32 research outputs found
Second Hankel determinant for the class of analytic functions defined by new differential operator
No full textDigital-Twin Data for Large-Scale 3D Printing
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MTRL_DATASET - Material Analysis Tools
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Overview
This project contains a dataset from research on a data-informed digital twin for large-scale 3D printing. The data was collected through a series of experiments using two machines: (i) a Kuka KR50R2500 industrial robot and (ii) a MAI® MULTIMIX-3D mortar mixing pump. Additionally, measurements of the printed object's width were recorded during the experiments.
The dataset has been used to explore correlations between machine performance, material behavior, and the final printed structure. The accompanying codebase enables replication of the study’s results, offering tools for data processing, clustering, visualization, and the analysis of various material properties and printing parameters.
The experiments in this study were conducted in two phases:
Correlation Analysis: The first phase focused on exploring the relationships between machine performance, material behavior, and the resulting printed object. Data from these experiments was then used to develop a clustering-based prediction model and a set of feedback control services to automate the operation of the Kuka robot and the mortar mixing pump.
Evaluation of Feedback Control: In the second phase, the feedback control services were implemented in a new set of experiments to assess their effectiveness in optimizing the 3D printing process.
Project Structure
MTRL_DATASET/
│
├── Data/
│ ├── Block_Tests/
│ │ ├── BlockTasks.json - Tasks data for block (evaluation ) tests
│ │ └── BlockMeasurements.json - Width measurements for different block types
│ │
│ └── Mixture_Experiments/
│ ├── PumpResponse_Clean.json - Clean pump response data for mixture experiments (used for clustering)
│ ├── PumpTasks.json - Task data for pump operations
│ └── WidthCorrelationTests.json - Data for width correlation tests
│
├── Clusters/ - Directory for saved ML models
│ ├── kmeans.pkl - Trained KMeans model
│ └── scaler.pkl - Fitted StandardScaler
|
├── Figs/ -Directory of all plots from the code
|
├── Blocks.py - Block data analysis and visualization
├── BlockMeasurements.py - Analysis of block measurement data
├── Clusters.py - Machine learning clustering of pump data
├── PumpData.py - Pump data analysis utilities
├── WidthCorrelations.py - Analysis of correlations between printed object width, Kuka robot velocity and pump reqeuncy
└── README.md - This file
Data Description
01_Mixture Experiments Data
The experiments in this section were done with 4 types of mixtures as follows:
Mix
Clay (kg)
Sand (kg)
Water (kg)
Flow Table (mm)
Density (g/L)
M1
5.00
7.50
3.00
169
2080
M2
5.00
7.50
2.75
163
2100
M3
5.00
7.50
2.50
147
2140
M4
5.00
7.50
2.25
127
2196
PumpResponse_Clean.json
Contains cleaned pump response data including:
Pump output power and current measurements
Mortar temperature readings
Pump pressure readings
Temporal data for pump operations
PumpTasks.json
Contains task information related to pump operations:
Task details for mixture experiments
Timing information for pump actions
Operational parameters and settings
WidthCorrelationTests.json
Contains test data for analyzing correlations between:
Width measurements
Pump frequency
Robot velocity
02_Block Tests Data
The experiments in this section were conducted on 3 block types with the following features:
Block
Velocity (m/s)
Frequency (Hz)
B1
0.11
17
B2
Adaptive
17
B3
0.11
Adaptive
BlockMeasurements.json
Contains width measurements (in mm) for three different blocks. This data is used for comparing width consistency across different block printing strategies.
BlockTasks.json
Contains detailed task data from the pump and the robot for block printing processes, including:
Task IDs and names
Task types (ReadData, Read, SetValue)
Actors (KukaPassiveRead, MaiPrinter)
Timestamps for start and end times
Job identification (e.g., "BLOCK1")
Processing levels and indices
The file contains over 2300 task entries.
