Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

Raw Data - High resolution electrochemical additive manufacturing of microstructured active materials: case study of MoSx as a catalyst for the hydrogen evolution reaction

The dataset contains raw data that complements the article: High resolution electrochemical additive manufacturing of microstructured active materials: Case study of MoSx as a catalyst for the hydrogen evolution reaction, J. Mater. Chem. A, 2021, 9, 22072-22081. C. Iffelsberger and M. Pumera* https://doi.org/10.1039/D1TA05581J Related to the MSCA Project: 888797 LoCatSpot

opencc-by-4.0Jul 2021View details →

Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering

A dataset for the publication: T. Puttonen, M. Salmi, J. Partanen, Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering, 2021. The paper studies the mechanical properties and fracture mechanics of Additive Manufacturing (AM) polyamide 12 (PA12) in two build orientations exposed to a 1500-hour accelerated weathering cycle (ISO-4982-3) followed by tensile testing (ISO-527). Fracture surfaces of X and Z build orientation AM PA12 and X build orientation AM glass-filled PA12 were studied with scanning electron microscopy. The tested AM materials were PA12, glass-filled PA12, and carbon-reinforced PA12. The reference materials cut from sheet included glass-filled and molybdenum disulfide-filled PA66, PMMA, ABS, PC, and cast PA12. The dataset contains: - Full tensile test results in PDF format, and individual CSV files - A python script for tensile CSV data plotting - Overall pictures of all samples after tensile tests - 3D models and drawings for tensile samples, manufacturing files for a custom QUV holder assembly - SEM images of fracture surfaces for AM polyamide 12 (SLS), X and Z build orientation, and glass-filled polyamide 12 (SLS) in the X build orientation   Version history: 1.0.1: A partially corrupted version of the tensile test results PDF file replaced (Tensile_test_results.pdf)

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Machine Breakdown Event

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an announcement of a machine breakdown event was simulated on Friday at 6:00, describing that machine MAQ119 could breakdown at any moment, detected using a predictive maintenance system. Accordingly, the proposed scheduler imposed a machine available frames constraint, during its event, of 0 usable frames, thus removing the machine from production. The proposed genetic algorithm was executed for 1 hour at period 769 (Friday at 7:00) until the remainder of the schedule’s time window. Also, the predefined optimization weights were 1 for total cost and 0 for machine occupancy deviation.   File Description: <ul> <li>Input_JSON_Machine_Breakdown_Optimization - JSON input data for the machine breakdown event</li> <li>Output_JSON_Machine_Breakdown_Optimization - JSON output data for the machine breakdown event</li> <li>Output_Statistics_Machine_Breakdown_Optimization - Excel output machine breakdown event statistics</li> </ul>

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Maintenance Optimization

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a maintenance optimization scenario was formulated to validate the scheduler's ability to schedule tasks as well as maintenance activities while also minimizing the total costs. Accordingly, it was considered an optimization weight of 1 for the total cost and 0 for machine occupancy deviation, as well as a 2-hour execution time for the genetic algorithm. Each maintenance activity, for every machine, has a duration of 6 hours and 10 minutes, with a labor cost of 3,22 EUR/hour during the stipulated maintenance hours and a monetary penalty, that doubles the cost (i.e., 6.44 EUR/hour) if done out of maintenance hours.   File Description: <ul> <li>Input_JSON_Maintenance_Optimization - JSON input data for the maintenance optimization</li> <li>Output_JSON_Maintenance_Optimization - JSON output data for the maintenance optimization</li> <li>Output_Statistics_Maintenance_Optimization - Excel output maintenance optimization statistics</li> </ul>

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Total Cost and Machine Occupancy Deviation Optimization

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a total cost and machine occupancy deviation optimization scenario was formulated that aims to demonstrate how the proposed scheduler is able to balance tasks between machines while also reducing overall costs. For this scenario, it was considered an optimization weight of 0.5 for both the total costs and machine occupancy deviation objectives and the genetic algorithm was executed for 2 hours.   File Description: <ul> <li>Input_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON input data for the total cost and machine occupancy deviation optimization</li> <li>Output_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON output data for the total cost and machine occupancy deviation optimization</li> <li>Output_Statistics_Total_Cost_Machine_Occupancy_Deviation_Optimization - Excel output total cost and machine occupancy deviation optimization statistics</li> </ul>

