Sie können Operatoren mit Ihrer Suchanfrage kombinieren, um diese noch präziser einzugrenzen. Klicken Sie auf den Suchoperator, um eine Erklärung seiner Funktionsweise anzuzeigen.
Findet Dokumente, in denen beide Begriffe in beliebiger Reihenfolge innerhalb von maximal n Worten zueinander stehen. Empfehlung: Wählen Sie zwischen 15 und 30 als maximale Wortanzahl (z.B. NEAR(hybrid, antrieb, 20)).
Findet Dokumente, in denen der Begriff in Wortvarianten vorkommt, wobei diese VOR, HINTER oder VOR und HINTER dem Suchbegriff anschließen können (z.B., leichtbau*, *leichtbau, *leichtbau*).
Dieser Artikel befasst sich mit der Optimierung lösungsmittelfreier Laminierungsprozesse zur Verbesserung der Haftfestigkeit zwischen Polyamid (PA) und Polyethylen (PE) -Folien, die für hochbarrierearme Polymerstrukturen in flexiblen Verpackungen von entscheidender Bedeutung sind. Die Studie nutzt die Taguchi-Methode, ein Design of Experiments (DOE) -Verfahren, um wichtige Betriebsparameter wie Oberflächenenergie, Maschinengeschwindigkeit und Anwendungstemperatur systematisch zu bewerten. Mithilfe eines orthogonalen Arrays L18 identifiziert die Forschung die Oberflächenenergie als den einflussreichsten Faktor, der für über 70% der Varianz der Haftfestigkeit verantwortlich ist. Zusätzlich werden die Rollen der Maschinengeschwindigkeit und der Anwendungstemperatur hervorgehoben, was erheblich zur Klebeleistung beiträgt. Der Artikel vergleicht zudem lineare und quadratische Regressionsmodelle und zeigt die Vorhersagekraft dieser Modelle bei der Prozessoptimierung auf. Die optimierten Parameter ergeben eine vorhergesagte Haftfestigkeit von 646,94 N, die den experimentellen Ergebnissen sehr nahe kommt, was die Effizienz und Kosteneffizienz des Taguchi-Ansatzes unterstreicht. Diese Studie liefert wertvolle Erkenntnisse über die Faktoren, die die Haftung bei lösungsmittelfreien Laminierungen beeinflussen und bietet einen Rahmen zur Verbesserung der Produktqualität in industriellen Umgebungen.
KI-Generiert
Diese Zusammenfassung des Fachinhalts wurde mit Hilfe von KI generiert.
Abstract
The increasing demand for environmentally sustainable and high-performance flexible packaging has accelerated the adoption of solvent-free lamination processes, particularly for multi-layered films such as polyethylene and polyamide bonded with polyurethane adhesives. Achieving optimal adhesion strength (AS) in solvent-free lamination remains challenging due to the complex interplay of processing parameters. This study employs Taguchi’s design of experiments (DOE) methodology to statistically optimize eight key parameters influencing AS, including application temperature, curing temperature, coating weight, machine speed, rewind tension, taper tension, surface energy, and mix ratio. An L18 orthogonal array was used to reduce experimental runs from 6,561 (full factorial design) to 18 while maintaining balanced parameter representation. Signal-to-noise (S/N) ratio analysis identified surface energy as the most influential factor, followed by machine speed and application temperature. ANOVA confirmed the statistical significance of surface energy (P = 0.047), accounting for 70.25% of the total variance in AS. Linear and quadratic regression models were developed to validate predictive accuracy, yielding R² values of 85.75% and 96.53%, respectively. A confirmation test under the optimized conditions predicted an AS of 646.94 N, closely matching the experimental value of 642 N with an error margin of 0.76%. The results demonstrate the effectiveness of Taguchi-based optimization and regression modeling in improving adhesion performance while minimizing experimental effort in SF lamination systems.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
1 Introduction
The increasing demand for high-barrier polymer structures in the flexible packaging industry has driven organizations to optimize processing conditions, focusing on advanced materials and technologies to enhance barrier performance [1‐3]. Modern packaging polymers are required to deliver effective barriers against oxygen and light, possess heat sealability, and maintain controlled water vapor permeability—functionalities that often necessitate multilayer or composite structures due to the limitations of single polymer [1, 2]. These attributes are critical to prolong the shelf life of packaging materials and prevent product loss after lamination, as evidenced by recent advancements in active and biodegradable packaging solutions that enhance barrier properties and maintain product quality [4‐7]. Lamination is therefore widely used to combine different polymer films and achieve the desired functionality.