03_Features
Within both the BlockTasks.json and PumpTask.json files, every data point adheres to a standardized Task Data Schema that includes the following fields:
_id: A unique identifier for this record.
task_id: A unique identifier for the specific task or operation.
name: The name assigned to the task or process.
type: The category or type of operation (e.g., a data reading action).
main_actor: The primary machine or module responsible for carrying out the task.
description: A brief textual description of the task.
message: A log message or status note associated with the operation.
element_id: A list of identifiers for related design elements (if any).
actors_data: Contains information for the actors involved in the task (nested details are omitted).
job: The job or process identifier associated with this record.
level: The hierarchical level or depth in a process, indicating its relative position.
index: A numerical order or position indicator for the task.
start_time: The timestamp marking the start of the task or operation.
end_time: The timestamp marking when the task was completed.
response: Contains the outcome or data returned by the task (nested details are omitted).
versions: Version control or metadata information regarding this record.
project: The identifier for the project to which this record belongs.
author: The user or entity that created or is responsible for this record.
04_Scripts
PumpData.py
Analyzing and visualising pump tasks data for the 4 mixtures.
WidthCorrelations.py
Analyzes and visualises correlations between width measurements, robot velocity, and pump frequency.
Clusters.py
Implements machine learning clustering on pump data and visualises the results.
Blocks.py
Analyzes task data for different block types, calculates timing metrics, and visualises the results.
BlockMeasurements.py
Analyzes and visualises block width measurement data.
05_Usage
Dependencies
This project requires the following Python packages:
pandas
numpy
matplotlib
scikit-learn
joblib
Install dependencies with:
pip install pandas numpy matplotlib scikit-learn joblib
Run the code
To generate the full analysis and plots from the dataset, simply execute the main.py file. Open your terminal, navigate to the code directory, and run:
cd path/to/your/code/directory
python main.py
For a more detailed overview of available parameters, run each of the individual Python files separately.
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Majorization for certain classes of meromorphic functions associated with a family of integral operator
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Uji Aktivitas Antikoagulan Pada Sel Darah Manusia Dari Ekstrak Alga Coklat Turbinaria Ornata
No full textPenelitian inidilakukan untuk menentukan apakah sepsis Turbinariaornata berpotensi sebagai antikoagulasi atau koagulasi. penelitian inimenggunakan darah manusia yang diasumsikan tidak memiliki gangguan koagulasidarah. Pengujian dilakukan pada darah manusia golongan darah B, O, AB dan A dan masing masing dengan 5 kali perlakuan. Perlakuanyang pertama dilakukan pada darah sebagai kontrol; Perlakuan ke dua padadarah yang diberi ekstrak Turbinaria ornata; perlakuan ke tiga pada darah yang diberi perlakuan EDTA danekstrak Turbinaria ornata; perlakuan ke empat pada darah yang diberikan EDTA; perlakuanke lima pada darah yang diberikanetanol PA. Pada perlakuan pertamamenunjukkan terjadi pembekuan darahpada kategori pembekuan darah normal yaitu antara 8-13 menit. Pada perlakuan ke dua,darah yang diberi ekstrak basah dan kering Turbinariaornata terlihat membeku antara 8-10 menit. Pada perlakuanke tiga, darah yang diberi EDTA danekstrak basah dan kering Turbinariaornata terlihat tidak membeku. Pada perlakuan keempagt, darah yang diberi EDTA juga tidak membeku. Padaperlakuan ke lima yaitu darah yang diberi etanol PA, terlihat darah membeku pada menit ke 3-4.Sehingga dari pengujian di laboratorium secara in vitro, ekstrak basah dankering Turbinaria ornata tidakmemiliki aktivitas antikoagulasi, tetapi memiliki sifat koagulasi yaitupembekuan darah
Green synthesis of Ag nanoparticles for water treatment (antimicrobial on Eschirichia coli)
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PENENTUAN KANDUNGAN PIGMEN KAROTENOID PADA KEPITING Grapsus albolineatus (Lamarck) BETINA DARI PERAIRAN PESISIR PANTAI DESA TANAWANGKO
