Stack Architecture Research Articles

A Model for Online Learning in Digital Hardware Design

The report presents a model of a calculator with a stack architecture. It is developed in the hardware description language TL-Verilog. The model is designed for online learning in a development environment for digital hardware design. It is part of a set of models used for training students and doctoral students at Technical University - Gabrovo.

Super learner approach to predict total organic carbon using stacking machine learning models based on well logs

Determining the total organic carbon (TOC) content is essential information for risk assessment in oil exploration, as it is a parameter used for the characterization of hydrocarbon-generating rocks, considering that intervals rich in organic matter are the basic requirements for oil and gas accumulation. However, the determination of TOC can be costly, demanding destructive tests in samples from the source rock, expensive laboratory machinery, and specialized personnel. In this context, one notes the necessity of the computational methods to bypass those problems and that machine learning models emerge as an option. One approach to integrating machine learning methods improves performance and, consequently, the prediction quality is stacking models. This paper presents a super learner strategy, based on stacking approaches, as a surrogate model for TOC modeling. The super-learner has three levels in this structure containing different types of learners (machine learning methods), where two stack models from the first two levels. The following machine learning models were used in the building of super learner the K-Neighbors Nearest (KNN), Linear Regression (LR), Multi-layer Perceptron Neural Network (MLP), Random Forest (RF), Ridge Regression (RR), and Support Vector Regression (SVR). The proposed model was compared with standalone machine learning models and other canonical stacking models. The resulting super learner stacking model attained the best average performance for the TOC modeling (R = 0.897, R2 = 0.80, RMSE = 1.16, MAE = 0.93, and MAPE = 28.30%). The proposed approach produces an alternative data-driven efficient model for TOC prediction, resulting in reliable automated technology to assist oil and gas well management and decision-making.

Understanding Travel Mode Choice Behavior: Influencing Factors Analysis and Prediction with Machine Learning Method

Building a multimode transportation system could effectively reduce traffic congestion and improve travel quality. In many cities, use of public transport and green travel modes is encouraged in order to reduce the emission of greenhouse gas. With the development of the economy and society, travelers’ behaviors become complex. Analyzing the travel mode choices of urban residents is conducive to constructing an effective multimode transportation system. In this paper, we propose a statistical analysis framework to study travelers’ behavior with a large amount of survey data. Then, a stacking machine learning method considering travelers’ behavior is introduced. The results show that electric bikes play a dominant role in Jinan city and age is an important factor impacting travel mode choice. Travelers’ income could impact travel mode choice and rich people prefer to use private cars. Private cars and electric bikes are two main travel modes for commuting, accounting for 30% and 35%, respectively. Moreover, the proposed stacking method achieved 0.83 accuracy, outperforming the traditional multinomial logit (MNL) mode and nine other machine learning methods.

Event-Based Epileptic Seizure Detection with Stacked Convolutional Restricted Boltzmann Machine

Research indicates that around 1% of the global population experiences epileptic seizures, described as an undue neuronal discharge in the brain, which can significantly impact the quality of life for those affected. Children who experience frequent or prolonged seizures may experience lasting cognitive damage and psychological difficulties, and sudden seizures can pose a serious risk of injury or even death. Therefore, developing an accurate machine learning model to predict seizures is crucial. This study introduces an Event-based Stacked Convolutional Restricted Boltzmann Machine architecture that can detect ictal periods in EEG signals. The model was trained and evaluated on a publicly available dataset of EEG recordings from 24 pediatric patients, achieving a high prediction accuracy of 94.2%, with a mean prediction time of 19.62 min. The model's mean sensitivity and specificity rates were also comparable to those of state-of-the-art predictive models on the CHB-MIT dataset.