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Energy Cost Optimization with Energy Selling

Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an energy cost optimization considering the presence of an energy buyer is proposed to validate the scheduler’s ability to maximize profits while also minimizing energy costs. The scenario considers an added sales value corresponding to 50% of the buying. For this scenario, the genetic algorithm was executed for 2 hours, with 1 and 0 for the optimization weights total cost and machine occupancy deviation, respectively.   File Description: <ul> <li>Input_JSON_Energy_Cost_Energy_Selling_Optimization - JSON input data for the energy cost optimization with energy selling</li> <li>Output_JSON_Energy_Cost_Energy_Selling_Optimization - JSON output data for the energy cost optimization with energy selling</li> <li>Output_Statistics_Energy_Cost_Energy_Selling_Optimization - Excel output energy cost optimization with energy selling statistics</li> </ul>

Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Joint Optimization of Production and Maintenance

Using the previous datasets at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>>,  <<a href="https://zenodo.org/record/7055698">https://zenodo.org/record/7055698</a>>, <<a href="https://zenodo.org/record/7055580">https://zenodo.org/record/7055580</a>>, and <<a href="https://zenodo.org/record/7055573">https://zenodo.org/record/7055573</a>>  a joint optimization of production and maintenance scenario was formulated which aims at combining all the features from the cited scenarios. For energy selling, it was considered an added sales value corresponding to 50% of the buying. Regarding maintenance activities, it was simulated an announcement of a maintenance activity for MAQ118 from Monday at 07:00 (i.e., period 1) to Monday at 17:00 (i.e., period 120), and another for MAQ120 which can be done at any time. These maintenance activities take 6 hours and 10 minutes to complete and have an associated labor cost of 3,22 EUR/hour in maintenance hours, and a double cost penalty (i.e., 6.44 EUR/hour) if done out of maintenance hours. The scenario was executed in 2 hours, with the corresponding optimization weights of 0.8 and 0.2 for the total cost and machine occupancy deviation, respectively.   File Description: <ul> <li>Input_JSON_Joint_Optimization_Production_Maintenance - JSON input data for the joint optimization of production and maintenance</li> <li>Output_JSON_Joint_Optimization_Production_Maintenance - JSON output data for the joint optimization of production and maintenance</li> <li>Output_Statistics_Joint_Optimization_Production_Maintenance - Excel output joint optimization of production and maintenance statistics</li> </ul>

opencc-by-4.0Apr 2024View details →

Ion Implantation Sensor and Process Target Data for Predicting Ion Beam Tuning in Semiconductor Manufacturing

