# Investigation on Product and Process Fingerprints for Integrated Quality Assurance in Injection Molding of Microstructured Biochips

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Experimental Setup and Methods

#### 2.1. Molding Tool Geometry

#### 2.2. Injection Molding Process and Experimental Conditions

^{4}× 3 full factorial experiment was utilized in order to investigate the experimental process window. The parameters under consideration are: Tmelt (Tm) [°C], Tmould (Tmld) [°C], Injection Speed (InjSp) [mm/s] and Packing Pressure (PackPr) [bar] that, as from well-established research [18] and preliminary screening experiments are known to be the most significant parameters affecting the quality of injection molded components and surface replication. Table 1 presents the experimental treatments. The process parameter levels were selected by assessing the specification of the material (Figure 5), a commercial grade of acrylonitrile butadiene styrene (ABS, Styrolution Terluran GP-35, INEOS Styrolution GmbH, Frankfurt am Main, Germany), which is characterized by a relatively large processing window. Other parameters such as packing (t

_{pack}= 10 s) and cooling times (t

_{cool}= t

_{pack}+ 10 s) were set on levels high enough to avoid their influence on the responses of the experiment.

#### 2.3. Pillar Dimensional Measurement and Uncertainty Evaluation Procedure

_{cal}which is the standard uncertainty as evaluated from a calibrated step height artefact to have traceable measurements, u

_{b}which is the standard uncertainty associated with the systematic error (b) of the measurement process, which is the measuring instrument bias. Thirdly, the u

_{th}is the standard uncertainty associated with the systematic error of the measurement process based on the heat expansion coefficient deviations of the material, since the measurements were not conducted at the reference temperature, and lastly u

_{p}is the uncertainty associated with the manufacturing variation from either mold or parts (u

_{pmould}and u

_{ppart}), which is calculated using a square distribution in the modified ISO 15530-3 (Equation (4)). The measurement on individual pillars, features, and different molded parts are all affected by instrument repeatability. Thus, for u

_{ppart}the maximum value of uncertainty contributor related to instrument and process is considered in order to avoid underestimating the uncertainty. These contributors are part of u

_{ppart}, where: u

_{ppillar}is the standard deviation of five repeated measurements on the same pillar; u

_{pfeatures}, the standard deviation of repeated measurements on four different pillar areas to estimate feature repeatability in terms of polymer replication and u

_{psample}the standard deviation of repeated measurements on 3 different samples on four different pillar area. The uncertainty contributors are used to calculate the uncertainty of the mold (Equation (1)) and part pillar (Equation (2)) measurements, as well as the deviation uncertainty (Equation (3)). The values of the specific uncertainties per position and experimental runs are provided in Table 4 and Table 5, respectively. Table 5 provides information on the expanded uncertainty for pillar height and height deviation measurements per run.

#### 2.4. Product Fingerprint as Quality Indicator

#### 2.5. Process Fingerprint as Quality Indicators

#### 2.5.1. Work of Error and Integrated Squared Error

_{0}the reference signal, y

_{i}any cycle signal, and i = 1, 2, …, N cycles.

_{work}) and the integrated squared error (ISE) [22] as described in Equations (7) and (8), respectively, are used to extract the information as one single value for every signal associated with the deviation of each processing cycle with respect to the reference cycle. The performance of the alignment error and the ISE as a quality indicator in consequence will be discussed in a following section of the paper.

#### 2.5.2. Shift Error

_{0}(t) and y

_{i}(t) is described by Equation (9) [23].

_{i}(i = 1, 2, …, N) in association to the reference signal y

_{0}. Cross-correlation measures the similarity between a reference y

_{0}and shifted (lagged) copies of y as a function of the lag as illustrated in Figure 8. The “Shift” error can be used as a QI and will be discussed further in Section 3.3.1. An example of cross correlation alignment from experimental Run 1 is provided in Figure 9.

#### 2.5.3. Work Deviation

#### 2.5.4. Dynamic Time Warping

_{i}to the reference vector y

_{0}is defined as the minimum distance from the beginning of the DTW table to the current position (k, j). Based on the dynamic programming (DP) algorithm [25] the DTW table can be calculated as follows [26] in Equation (11):

_{i}(k) and y

_{0}(j) of the input and reference signals y

_{0}and y

_{i}and is calculated with the use of L2-norm in Equation (12).

_{0}and y

_{i}, onto a common set of instances such that the warping distance “WarpDis”, the sum of Euclidean distances between corresponding points y

_{i}(k) and y

_{0}(j), is minimized. To properly match the input and reference signals, the algorithm repeats each element of vectors y

_{i}and y

_{0}as many times as necessary resulting in two signals y

_{i}* and y

_{0}* of equal size, as illustrated in Figure 11. As such, the warping distance “WarpDis” can be used as a QI.

