electronics* **2019**, *8*, 1272.
https://doi.org/10.3390/electronics8111272

**AMA Style**

Khomenko M, Veligorskyi O, Chakirov R, Vagapov Y.
An ANN-Based Temperature Controller for a Plastic Injection Moulding System. *Electronics*. 2019; 8(11):1272.
https://doi.org/10.3390/electronics8111272

**Chicago/Turabian Style**

Khomenko, Maksym, Oleksandr Veligorskyi, Roustiam Chakirov, and Yuriy Vagapov.
2019. "An ANN-Based Temperature Controller for a Plastic Injection Moulding System" *Electronics* 8, no. 11: 1272.
https://doi.org/10.3390/electronics8111272