These material properties not only influence product integrity but also determine the choice of lamination method applied during packaging. Flexible packaging commonly employs three types of adhesive lamination: solvent-based, water-based, and solvent-free. Among these, solvent-free lamination has gained prominence in recent years due to its elimination of solvent emissions during processing, aligning with environmental and safety objectives [8]. Given the increasing demand for high-strength, high-performance laminated polymers, optimizing the solvent-free lamination process is essential to reduce process variation and enhance productivity. Recent advancements in adhesive chemistry, equipment design, and process control have significantly improved the efficiency and sustainability of solvent-free lamination, making it a preferred choice in flexible packaging applications [9].
Anzeige
Bonding polyamide (PA) to polyethylene (PE) remains intrinsically challenging because the high polarity of PA and the low surface energy of PE limit interfacial wetting and specific interactions. Recent PA–PE multilayer studies have shown that surface activation of PE (e.g., plasma-based treatments) can introduce polar functionalities and substantially increase peel/joint strength, highlighting the central role of surface chemistry and interfacial mechanisms in PA–PE adhesion [10, 11]. Related packaging-oriented investigations in solventless (solvent-free) lamination similarly report that achieving robust bonding with two-component polyurethane systems depends strongly on substrate surface energy (particularly its polar component), reinforcing surface energy control as a primary driver of laminate strength [12]. Against this background, the present work contributes a process-level optimization for an industrial solvent-free PA–PE lamination line, quantifying how key operational parameters collectively influence adhesion strength and identifying a robust parameter window for high-strength laminates.
Design of experiments (DOE) techniques, particularly the Taguchi method, offer a systematic approach to multivariate optimization. The Taguchi DOE approach is efficient in reducing time and cost, and it minimizes the sensitivity of output variables to uncontrolled noise factors [13, 14]. The Taguchi orthogonal array (OA) has been widely applied to optimize production processes across various industries. For example, Ayyildiz et al. [13] used a Taguchi L18 array to optimize surface roughness in drilling medium-density fiberboard. In that study, two parameters at three levels and one parameter at two levels were investigated (18 experiments total). Both linear and quadratic regression models were developed, and the feed rate was found to be the most significant factor (50.10% contribution) [13].
Akgün and Kara [13] applied a Taguchi OA to study the effect of tool coating parameters on the turning of AA 6061 alloy. They investigated three parameters at three levels and one parameter at two levels using a ‘smaller-is-better’ criterion. Their results demonstrated the effectiveness of the Taguchi method in optimizing the process [14]. Kara et al. [15] examined the effect of process parameters and their performance on grinding of cryogenically treated AISI 5140 steel process. They used a Taguchi based L18 OA and developed linear and quadratic regression models, both achieving R2 above 95%. They found that optimal conditions were achieved for specimens cryogenically treated for 30 h. Similarly, Ozbek et al. [16] investigated cutting parameters in the turning of AISI P20 die steel using a Taguchi L18 design, and identified feed rate as the most statistically significant factor [16].
Although regression models are frequently reported alongside Taguchi-based optimization studies, reliance on the coefficient of determination (R²) alone is insufficient to establish statistical relevance. In manufacturing and materials processing applications, regression results must be interpreted in conjunction with ANOVA, parameter-level p-values, and confidence levels, particularly when models are derived from screening-type orthogonal array designs where interaction effects are not fully resolved. Consequently, in Taguchi-based studies, regression analysis should be viewed as a complementary predictive tool, while factor significance and process robustness are primarily established through S/N ratio analysis and ANOVA-based contribution assessment, as commonly observed in adhesion and composite manufacturing processes [17].
Anzeige
Recent studies have applied the Taguchi method to optimize surface roughness in polymer processing. For instance, Maidin et al. [18] utilized a Taguchi DOE approach to optimize surface roughness in Fused Deposition Modeling (FDM) of ABS polymers, identifying flow rate and layer height as significant factors [18]. Similarly, Barragán-Trinidad et al. [19] applied the Taguchi method to optimize the photo-Fenton process for wastewater treatment, achieving significant improvements in chemical oxygen demand (COD) removal [19]. Research showed the potential of predictive modeling in optimizing process outputs under varied parameter settings, as demonstrated in similar applications involving dry-jet wet spinning processes [20].