#### 2.5.5. Signal Integral

_{x}” is calculated with Equation (14) and of the time resolved data from the whole signal y(t) recorded starting at the injection phase (t

_{0}= 0 s), till the end of the packing phase (t

_{n}= 11 s). The integral is related to the energy stored in the system and can differ on the measured quantity. When the integral is calculated from the pressure signals, it provides the approximate value of energy stored in the polymer from the melting, compression, and injection of the molten polymer in the cavity.

#### 2.5.6. Signal Power

_{x}” is given as the sum of the absolute squares of the time-domain samples of the signals divided by the signal length. Similar to the integral, signal power relates to the energy of the system for all the recorded frequencies of the signal.

## 3. Results and Discussion

#### 3.1. Dimensional Measurements and Uncertainty Calculation

_{part}) and Equation (3) (U

_{dev}). The uncertainty bars as illustrated on the bar graphs are associated with the combined measurement uncertainty (U

_{dev}) (Figure 12) from both mold (U

_{mold}) and parts (U

_{part}) (Figure 10) measurements (Equation (3)) as calculated based in the ISO 15530-3 [20].

#### 3.2. Product Fingerprint Analysis

_{i}and y

_{i}: data points in the vectors; $\overline{\mathrm{x}}$ and $\overline{\mathrm{y}}$: the sample means of datasets X and Y.

^{4}× 3 full factorial experiment were used for the correlation analysis and the calculation of the absolute Pearson coefficients.

#### 3.3. Process Fingerprint Analysis

- (1)
- The first type is characterized by those indicators which originated from the deviation of both the transient injection pressure and injection speed signals with respect to the reference signals such as error of alignment (ε(t)), integrated squared error (ISE), cross correlation shift error (Shift), and dynamic time warping (WarpDis);
- (2)
- Those indicators where the “signal integral” (I
_{x}) and “signal power” (SP_{x}) were calculated from each signal to extract the information from the signal curve and are subsequently converted into a single value representative of the second type.

#### 3.3.1. Process Fingerprint Based on Indicators of Type 1

#### 3.3.2. Process Fingerprint Based on Indicators of Type 2

## 4. Conclusions

- The variation of the IM process parameters settings has an effect on the manufacturing quality and replication of the molded μ-pillar structured components placed both in the cavities as well as on the runners.
- The variation of the process was used to assess the suitability of μ-pillars in the eight different positions to act as product fingerprint. The analysis was based on their replication quality. A correlation analysis was then used for verification. This track was focused on the μ-pillars positioned on the runners of the molding in positions C1RP2 and C2RP2. For Cavity 1, it can be seen that the dataset position C1RP2 can be used to monitor the quality of the μ-features on the part, especially for position C1PP1 (near the gate) with the highest correlation to originate to the combination is C1PP1/C1RP2 (|$\mathsf{\rho}$| = 0.73) (i.e., near the gate/on the runner), followed by C1PP2/C1RP2 (|$\mathsf{\rho}$| = 0.60) and C1PP3/C1RP2 (|$\mathsf{\rho}$| = 0.57) (i.e., far from the gate/on the runner). Instead, the μ-pillars on the runner of Cavity 2 (C2RP2), did not present strong correlations with respect to the measurands of the features in the cavity, indicating that these μ-pillars are not suitable to serve as a “product fingerprint”.
- Two different types of process fingerprint candidates were assessed for their suitability to act as quality indicators of the micro structures on the molded parts. Results show that only a small number of process fingerprint candidates from the category of deviation-based process fingerprints (i.e., Type 1) were considered suitable for process monitoring when considered together with the proper measurand. From the Type 2 indicators in fact, no candidate presented a strong correlation with the quality of a measurand. This indicates that the integral and signal power of machine injection pressure and speed signals could not be used for the monitoring of the overall part quality in the current application.
- Finally, it can be concluded that the deviation of the quality of the part’s μ-pillars can be monitored by monitoring the deviation of the “Workdev-InjPr”, “ISE-InjPr”, “ISE-InjSp”, and “WarpDis-InjPr” process fingerprints. These fingerprints present similar trends with measurands for most of the treatments in the investigated process window.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Half section view (

**a**) and ¾ views of the movable (

**b**) and stationary (

**c**) sides of the mold used for the experiment.

**Figure 3.**Molded geometry with fingerprint structures on the part and runners (

**a**,

**b**), and measurement positions on Cavity 1 (

**c**) and Cavity 2 (

**d**).

**Figure 4.**(

**a**) Molded component with fingerprint structures on the part and runners. The fingerprints at the front (top) and back (bottom) side of the components are visible. (