A novel higher order appearance texture analysis to diagnose lung cancer based on a modified local ternary pattern

Background and ObjectiveLung cancer is an important cause of death and morbidity around the world. Two of the primary computed tomography (CT) imaging markers that can be used to differentiate malignant and benign lung nodules are the inhomogeneity of the nodules’ texture and nodular morphology. The objective of this paper is to present a new model that can capture the inhomogeneity of the detected lung nodules as well as their morphology. MethodsWe modified the local ternary pattern to use three different levels (instead of two) and a new pattern identification algorithm to capture the nodule’s inhomogeneity and morphology in a more accurate and flexible way. This modification aims to address the wide Hounsfield unit value range of the detected nodules which decreases the ability of the traditional local binary/ternary pattern to accurately classify nodules’ inhomogeneity. The cut-off values defining these three levels of the novel technique are estimated empirically from the training data. Subsequently, the extracted imaging markers are fed to a hyper-tuned stacked generalization-based classification architecture to classify the nodules as malignant or benign. The proposed system was evaluated on in vivo datasets of 679 CT scans (364 malignant nodules and 315 benign nodules) from the benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and an external dataset of 100 CT scans (50 malignant and 50 benign). The performance of the classifier was quantitatively assessed using a Leave-one-out cross-validation approach and externally validated using the unseen external dataset based on sensitivity, specificity, and accuracy. ResultsThe overall accuracy of the system is 96.17% with 97.14% sensitivity and 95.33% specificity. The area under the receiver-operating characteristic curve was 0.98, which highlights the robustness of the system. Using the unseen external dataset for validating the system led to consistent results showing the generalization abilities of the proposed approach. Moreover, applying the original local binary/ternary pattern or using other classification structures achieved inferior performance when compared against the proposed approach. ConclusionsThese experimental results demonstrate the feasibility of the proposed model as a novel tool to assist physicians and radiologists for lung nodules’ early assessment based on the new comprehensive imaging markers.

Structural design and mechanical performance verification of a mobile piler with a double-scissor fork

The traditional piler structure is column type. The stacker of this structure needs the same height as the top of the warehouse. At present, there is no good solution for highly inconsistent special-shaped warehouses. This paper proposes a piler based on the requirement of variable height of the piler based on a double shear fork mechanism. The double shear difference mechanism drives the lifting and lowering of the piler. Since there is no need to arrange rails on the top of the warehouse, they can be used in warehouses with different heights. Especially for warehouses with a height of less than 3 m, this new structure of piler has broad application space and application prospects. Finally, according to the actual working force of the piler, the mechanical model is established and verified by FEM. It is proved that the stacking machine with this structure has good mechanical properties.

AI Model Factory: Scaling AI for Industry 4.0 Applications

This demo paper discusses a scalable platform for emerging Data-Driven AI Applications targeted toward predictive maintenance solutions. We propose a common AI software architecture stack for building diverse AI Applications such as Anomaly Detection, Failure Pattern Analysis, Asset Health Forecasting, etc. for more than a 100K industrial assets of similar class. As a part of the AI system demonstration, we have identified the following three key topics for discussion: Scaling model training across multiple assets, Joint execution of multiple AI applications; and Bridge the gap between current open source software tools and the emerging need for AI Applications. To demonstrate the benefits, AI Model Factory has been tested to build the models for various industrial assets such as Wind turbines, Oil wells, etc. The system is deployed on API Hub for demonstration.

Hysteretic behavior simulation based on pyramid neural network: Principle, network architecture, case study and explanation

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to integrate features directly in the output module. In addition, a weighted stacked strategy is proposed to enhance the conventional feature fusion method. Subsequently, the redesigned architectures are compared with other commonly used network architectures. Results show that the redesigned architectures outperform the alternatives in 87.5% of cases. Meanwhile, the long and short-term memory abilities of different basic network architectures are analyzed through a specially designed experiment, which could provide valuable suggestions for network selection.

StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture.

One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues. However, identifying THPs experimentally, using methods such as phage display or in vivo screening, is a complex, time-consuming task, hence the need for computational methods. In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture. With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods. It achieved an accuracy of 0.915 and a 0.831 Matthews Correlation Coefficient (MCC) score on the main dataset, and an accuracy of 0.883 and a 0.767 MCC score on the small dataset. StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs. Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.

A Flight Helmet-Attached Force Gauge for Measuring Isometric Neck Muscle Strength.