In summary, the Taguchi method has proven effective for optimizing a variety of manufacturing processes. However, its application to solvent-free film lamination processes has not been extensively reported. This study applies a Taguchi L18 DOE combined with Analysis of variance (ANOVA) and regression modeling to optimize the solvent-free lamination process parameters to enhance the adhesion strength (AS) between PA and PE films.
2 Materials and methods
2.1 Materials
The solvent-free lamination process was applied to PA and PE films. The PA film had a constant surface energy of 48 dyne/cm, while the PE film’s surface energy was varied at three different levels (38 and 42 dyne/cm) [21]. Lamination between the two films was facilitated by applying a solvent-free two-component adhesive system (polyurethane resin + hardener) using an application roller. This Polyurethane-based solvent-free adhesive is commonly used in flexible packaging due to their strong bonding characteristics and environmental compliance [22]. Table 1 summarizes the key physical and chemical properties of the polyamide, polyethylene, and polyurethane adhesive system relevant to solvent-free lamination and adhesion performance.
Table 1
The physical and chemical properties of the used materials
Material
Property
Typical value
Unit
PA film
Density
1.12–1.15
g/cm³
Melting temperature
210–220
°C
Surface energy (after treatment)
~ 48
dyne/cm
PE film
Density
0.92–0.94
g/cm³
(LLDPE/LDPE blend)
Melting temperature
110–125
°C
Surface energy (after treatment)
40–44
dyne/cm
Polyurethane adhesive
Viscosity (25 °C)
2–5
Pa·s
(resin + hardener)
Recommended mix ratio
80:20
wt%
Bonding of the PA and PE films was performed using a duplex SL 450/600 HD laminating machine. The effect of process parameters on the AS between the bonded films was investigated. Figure 1 illustrates the configuration of the materials in the lamination process.
Fig. 1
Schematic of the material configuration for lamination
Figure 2 provides a schematic illustration of the PA and PE unwind stations, corona treatment of the PE film, solvent-free adhesive application, lamination nip, and rewind unit, with key operating parameters indicated.
Fig. 2
Schematic representation of the solvent-free lamination process and key process parameters investigated in this study
The PE film was prepared in-house using a 50:50 blend of linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE) resins (supplier: Sasol Pty Ltd). Prior to lamination, the PE surface was corona-treated to achieve a minimum surface energy of 40 dyne/cm. The PA film was supplied by Kolon (Kyungnam), and had a surface energy of 48 dyne/cm after treatment.
To ensure optimal lamination conditions, both films were stored at a minimum temperature of 20 °C and were wrapped in plastic film to prevent contamination and moisture uptake. The PA film was further stored in wooden boxes with silica gel to limit moisture absorption. Both PE and PA films were used within one week of corona treatment to prevent surface energy decay and additive migration. The polyurethane resin and hardener were procured from Morchem Ltd.
2.2 Experimental procedure
Before running experiments, the effectiveness of the corona treatment was verified by assessing the surface energy of the treated films using the wetting tension method with ethyl cellosolve and formamide, following ASTM D2578 standard procedures [23].
Surface energy measurements were performed immediately after corona treatment and prior to lamination to confirm treatment effectiveness and minimize the influence of surface energy decay. Only films achieving a minimum surface energy of 40 dyne/cm were used for lamination experiments, and surface energy was periodically rechecked during the experimental campaign to ensure consistency.
The laminating system was then loaded with the PE and PA films (with PE on unwind 2 and PA on unwind 1 of the laminator). To ensure consistent adhesive application, the gap between the adhesive application rollers was set using a gauge dial indicator.
The AS measurement tests were conducted using tensile testing machine (Instron 5969 system) in accordance with ISO 527-3 procedures for plastic films [24]. A laminated PA-PE specimen (10 × 10 mm) was cured and mounted with the PA side placed on the upper gripper and the PE side on the lower gripper, as illustrated in Fig. 3.