<h2>Dataset Description:</h2> This dataset is designed to predict ion beam tuning setup processes in semiconductor manufacturing, in terms of tuning success or failure, and tuning duration. It is split into <code>X</code> and <code>y</code> to allow for supervised learning approaches. <ul> <li><code>X</code> represents the current equipment condition and the process targets of the currently processed and the upcoming lot, as defined within recipes.</li> <li><code>y</code> represents the ion beam tuning setup report, which informs about the tuning success ratio and tuning duration. These setups are necessary, when switching between recipes to prepare the equipment for processing the next lot. <code>y</code> contains three labels, enabling classification of (1) tuning success or fail, and (2) prolonged tuning, as well as (3) estimation of tuning duration as a regression task.</li> </ul> About <code>X</code>: Each lot is processed with a specific recipe to achieve the process target. The tuning takes place before the first wafer of the to-be-tuned recipe is processed. Each row in <code>X</code> includes logistical information such as the equipment used for processing and parsed recipe / process target information for the current and upcoming lot. The majority of data consists out of aggregated metrics of equipment-internally tracked sensor traces, recording physical parameters such as gas flows, temperatures, voltages and currents. When analyzed in conjunction with the processed recipe, these sensors provide insights into the current equipment condition.  About <code>y</code>: The <code>setup_result</code> column indicates the success or failure of tuning - with <code>setup_result=0</code> indicating tuning success, while <code>setup_result=1</code> signals tuning failure. If the first tuning attempt fails, there may be follow-up attempts, but these are not included in this dataset. The <code>duration</code> column represents the tuning duration in seconds, as used for regression analysis. The <code>duration_interval</code> column is a binary label for prolonged tunings, i.e. <code>duration_interval=1</code> for instances, which take more than 6 minutes to tune. For reproducibility of the corresponding paper's results: <ol> <li>The dataset contains the same carefully curated subset of features.</li> <li>The train_test_split() has already been performed, thus we provide <code>x_train</code> and <code>x_valid</code> separately.</li> <li>To reduce the effect of outliers in the data, the sensor data has already been scaled, as derived from <code>x_train</code>.</li> </ol> In summary, these datasets (<code>X</code>, <code>y</code>) provide comprehensive information for predicting ion beam tuning in semiconductor manufacturing, making it a valuable resource for researchers and practitioners in the field. <h2>Python Code for Reproducibility:</h2> Furthermore, we share a jupyter notebook <code>ionbeamtuning.ipynb</code> with Python code to train the best performing model on the provided data, as described in the paper. To execute the code, you may need to install any missing packages specified in the <code>requirements.txt</code>, as indicated within the notebook.

opencc-by-4.0Oct 2023View details →

Light-Emitting Diode (LED) Manufacturing Cost Model

Excel files containing a bottom-up cost-models for GaN-based white light-emitting diodes (LEDs). Covers the commercial origins of the technology around 2003, 2012 and 2020. Compiled as part of the research project <a href="https://web.archive.org/web/20220920225758/https://www.ceenrg.landecon.cam.ac.uk/research/climate-change-and-energy-policy/what-factors-drive-innovation-in-energy-technologies-the-role-of-technology-spillovers-and-government-investment">"What factors drive innovation in energy technologies? The role of technology spillovers and government investment"</a>, funded by the Alfred P. Sloan Foundation. <table> <tbody> <tr> <th>File</th> <th>Content</th> <th>Comment</th> </tr> </tbody> <tbody> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/LEDCOM2003.xlsx">LEDCOM2003.xlsx</a></td> <td>Cost model for 2003. Includes additional description and credits.</td> <td> </td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/LEDCOM2003.xlsx">LEDCOM2012.xlsx</a></td> <td>Cost model for 2012.</td> <td> </td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/LEDCOM2003.xlsx">LEDCOM2020.xlsx</a></td> <td>Cost model for 2020.</td> <td> </td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/Cost%20Model%20Inputs.xlsx">Cost Model Inputs.xlsx</a></td> <td>Inputs for the cost model (all years).</td> <td>Includes data on electricity, clean room costs, etc.</td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/Cost%20Model%20Inputs.xlsx">LEDCOMv2.zip Inputs.xlsx</a></td> <td>Archive of the original U.S. Department of Energy cost model</td> <td>Includes descriptive documents.</td> </tr> </tbody> </table> Version 2: An incorrent comment in Cell D6 in the “Global” sheet in the “LEDCOM2020.xlsx” file has been removed.

opencc-by-4.0Sep 2024View details →

Data on the material characterization of cast and additively manufactured IN939 subjected to room-temperature low-cycle fatigue load

The original data to the research paper termed "Room-temperature low-cycle fatigue behaviour of cast and additively manufactured IN939 superalloy" are enclosed. Two specimen orientations of L-PBF IN939 - horizontal and vertical, and two thermodynamical states - without subsequent heat treatment (non-treated) and standard aged according to Delargy et al., 1986, were investigated. The paper concerns the low-cycle fatigue performance of cast and additively manufactured IN939 superalloy. It brings a comprehensive account on the damage and deformation behaviour of the tested alloy, combining the test analyses with high-resolution SEM and TEM observations.