INTRODUCTION: Fighter pilots must withstand high Gz-forces that can damage the cervical spine. Strength of the cervical musculature is of vital importance when it comes to preventing these G-induced neck injuries. However, there is very little evidence on valid neck muscle strength measurement methods for fighter pilots. The aim of this study was to examine the validity of a commercial force gauge attached to a pilot's helmet for measuring isometric neck muscle strength.METHODS: A total of 10 subjects performed maximal isometric cervical flexion, extension, and lateral flexion with the helmet-attached gauge and with a weight stack machine, which was used as a reference. Electromyography (EMG) activities were recorded from the right and left sternocleidomastoids and cervical erector spinae muscles during all measurements. Paired t-tests, Pearson correlation coefficient, and Wilcoxon's test were used to analyze the data.RESULTS: Difference of mean force values between the devices was statistically significant in all directions. Pearson correlation coefficient varied between 0.73 and 0.89 and it was highest in cervical flexion. EMG activities were significantly different only in the left CES during flexion.DISCUSSION: The helmet-attached gauge is a valid tool for measuring isometric neck muscle strength and is best used as a means to compare individual differences in strength levels or to track the progress of strength development.Nyländen P, Virmavirta M, Sovelius R, Kyröläinen H, Honkanen T. A flight helmet-attached force gauge for measuring isometric neck muscle strength. Aerosp Med Hum Perform. 2023; 94(6):480-484.

An Optimal Stacked ResNet-BiLSTM-Based Accurate Detection and Classification of Genetic Disorders.

Gene is located inside the nuclease and the genetic data is contained in deoxyribonucleic acid (DNA). A person's gene count ranges from 20,000 to 30,000. Even a minor alteration to the DNA sequence can be harmful if it affects the cell's fundamental functions. As a result, the gene begins to act abnormally. The sorts of genetic abnormalities brought on by mutation include chromosomal disorders, complex disorders, and single-gene disorders. Therefore, a detailed diagnosis method is required. Thus, we proposed an Elephant Herd Optimization-Whale Optimization Algorithm (EHO-WOA) optimized Stacked ResNet-Bidirectional Long Term Short Memory (ResNet-BiLSTM) model for detecting genetic disorders. Here, a hybrid EHO-WOA algorithm is presented to assess the Stacked ResNet-BiLSTM architecture's fitness. The ResNet-BiLSTM design uses the genotype and gene expression phenotype as input data. Furthermore, the proposed method identifies rare genetic disorders such as Angelman Syndrome, Rett Syndrome, and Prader-Willi Syndrome. It demonstrates the effectiveness of the developed model with greater accuracy, recall, specificity, precision, and f1-score. Thus, a wide range of DNA deficiencies including Prader-Willi syndrome, Marfan syndrome, Early Onset Morbid Obesity, Rett syndrome, and Angelman syndrome are predicted accurately.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March–June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Artificial intelligence-based refractive error prediction and EVO-implantable collamer lens power calculation for myopia correction

BackgroundImplantable collamer lens (ICL) has been widely accepted for its excellent visual outcomes for myopia correction. It is a new challenge in phakic IOL power calculation, especially for those with low and moderate myopia. This study aimed to establish a novel stacking machine learning (ML) model for predicting postoperative refraction errors and calculating EVO-ICL lens power.MethodsWe enrolled 2767 eyes of 1678 patients (age: 27.5 ± 6.33 years, 18–54 years) who underwent non-toric (NT)-ICL or toric-ICL (TICL) implantation during 2014 to 2021. The postoperative spherical equivalent (SE) and sphere were predicted using stacking ML models [support vector regression (SVR), LASSO, random forest, and XGBoost] and training based on ocular dimensional parameters from NT-ICL and TICL cases, respectively. The accuracy of the stacking ML models was compared with that of the modified vergence formula (MVF) based on the mean absolute error (MAE), median absolute error (MedAE), and percentages of eyes within ± 0.25, ± 0.50, and ± 0.75 diopters (D) and Bland-Altman analyses. In addition, the recommended spheric lens power was calculated with 0.25 D intervals and targeting emmetropia.ResultsAfter NT-ICL implantation, the random forest model demonstrated the lowest MAE (0.339 D) for predicting SE. Contrarily, the SVR model showed the lowest MAE (0.386 D) for predicting the sphere. After TICL implantation, the XGBoost model showed the lowest MAE for predicting both SE (0.325 D) and sphere (0.308 D). Compared with MVF, ML models had numerically lower values of standard deviation, MAE, and MedAE and comparable percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 0.75 D prediction errors. The difference between MVF and ML models was larger in eyes with low-to-moderate myopia (preoperative SE > − 6.00 D). Our final optimal stacking ML models showed strong agreement between the predictive values of MVF by Bland-Altman plots.ConclusionWith various ocular dimensional parameters, ML models demonstrate comparable accuracy than existing MVF models and potential advantages in low-to-moderate myopia, and thus provide a novel nomogram for postoperative refractive error prediction and lens power calculation.