Fig. 3
Shows PA and PE material placed on the tensile testing machine grips
All AS measurements were taken after a 48-hour curing period to allow the polyurethane adhesive to fully react. All experiments were performed in three replications to ensure reliability. For each run, the measured AS values were averaged and converted to a signal-to-noise (S/N) ratio (using the ‘larger-is-better’ criterion). The response variables (AS, tensile strength, and S/N ratio) were calculated using Minitab 17 statistical software.
The selection of parameter levels was grounded in both industrial practice and material behavior under solvent-free lamination conditions. Application temperature (AV) and Curing Temperature (CT) were chosen to span the range where polyurethane adhesives exhibit optimal viscosity and curing characteristics, ensuring uniform application and strong intermolecular bonding. Given PU’s non-Newtonian and moisture-sensitive nature, AV values between 35 °C and 55 °C and a CT above 15 °C were selected to avoid premature curing or delamination.
Machine Speed (MS) levels (150, 180 and 200 m/min) reflect practical operating conditions in commercial laminating units and help assess the influence of dynamic processing speed on bond uniformity. For Rewind Tension (RT) and Taper Tension (TT), ranges were selected to control substrate elasticity and compensate for the inherently low green strength of solvent-free adhesives. These tensions directly influence the integrity of the laminate during and after application.
Coating Weight (CW) levels (1.5 to 2.5 gsm) were chosen based on standard adhesive distribution ranges to evaluate the trade-off between sufficient wetting and excessive build-up. Collectively, these parameters and their levels are representative of realistic manufacturing conditions and are expected to provide a meaningful understanding of their individual and combined effects on AS.
Finally, Mix Ratio (MR) between the PU adhesive and its hardener (80/20) was chosen based on supplier specifications to ensure optimal chemical reactivity and bond strength. Deviations from this ratio cause degradation in adhesive properties and increased variation in AS.
Three levels were chosen for most parameters to capture non-linear effects in the lamination process except for CT, which was assigned two levels. The eight process parameters and their levels are listed in Table 2. The L18 (37 × 21) OA require only 18 runs to cover all parameter combinations in a balanced manner.
Table 2
Process parameters and their levels used in the L18 Taguchi design
Parameter
Levels
Units
1
2
3
Curing temperature (CT)
28
32
-
oC
Rewind tension (RT)
80
100
120
N
Taper tension (TT)
15
25
35
%
Surface energy (SE)
40
42
44
dynes/cm
Coating weight (CW)
1.5
2.0
2.5
gsm
Machine speed (MS)
150
180
200
m/min
Application temperature (AV)
35
45
55
oC
Mix ratio (MR)
75
85
95
%
2.3 Statistical analysis
ANOVA was used to quantify the effect and percentage contribution of each input variable on the output response (AS). All statistical analyses were performed at a 95% confidence level with a significance threshold of α = 0.05, consistent with standard practice in manufacturing process optimization studies. Minitab 17 software was used to perform the ANOVA and to develop predictive regression equations.
The total sum of squares (SST) of the response is given by:
Taguchi method developed three ways of categorising quality, namely: the lower the better, the nominal the better, and the higher the better [17]. For the purpose of this study, higher the better was used since a high bond strength is required between the two films.
Where yi is the experimental response variable, and n is the number of tests in the trial.
The ANOVA partitions this total variance into contributions from each parameter and from error. Regression models (linear and quadratic) were fitted to the experimental data to enable prediction of AS as a function of the process parameters.
3 Results and discussion
The effect of each parameter on AS was evaluated by analyzing the S/N ratios of the AS responses. For each run, AS was measured using the tensile testing machine. The runs, and the corresponding AS values and S/N ratios are presented in the L18 OA in Table 3. Each experiment was replicated three times to ensure the reliability of the results.