opencc-by-4.0Jan 2020View details →

Metal Additive Manufacturing Open Repository

Metal Additive Manufacturing Open Repository This dataset gathers data from different parts of Additive manufacturing processes (Laser metal deposition - LMD, and Wire-arc additive manufacturing - WAAM). The dataset covers not only the process data, but also the design, NDT (Non-Destructive Testing) and dimensional inspection. Motivation The industrialisation of Additive Manufacturing (AM) requires a holistic data management and integrated automation. The presented dataset is part of an end-to-end Digital Manufacturing solution, enabling a cybersecured bidirectional dataflow for a seamless integration across the entire AM chain. The goal is to develop a new manufacturing methodology capable of ensuring the manufacturability, reliability and quality of a target metal component from initial product design via Direct Energy Deposition (DED) technologies, implementing a zero-defect manufacturing approach ensuring robustness, stability and repeatibility of the process. To that end, we present the Metal Additive Manufacturing Open Dataset, the first holistic dataset for AM manufacturing, covering all engineering stages from desing to validation. We hope that this dataset will be the first step for the development of new data pipelines aimed to optimize and improve the AM processes and to speed up their digital transformation. Authors <ul> <li>Carlos Gonzalez-Val: Main contact (carlos.gonzalez@aimen.es)</li> <li>Baltasar Lodeiro</li> <li>Marcos Diez</li> </ul>   Entities This dataset was collected under the INTEGRADDE project. Attributions: <ul> <li>AIMEN: Process data collection and manufacturing of T-Coupons, CC-Coupons-AIMEN and Jet Engine.</li> <li>MX3D: Process data collection and manufacturing of CC-Coupons-MX3D and Plates.</li> <li>University of West: Process data collection and manufacturing of CC-Coupons-WEST.</li> <li>IREPA: Process data collection and manufacturing of CC-Coupons-IREPA.</li> <li>CEA: Tomography analysis.</li> <li>DATAPIXEL: Dimensional inspection.</li> </ul> Structure The dataset follows this structure: <ul> <li>Dataset <ul> <li>[SAMPLE 1 NAME] <ul> <li>README: metadata and information about the sample. Format: txt.</li> <li>Photo: a photo of the manufactured sample. Format: jpg.</li> <li>Design: a 3D design file of the piece before manufacturing (original design). Format: stl.</li> <li>Trajectories: the trajectories followed for the manufacturing. Format: gcode.</li> <li>Process data: data recorded from the process. Format hdf5.</li> <li>Tomography: data from a 3D tomographic reconstruction. Format: raw.</li> <li>Dimensional inspection: A comparison</li> </ul> </li> <li>[SAMPLE 2 NAME] <ul> <li>...</li> </ul> </li> </ul> </li> </ul> Further information and metadata is contained in each stage's subdirectory. Note that not all the samples contain all the stages. Software To open the different files that conform the dataset, we recommend the following Open softwares: <ul> <li> hdf5 -> HDF5 Viewer: https://www.hdfgroup.org/downloads/hdfview/</li> <li> stl/amf -> Slic3r: https://slic3r.org / OpenJScad: https://openjscad.org/</li> <li> stp -> ShareCad: https://beta.sharecad.org/</li> <li> gcode -> Text editor / Slic3r: https://slic3r.org/</li> <li> raw -> ImageJ: https://imagej.net/</li> </ul> More information on how to open the files of the dataset can be found in the README.

opencc-by-4.0Mar 2023View details →

Shanghai zhizao changshang gailan 上海製造廠商概覽 (A Compendium of Shanghai Manufacturing Firms) dataset

The 上海製造廠商概覽 dataset contains the data extracted from the survey of industrial establishments in Shanghai published in 1947 by Lianhe zhengxinsuo 聯合徵信所. The spreadsheet contains the original data ("Data-Main" tab), the metadata ("Metadata" tb) and a series of pre-calculated dataset based on the main data. The dataset was used as the basis for the papers published by Christian Henriot: 1.     “The impact of war on Shanghai’s industrial structure: A GIS-based analysis of the Shanghai industrial surveys (1935-1940)” (with Isabelle Durand), <a href="https://www.virtualshanghai.net/Data/Tables?ID=237#.Uweb8V5ySCU">Annals of GIS</a>, Vol. 18, no. 1, 2012, 45-55 2.     “Regeneration and Mobility: The Spatial Dynamics of Industries in Wartime Shanghai”, <a href="http://www.journals.elsevier.com/journal-of-historical-geography/">Journal of historical geography</a>, vol. 38, no. 2, 2012, pp. 167-180

opencc-by-4.0May 2023View details →

Supplementary codes and datasets for "Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials"