Time series forecasting based on a novel ensemble‐based network and variational mode decomposition

AbstractStatistical and computational intelligence methods contain weaknesses in handling nonlinearity, non‐stationarity and noise. This research develops a novel decomposition ensemble‐based network named VMD‐DENetwork for time series forecasting over different horizons. A robust decomposition technique called variational mode decomposition (VMD) is applied to decompose the input sequence into several intrinsic modes in a non‐recursive manner. The optimal number of intrinsic modes is selected based on a comprehensive analysis to ensure the stability of the framework. The proposed DENetwork is developed based on stacking architecture and constitutes heterogeneous learners to model the nonlinear and complex relationships. It combines a convolutional neural network, long short‐term memory and an extreme learning machine. A firefly optimization algorithm is adopted for utilizing hyperparameters of the proposed model to enhance the efficiency of VMD‐DENetwork. The forecasting performance is verified by using six real‐world data sets from the New York Mercantile and International Petroleum Exchange. The final obtained results are compared with several peer‐advanced algorithms using the root mean squared error (RMSE), mean absolute error (MAE), Theil inequality coefficient (TIC) and correlation coefficient (R) metrics. The experimental results confirm that the proposed model demonstrates outstanding prediction performance. The employed optimization algorithm is compared with three frequently used bio‐inspired optimization algorithms, and their performance is tested using standard CEC benchmarks.

Decompressor for hardware applications

The use of lossless compression in the application specific computers provides such advantages as minimized amount of memory, increased bandwidth of interfaces, reduced energy consumption, and improved self-testing systems. The article discusses known algorithms of lossless compression with the aim of choosing the most suitable one for implementation in a hardware-software decompressor. Among them, the Lempel-Ziv-Welch (LZW) algorithm makes it possible to perform the associative memory of the decompressor dictionary in the simplest way by using the sequential reading the symbols of the decompressed word. The analysis of the existing hardware implementations of the decompressors showed that the main goal in their development was to increase the bandwidth at the expense of increasing hardware costs and limited functionality. It is proposed to implement the LZW decompressor in a hardware module based on a microprocessor core with a specialized instruction set. For this, a processor core with a stack architecture was selected, which is developed by the authors for the tasks of the file grammar analyzing. Additional memory block for the dictionary storing and an input buffer which converts the byte stream of the packed file into a sequence of unpacked codes are added to it. The processor core instruction set is adjusted to both speed up decompression and reduce hardware costs. The decompressor is described by the Very high-speed integral circuit Hardware Description Language and is implemented in a field programable gate array (FPGA). At a clock frequency of up to two hundred megahertz, the average throughput of the decompressor is more than ten megabytes per second. Because of the hardware and software implementation, an LZW decompressor is developed, which has approximately the same hardware costs as that of the hardware decompressor and has a lower bandwidth at the costs of flexibility, multifunctionality, which is provided by the processor core software. In particular, a decompressor of the Graphic Interchange Format files is implemented on the basis of this device in FPGA for the application of dynamic visualization of patterns on the embedded system display

Quality of Artificial Intelligence Driven Procurement Decision Making and Transactional Data Structure

Purpose: Current data driven decision making development calls for the quality assurance based on quality data structure. The paper analyses transactional data structure used in public procurement in Slovakia and the effect of data structure enhancement on prediction performance as crucial part of artificial intelligence (AI) quality assurance standard. We examine the significance of data structure enhancement and attributes transformation for prediction modelling. Methodology/Approach: The research is based on mutli-step model using stacked ensemble machine learning (ML) algorithm and simulating input space of 211 attributes transformed and aggregated according to different perspectives assessed by r2, mean absolute error (MAE) or mean square error (MSE). Findings: The results show that different performance of variable categories to prediction power. The most significant predictors were in category related to sectoral product classifications and in category related to variables aggregated for supplier, what underline the significance of structured information of all suppliers and negotiation participants in public tenders. Research Limitation/Implication: Methodology is based on big data with high complexity. Due to limited computing power, no subjects’ IDs were used as inputs. The complexity behind data and processes call for more complex simulations of all variables and their mutual interaction and interdependencies. Originality/Value of paper: The paper contributes to data science in transactional data domain and assessed the significance of different variables categories with respect to their specific added value to prediction power.

Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance

The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS.

Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate

In this study, the effects of mixed convection heat transfer from a moving vertical flat plate with an experimental and stacked heterogeneous ensemble learning approach are analyzed. In the experimental work, the effects on both natural and forced convection of dimensionless oscillation amplitude (Ao), dimensionless oscillation frequency (Wo) and Rayleigh number (Ra) are investigated. In the experiments, the vertical movement of the plate is provided by a flywheel-motor assembly. The average Nusselt numbers (Nu) on the fixed plate and the moving plate surface were obtained. Additionally, this study is focused on the prediction of heat transfer of a moving flat plate using single-based algorithms (Gradient Boosting, AdaBoost, Multilayer Per-ceptron) and a stacked heterogeneous ensemble learning model. The statistical per-formance of the single-based algorithms and the stacked ensemble model is meas-ured in the prediction of mixed convection heat transfer. The results show that the stacked-based ensemble learning model yielded the MSE = 2.01, RMSE = 1.42, MAE = 1.1 and R2 = 0.99 values. Overall, this study reveals that the proposed stacked en-semble machine learning model can be used successfully for modeling convection heat transfer of a moving plate.

LinVec: A Stacked Ensemble Machine Learning Architecture for Analysis and Forecasting of Time-Series Data

Objectives: The proposed work integrates multiple Machine Learning approaches in a single model to be used for the analysis and forecasting of Time Series data. Methods: In the present work, the concept of Stacked Ensemble learning is proposed that uses Multiple Linear Regression and Support Vector Regression techniques as the base models. A Meta Model is constructed based on Multiple Linear Regression with necessary modifications. The outputs from the base models are fed into the meta-model which is mended with the capability of combining the predictions from the two base models to produce better results than the individual constituent parts, after running a k-fold training procedure. Findings: The proposed model is capable of analyzing and predicting any Time Series data. In the present study, stock data of six companies enlisted in the National Stock Exchange of India are analyzed for the prediction of the next day’s Open, High, and Low prices. The proposed work achieves better accuracy and reduces the error in prediction when compared to similar works done in the same field. Novelty: The amalgamated technique used in this work can be considered as a generalization of the stacked ensemble method in a broader aspect. The proposed model combines the strengths of multiple Machine Learning methods into a single model to achieve better performance than its individual counterparts. Further, several recent works have tried to predict only the next day’s Open and Closing Prices of stocks, but for an intraday trader, prediction of the next day’s Low and High prices of a stock are more significant than the closing prices. Very few works have predicted all of the Open, High and Low prices in a single study, our present work achieved this quite successfully. Keywords: Machine Learning; Stacked Ensemble Model; Support Vector Regression; Multiple Linear Regression; Time Series data analysis; Stock Price Prediction

Quality Assurance for Flexible Stack Assembly of Lithium‐Ion Cells

The development and scale‐up of lithium‐ion battery (LIB) production for a sustainable energy supply is advancing very rapidly and in versatile directions. Manufacturing processes and production steps are constantly developed and optimized to improve production efficiency. To integrate new machinery into a production line while conforming to the DIN EN ISO 9001 standards of certification, it is necessary to define the procedures for assuring product quality. Herein, a quality assurance concept is designed for an innovative flexible stacking process currently under development. Critical sources of errors are identified by employing the failure process matrix approach. On this basis, it is described how these errors are either avoided by suitable design of the machine components and its control or by which inspection technology is integrated to detect defective intermediate products. In the resulting quality assurance concept, a digital twin of the stacking machine is employed to constantly surveil process and product properties and also to facilitate the change of the produced products on the machine. Evaluating the quality management already in the design phase of the machine saves resources and enables a deeper understanding of the stacking process.