Table 3
L18 OA, measured AS and S/N ratios
Run
CT
RT
TT
SE
CW
MS
AV
MR
AS (N)
S/N
AS
1
1
1
1
1
1
1
1
1
350
50.88
2
1
1
2
2
2
2
2
2
490
53.80
3
1
1
3
3
3
3
3
3
355
51.00
4
1
2
1
1
2
2
3
3
370
51.36
5
1
2
2
2
3
3
1
1
460
53.26
6
1
2
3
3
1
1
2
2
640
56.12
7
1
3
1
2
1
3
2
3
450
53.06
8
1
3
2
3
2
1
3
1
550
54.81
9
1
3
3
1
3
2
1
2
300
49.54
10
2
1
1
3
3
2
2
1
565
55.04
11
2
1
2
1
1
3
3
2
300
49.54
12
2
1
3
2
2
1
1
3
445
52.97
13
2
2
1
2
3
1
3
1
440
52.87
14
2
2
2
3
1
2
1
2
590
55.42
15
2
2
3
1
2
3
2
3
300
49.54
16
2
3
1
3
2
3
1
1
560
54.96
17
2
3
2
1
3
1
2
2
380
51.60
18
2
3
3
2
1
2
3
3
450
53.06
The S/N ratio analysis indicated that SE has a directly proportional relationship with AS. The maximum AS observed was 640 N (in run 6, SE = 44 dyne/cm) corresponding to S/N ratio of 56.12. The minimum AS observed was 300 N with the lowest S/N ratio of 49.54, recorded in runs 9, 11, and 15, despite having the same SE = 44 dyne/cm, indicating the influence of interactions with other parameters. Figure 4 illustrates the main effects of each parameter on the S/N ratio. It clearly shows that SE has the most significant and positive influence on AS, followed by MS and AV.
Fig. 4
Main effects of process parameters on S/N ratio for AS
The S/N analysis results are summarized in Table 4 Higher S/N values correspond to better performance. The “Delta” row (range of means) and “Rank” indicate the relative importance of each parameter. SE has the largest range (Delta = 4.15) and is ranked 1 st, confirming its dominant influence on AS. MS and AV followed in second and third place, respectively.
Table 4
Mean S/N ratios for AS at each parameter level (larger-is-better criterion)
Level
CT
RT
TT
SE
CW
MS
AV
MR
1
52.65
52.21
53.03
50.41
53.02
53.21
52.84
52.77
2
52.78
53.10
53.07
53.17
52.91
53.04
53.20
52.81
3
52.84
52.04
54.56
52.22
51.90
52.11
52.57
Delta
0.13
0.89
1.03
4.15
0.80
1.31
1.09
0.24
Rank
8
5
4
1
6
2
3
7
Based on the S/N ratio analysis presented in Table 3, the optimum operating conditions for achieving high AS are: CT = 32 °C (level 2), RT = 100 N (level 2), TT = 25% (level 2), SE = 44 dyne/cm (level 3), CW = 1.5 gsm (level 1), MS = 150 m/min (level 1), AV = 45 °C (level 2), and MR = 85% (level 2).
Since this optimal parameter combination was not included among the original experimental runs, a confirmation test was conducted. The predicted adhesion strength under the optimal condition (ASopt) was estimated using the Taguchi additive prediction model (Eq. 6), in which the contribution of each factor at its optimal level is added relative to the overall mean (ATAS).
In (Eq. 6), the subscripted terms (e.g., CT₂, SE₃) denote the mean AS values corresponding to the selected optimal level of each factor. ANOVA was performed to quantify the contribution of each parameter to the variation in AS. Results are shown in Table 5.
Table 5
ANOVA for AS
Source
Degree of Freedom
Sum of squares
Mean of squares
F
P
Contribution
(%)
CT
1
235
234.7
0.07
0.814
0.12
RT
2
7408
3704.2
1.12
0.471
3.89
TT
2
7758
3879.2
1.18
0.459
4.08
SE
2
133,525
66762.5
20.27
0.047
70.25
CW
2
7158
3579.2
1.09
0.479
3.76
MS
2
14,533
7266.7
2.21
0.312
7.64
AV
2
11,200
5600.0
1.70
0.370
5.89
MR
2
1658
829.2
0.25
0.799
0.87
Residual Error
2
6586
3293.1
3.46
Total
17
190,062
100
As shown in Table 5, SE is the only parameter that is statistically significant at the 95% confidence level (p < 0.05), indicating its dominant influence on adhesion strength. This result is consistent with the critical role of surface activation in promoting interfacial bonding in PA–PE lamination. The remaining parameters, while contributing to AS variation, did not reach statistical significance within the investigated ranges, which can be attributed to the screening nature of the Taguchi L18 design, limited degrees of freedom, and the relatively narrow industrial operating windows considered. The overall ANOVA model was statistically significant (p < 0.05), confirming its adequacy for describing the observed response variation. The residual error was low (3.46%), indicating that the model captured most of the variability in AS. These findings were consistent with the results of the S/N analysis.