**b**) Bottom (Cavity 1) and (

**c**) top (Cavity 2) parts of the microfluidic system.

**Figure 5.**(

**a**) PvT and (

**b**) viscosity plots of material Styrolution Terluran GP-35 (Acrylonitrile Butadiene Styrene—ABS) [19].

**Figure 6.**Flow diagram of the experimental sequence. The figure denotes the measurement areas on the part (i.e., PP1 = Part Position 1) and on the runner (i.e., RP2 = Runner Position 2) without the indication of cavity as seen in the text (i.e., Cavity 2 RP2 = C2RP2)

**Figure 7.**(

**a**) SEM 3D image of the pillars and (

**b**–

**d**) pillar height measurement procedure, (

**b**) step 1: extracting cros-section profiles, (

**c**) step 2: assessing pillar height from the four extracted profiles as indicated by different color, and (

**d**) 3D representation of the pillar [5].

**Figure 9.**Signals of injection speed and pressure from part cycles 1, 5, and 10 of Run-1 before (

**top**) and after cross correlation alignment (

**bottom**).

**Figure 10.**Representation of work deviation, given by the non-intersecting area of signals y

_{0}(t) and y

_{i}(t).

**Figure 11.**Alignment of original (

**top**) and cross-correlated (

**bottom**) signals of injection speed and pressure of Run 1 and part c ycles 1, 5, and 10 using dynamic time warping (DTW).

**Figure 12.**Average pillar height and U

_{part}measurement uncertainty per position on the part for Cavities 1 and 2 parts. The x-axis here represents the experimental DOE runs (R1 for Run 1) as presented in Table 1.

**Figure 13.**Comparison of average pillar height deviation (mold-part) per position for Cavities 1 and 2.

**Figure 14.**Influence of IM process on the eight measurand deviations (from mold values) and “product fingerprints candidates”. (

**a**) Position PP1 in Cavities 1 and 2, (

**b**) position PP2 in Cavities 1 and 2, (

**c**) position PP3 in Cavities 1 and 2, (

**d**) position RP2 in Cavities 1 and 2. The figure presents the main effects (left column) and the Pareto graphs (right column), with a schematic representation of the measurement areas to be provided at the top. The error bars in the main effect’s plots represent the measurement uncertainty from the dataset of the respective product fingerprint (Table 6).

**Figure 15.**Pearson correlation coefficient plots of measurands to the pillar “product fingerprint” positioned on the runner of the molding (

**a**) in Cavity 1 and (

**b**) in Cavity 2. A perfect correlation |$\mathsf{\rho}$| = 1 exists only for combinations of the same dataset.

**Figure 16.**Example of process fingerprint candidates to measurand trends for experimental run 16 based on (

**a**) nominal signals and (

**b**) cross correlated signals. The legend of the graphs denotes both the measurand datasets (i.e., C1PP1: Cavity 1–Position PP1) and the deviation based (Type 1) “process fingerprints”.

**Figure 18.**Pearson correlation coefficient plots of measurands to the pillar “product fingerprint” positioned on the runner of the molding (

**a**) in Cavity 1 and (

**b**) in Cavity 2.

Run | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Parameter | Unit | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |

Tm | [°C] | 220 | 260 | 220 | 260 | 220 | 260 | 220 | 260 | 220 | 260 | 220 | 260 | 220 | 260 | 220 | 260 |

Tmld | [°C] | 40 | 40 | 60 | 60 | 40 | 40 | 60 | 60 | 40 | 40 | 60 | 60 | 40 | 40 | 60 | 60 |

InjSp | [mm/s] | 100 | 100 | 100 | 100 | 140 | 140 | 140 | 140 | 100 | 100 | 100 | 100 | 140 | 140 | 140 | 140 |

PackPr | [bar] | 440 | 440 | 440 | 440 | 440 | 440 | 440 | 440 | 540 | 540 | 540 | 540 | 540 | 540 | 540 | 540 |