No full textGrapsus albolineatus (Lamarck) is one of the species of blackish-green crab found above or below the coastal rocks. At has long legs and no swimming legs and has a small claw. Purple capitals, is characteristic of this type of crab., the G. albolineatus crab has an attractive color on the carapace organ that indicates the presence of pigment content. This study was aimed to determined the content and it’s pigment type of the organs of the carapace, epidermal layer, hepatopancreas, blood and gonads in the female G. albolineatus (Lamarck) crab. The method of this research in order to separated and determinated of pigment content by using thin layer chromatography (TLC) method. The results obtained in this study were the total pigment content of G. albolineatus crab showed the highest value in gonad organ with value 34,41 μg, followed by epidermal layer organ 12,19 μg, hepatopancreas 9,61 μg, blood 1,06 μg and carapace 0.42 μg. the pigment content of the gonads organ has the highest value compared with other organs, it is presumed that the female G. albolineatus crab is at the mature stage of the gonad, so that the carotenoid pigment is still accumulated on the gonad organ used for the gonadal maturation process. The types of pigment identified in the extract of the carapace organ, epidermal layer, hepatopancreas, gonads and blood from female G. albolineatus crabs with semipolar solution of Petroleum Eter and Acetone are: β-carotene, echinone, kantaksantin, adonirubin type, astaxanthine and astacene .Keywords: Grapsus albolineatus, TLC, pigmentGrapsus albolineatus (Lamarck) merupakan salah satu spesies kepiting yang berwarna hitam kehijauan yang ditemukan di atas atau di bawah batu pantai. Memiliki kaki jalan yang panjang dan tidak memiliki kaki renang serta memiliki capit yang berukuran kecil. Capit berwarna ungu, merupakan ciri khas kepiting jenis ini. Kepiting G. albolineatus memiliki warna yang menarik pada organ karapas yang mengindikasikan adanya kandungan pigmen. Penelitian ini bertujuan untuk menentukan kandungan dan jenis pigmen pada organ karapas, lapisan epidermis, hepatopankreas, darah dan gonad pada kepiting G. albolineatus (Lamarck) betina. Pemisahan yang umum digunakan dalam penentuan jenis pigmen karotenoid adalah menggunakan metode Kromatografi Lapis Tipis. Pemisahan ini dikenal karena proses pemisahannya mudah, sederhana dan membutuhkan waktu yang relatif singkat serta dapat menghasilkan data yang akurat. Hasil yang diperoleh dalam penelitian ini adalah kandungan pigmen total dari kepiting G. albolineatus menunjukkan nilai tertinggi pada organ gonad dengan nilai 34,41 ïg, diikuti organ lapisan epidermis 12,19 ïg, hepatopankreas 9,61 ïg, darah 1,06 ïg dan karapas 0,42 ïg. kandungan pigmen pada organ gonad memiliki nilai tertinggi dibandingkan dengan organ lainnya, diduga kepiting G. albolineatus betina ini berada pada tahap matang gonad, sehingga pigmen karotenoid masih tertumpuk pada organ gonad yang digunakan untuk proses pematangan gonad. Jenis-jenis pigmen yang teridentifikasi pada ekstrak organ karapas, lapisan epidermis, hepatopankreas, gonad dan darah dari kepiting G. albolineatus betina dengan larutan pengembang PE dan Aseton (80:20) yang bersifat semipolar yaitu: β-karoten, ekinenon, kantaksantin, tipe adonirubin, astaksantin dan astasen.Kata kunci : Grapsus albolineatus, KLT, pigme
ANALISIS JENIS PIGMEN DAN UJI AKTIVITAS ANTIBAKTERI EKSTRAK PIGMEN XANTOFIL PADA ALGA COKLAT Sargassum polycystum (C.Agardh)
No full textThe separation of algae extracts in a Thin Layered Chromatography used Matsjeh method, covering plate preparation, sample application, plate development and visualization. In addition, the pigment types of brown algae, Sargassum Polycystum (C. Agardh), were β-caroten, pheofitine, chlorophyll-a and lutein. The pigment types of extracts dissolved difference were β-caroten, xanthofil (zeaxanthin and lutein), chlorophyll-a and chlorophyll-c. The antibacterial activity test used Kirby-Bauer method. Results indicated that the extracts of S. Polycystum, had antibacterial activity on four test bacteria, Escherichia coli, Klebsiella pneumonia, Salmonella paratiphy b and Staphylococcus aureus.Kata Kunci: Algae, Sargassum Polycystum, pigment and antibacteria