To further examine the relationship between AS and the lamination process parameters, regression models were developed based on the experimental data. The linear regression model (Eq. 7), which includes only first-order terms, explained 73.09% of the variance in AS (adjusted R² = 73.09%). The quadratic regression model (Eq. 8), incorporating second-order (squared) terms to account for non-linear effects, achieved a higher predictive accuracy with an adjusted R² of 96.53%.
Both regression models were constructed using least-squares fitting and are presented to assess trend behavior and predictive capability within the experimental domain. These models are used as complementary analytical tools and do not replace the Taguchi-based optimization and ANOVA results, which remain the primary basis for parameter ranking and robustness assessment.
A confirmation test was carried out since the optimum parameters obtained were not part of the Taguchi OA. Remarkably, the optimal operating parameters were achieved with a significantly fewer number of experiments (18 experiments) than in a traditional full factorial design (FFD), where a high number of experiments (37 × 21 = 4374) would have been necessary to consider all possible levels and combinations of parameters.
A confirmation test was carried out under the optimal conditions identified by the Taguchi analysis. The Taguchi-based predicted AS under these conditions was 646.94 N, very close to the experimental result of 642 N (0.76% error).
Table 6 presents a comparison between experimental and predicted AS values for two key cases: run 6 and the optimum condition. Values from the Taguchi model, linear regression, and quadratic regression are shown.
Table 6
Experimental and predicted AS under optimal conditions
Taguchi method
Linear regression method
Quadratic regression
experimental
pred.
error %
pred.
error %
pred.
error %
Run 6
640
613.05
4.21
580.38
9.32
644.86
0.76
Optimum
642
646.94
0.76
608.02
5.29
697.10
8.58
The indicate that the Taguchi additive formulation provided close agreement between the estimated and experimentally measured adhesion strength at the optimal parameter combination, with a deviation of only 0.76% in the confirmation test. This small deviation confirms the validity of the selected optimal settings rather than serving as a predictive accuracy metric. The quadratic regression model demonstrated improved predictive performance compared with the linear model; however, its prediction at the optimal condition exhibited a higher deviation (8.58%). The linear regression model showed larger discrepancies in both cases. These results highlight the complementary roles of Taguchi-based confirmation testing and regression analysis in process optimization, where Taguchi is used for factor ranking and robustness assessment, and regression models are used for predictive analysis within the experimental domain [16].
Importantly, the Taguchi OA enabled determination of the optimal parameter set with only 18 experimental runs. In contrast, a full factorial design considering all parameters combinations would have been prohibitively expensive and time-consuming. This highlights the efficiency and cost-effectiveness of the Taguchi approach in optimizing complex lamination processes.
4 Conclusion
This study successfully applied Taguchi’s design of experiments and regression analysis modeling to statistically optimize key processing parameters in a solvent-free lamination system using polyurethane adhesives for flexible packaging applications. Through an L18 OA, eight critical parameters, including application temperature, curing temperature, coating weight, machine speed, rewind tension, taper tension, surface energy, and mix ratio were efficiently evaluated with only 18 experimental runs.
SE was found to be the most influential factor affecting AS, accounting for more than 70% of the total variance as confirmed by ANOVA. Though, the roles of the other parameters, MS and AV, which contributed 7.64% and 5.89% respectively, should not be overlooked. Their combined influence of 13.53% underscores the importance of fine-tuning secondary parameters to ensure consistent and reliable bonding performance in industrial environments.
Regression modeling further validated the process, with the quadratic model achieving an R² of 96.53%. The optimized parameter combination yielded a predicted AS of 646.94 N, closely matching the experimental value of 642 N, with a minimal error margin of 0.76%.
These results provide valuable insights into the factors affecting adhesion in solvent-free lamination and demonstrate how process parameters can be tuned to improve performance. The optimized parameters can be directly used to set up laminating units for improved product quality. Furthermore, this optimization framework can be readily extended to other lamination and coating systems involving multi-material bonding, particularly where process efficiency, material performance, and sustainability are critical.