Measurement Settings | |
---|---|

Objective | ×20 |

Exposure | 3.05 ms |

Contrast | 1.11 |

Vertical resolution | 299 nm |

Uncertainty Contributions | Mold Inserts | Parts | ||
---|---|---|---|---|

Cavity 1 | Cavity 2 | Cavity 1 | Cavity 2 | |

u_{cal} [μm] | 0.1 | 0.1 | 0.1 | 0.1 |

u_{th} [μm] | 0.003 | 0.003 | 0.003 | 0.003 |

u_{b} [μm] | 0.034 | 0.034 | 0.034 | 0.034 |

u_{ppart} [μm] | - | - | 0.26–0.97 | 0.22–0.95 |

u_{mold} [μm] | 0.11–0.12 | 0.13–0.79 | - | - |

Expanded Uncertainties | Mold Inserts | Parts | ||
---|---|---|---|---|

Cavity 1 | Cavity 2 | Cavity 1 | Cavity 2 | |

U_{part} [μm] | - | - | 0.54–1.94 | 0.45–1.91 |

U_{mold} [μm] | 0.25–0.26 | 0.29–1.58 | - | - |

U_{dev} [μm] | 0.54–3.92 |

Expanded Uncertainties per Run (Uexp [μm]) | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 | R15 | R16 | |

Cavity 1 | ||||||||||||||||

U_{part} | 1.04 | 1.10 | 0.77 | 0.88 | 1.42 | 0.84 | 1.94 | 1.87 | 0.55 | 1.40 | 0.74 | 1.43 | 1.05 | 1.29 | 0.76 | 1.02 |

U_{dev} | 1.65 | 1.74 | 1.77 | 1.86 | 1.86 | 1.77 | 2.09 | 1.69 | 1.98 | 1.95 | 2.02 | 1.83 | 2.49 | 1.68 | 1.80 | 2.28 |

Cavity 2 | ||||||||||||||||

U_{part} | 1.04 | 1.10 | 0.79 | 0.97 | 1.42 | 0.84 | 1.94 | 1.87 | 1.18 | 1.40 | 1.24 | 1.43 | 1.91 | 1.29 | 0.85 | 1.64 |

U_{dev} | 1.65 | 1.74 | 1.77 | 1.86 | 1.86 | 1.77 | 2.09 | 1.69 | 1.98 | 1.95 | 2.02 | 1.83 | 2.49 | 1.68 | 1.80 | 2.28 |

U_{me}. per Run-Cavity 1 | |||||||||

Tm [°C] | Tmld [°C] | InjSp [mm/s] | PackPr [bar] | ||||||

Position | Unit | 220 | 260 | 40 | 60 | 100 | 140 | 440 | 540 |

PP1 | [μm] | 5.6 | 3.6 | 3.9 | 3.8 | 3.0 | 3.8 | 3.2 | 3.0 |

PP2 | [μm] | 5.7 | 2.2 | 5.1 | 4.2 | 6.2 | 0.8 | 3.5 | 5.7 |

PP3 | [μm] | 16.0 | 1.8 | 13.3 | 13.3 | 17.3 | 3.2 | 12.6 | 14.0 |

RP2 | [μm] | 27.5 | 21.5 | 23.9 | 25.6 | 26.2 | 21.0 | 27.2 | 21.8 |

U_{me}. per Run-Cavity 2 | |||||||||

Tm [°C] | Tmld [°C] | InjSp [mm/s] | PackPr [bar] | ||||||

Position | Unit | 220 | 260 | 40 | 60 | 100 | 140 | 440 | 540 |

PP1 | [μm] | 3.1 | 3.2 | 3.2 | 3.0 | 3.1 | 3.2 | 3.2 | 3.1 |

PP2 | [μm] | 3.7 | 4.3 | 3.9 | 4.1 | 3.9 | 4.2 | 4.0 | 3.9 |

PP3 | [μm] | 3.7 | 3.2 | 3.1 | 3.5 | 3.4 | 3.3 | 3.4 | 3.3 |

RP2 | [μm] | 46.8 | 21.8 | 37.3 | 39.4 | 32.8 | 3.7 | 35.2 | 41.3 |

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## Share and Cite

**MDPI and ACS Style**

Giannekas, N.; Zhang, Y.; Tosello, G. Investigation on Product and Process Fingerprints for Integrated Quality Assurance in Injection Molding of Microstructured Biochips. *J. Manuf. Mater. Process.* **2018**, *2*, 79.
https://doi.org/10.3390/jmmp2040079

**AMA Style**

Giannekas N, Zhang Y, Tosello G. Investigation on Product and Process Fingerprints for Integrated Quality Assurance in Injection Molding of Microstructured Biochips. *Journal of Manufacturing and Materials Processing*. 2018; 2(4):79.
https://doi.org/10.3390/jmmp2040079

**Chicago/Turabian Style**

Giannekas, Nikolaos, Yang Zhang, and Guido Tosello. 2018. "Investigation on Product and Process Fingerprints for Integrated Quality Assurance in Injection Molding of Microstructured Biochips" *Journal of Manufacturing and Materials Processing* 2, no. 4: 79.
https://doi.org/10.3390/jmmp2040079