Declarations
Competing interests
The authors have no relevant financial or non-financial interests to disclose.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yue S, Zhang T, Wang S, Han D, Huang S, Xiao M, Meng Y (2024) Recent progress of biodegradable polymer package materials: nanotechnology improving both oxygen and water vapor barrier performance. Nanomaterials 14(4):338. https://doi.org/10.3390/nano14040338CrossRef
2.
Deshmukh RK, Tripathi S, Bisht S, Kumar P, Patil T, Gaikwad KK (2025) Mucilage-based composites films and coatings for food packaging application: A review. Int J Biol Macromol 260:140276. https://doi.org/10.1016/j.ijbiomac.2025.140276CrossRef
3.
Chiang H-C, Iverson ET, Schmieg K, Stevens DL, Grunlan JC (2023) Highly moisture resistant super gas barrier polyelectrolyte complex thin film. J Appl Polym Sci 140(4):57163. https://doi.org/10.1002/app.57163CrossRef
4.
Tamarindo S, Pastore C (2016) Packaging film impact on food organoleptic properties: an experimental study. J Appl Packag Res 8(2):78–87
5.
Poulose S, Jonkkari L, Hedenqvist MS, Kuusipalo J (2021) Bioplastic films with unusually good oxygen barrier properties based on potato fruit-juice. RSC Adv 11(20):12543–12548CrossRef
Fernandez-Menez T, García-López D, Argüelles A, Fernández A, Viña J (2021) Shelf life of fresh sliced sea bream packed in PET nanocomposite trays. Foods 10(12):2974. https://doi.org/10.3390/foods10122974CrossRef
Cook M (1996) Reduction of solvents in adhesives. Pigment Resin Technol 5(1):12–16CrossRef
10.
Repeta V, Kukura Y, Shibanov V, Myklushka I, Kukura V (2020) Influence of properties of materials for solventless lamination on the bonding strength of multilayer packaging. J Print Media Technol Res 9(3):177–184. https://doi.org/10.14622/JPMTR-2001CrossRef
11.
Štěpánová V, Šrámková P, Sihelník S, Zemánek M, Jurmanová J, Stupavská M, Kováčik D (2023) Adhesion improvement on the inner side of LLDPE/PA tubular film exposed to DCSBD roll-to-roll plasma system from the outer side of the film. Plasma Process Polym 20(5):e202200226. https://doi.org/10.1002/ppap.202200226CrossRef
Ayyildiz EA, Ayyildiz M, Kara F (2021) Optimization of surface roughness in drilling medium-density fibreboard with a parallel robot. Adv Mater Sci Eng 2021:6612345. https://doi.org/10.1155/2021/6612345CrossRef
14.
Akgun M, Kara F (2021) Analysis and optimization of cutting tool coating effects on the surface roughness and cutting force on turning of AA 6061 alloy. Adv Mater Sci Eng 2021:6672345. https://doi.org/10.1155/2021/6672345CrossRef
15.
Kara F, Kaklu U, Kabasakaloglu U (2020) Taguchi optimization of surface roughness in grinding of cryogenically treated AISI 5140. Mater Test 62(10):1041–1047. https://doi.org/10.3139/120.111456CrossRef
16.
Ozbek NA, Ozbek O, Kara F (2021) Statistical analysis of the effect of the cutting tool coating type on sustainable machining parameters. J Mater Eng Perform 30(10):7783–7795. https://doi.org/10.1007/s11665-021-05709-4CrossRef
Maidin S, Fadani I, Nor Hayati NM, Albaluooshi H (2022) Application of Taguchi method to optimize fused deposition modeling process parameters for surface roughness. J Teknol 84(6):29–37. https://doi.org/10.11113/jurnalteknologi.v84.18621CrossRef
19.
Barragán-Trinidad M, Guadarrama-Pérez O, Guillén-Garcés RA, Bustos-Terrones V, Trevino-Quintanilla LG, Moeller-Chávez G (2023) The grey–Taguchi method, a statistical tool to optimize the photo-Fenton process: a review. Water 15(15):2685. https://doi.org/10.3390/w15152685CrossRef
ASTM D2578-17, Standard Test Method for Wetting Tension of Polyethylene and Polypropylene Films, International ASTM (2017) West Conshohocken, PA, USA, [Online]. Available: https://www.astm.org/d2578-17.html
Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.