This repository provides data and codes for manuscript “Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials”  by M. Do&scaron;kář, M. Somr, R. Hlůžek, J. Havelka, J. Novák, and J. Zeman, published first as a preprint <a href="https://arxiv.org/abs/2305.09280">arXiv:2305.09280</a> at arXiv.org; see the actual description of the Zenodo entry for the latest reference. This repository contains: <ol> <li>MATLAB and C++ source codes for combinatorial design and numerical analyses (folder <code>./numerics/</code>),</li> <li>experimental data (folder <code>./experiments/</code>),</li> <li>3D models of parts used in robotic-assembly (folder <code>./models/</code>),</li> <li>a control script for robotic assembly (folder <code>./robotics/</code>).</li> </ol> Numerics All simulations were performed with an in-house MATLAB code, which extends the finite element toolbox for finite strain calculations accompanying the work of <a href="https://doi.org/10.1016/j.cma.2020.113333">van Bree, S. E. H. M., Roko&scaron;, O., Peerlings, R. H. J., Do&scaron;kář, M., & Geers, M. G. D. (2020). A Newton solver for micromorphic computational homogenization enabling multiscale buckling analysis of pattern-transforming metamaterials. Computer Methods in Applied Mechanics and Engineering, 372, 113333</a>. In particular, this snapshot corresponds to a cleaned-up version (excluding files unrelated to the publications) of commit <code>21cfc2e9</code>. The MATLAB codebase contains MEX files written in C++ to accelerate selected procedures. In order to run any code, these MEX files must be compiled first. We use CMake build automation, with the main <code>CMakeLists.txt</code> located in <code>./numerics/mex</code>. Combinatorial search was performed by the <code>RUN_modular_exploration.m</code> script; see definition of problems with the script. The results of the enumerations, stored in <code>./dat/exploration</code>, were analysed with <code>POST_modular_S_v3.m</code>, identifying layouts leading to the extreme (min/max) tilt angles. Comparison against experimental measurements was facilitated by a series of scripts <code>POST_DIC_{...}.m</code>. First, run <code>POST_DIC_step1_extract_points_in_mesh.m</code> to identify locations.mat. Next, post-process extensometer data with <code>POST_DIC_step2_merge_extensometer_data.m</code>, and use <code>POST_DIC_step3_impose_extracted_BC.m</code> to parse DIC results in a format suitable for imposing BC later in this script. Finally, comparison between experimental and computed displacements is provided by <code>POST_DIC_step4_modular_comparison_experiments.m</code>. (Note that the particular files need to be manually provided in the “Compute deformation process” part of <code>POST_DIC_step4_modular_comparison_experiments.m</code>.) Experimental data This folder contains data from (i) an unixaial tension test of a dogbone specimen (both from a MTS loading machine and DIC data) and (ii) two measurement sessions extracting the L-shape domain responses using DIC (<code>20_11_30 - Hluzek_Elka_newassemblyplan</code> and <code>21_04_12 - Hluzek_ Elka_quarters</code> with lower loading threshold). For post-processing, see the above-mentioned <code>POST_DIC_{...}.m</code> scripts. <code>*.mat</code> files present directly in <code>./experiments/</code> folder were obtained and are need by those scripts. 3D models The folder contains geometrical models for individual parts needed for robot-assisted assembly of module molds for casting. This includes: <ol> <li>a silo extension to store more tiles (file <code>silo_extension.stl</code>),</li> <li>formwork modules around the main structure for the purpose of casting silicone (file <code>tile_formwork.stl</code>),</li> <li>all types of tiles for the inside structure (file <code>tile_inside_types.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_1.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_2.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_3.stl</code>),</li> <li>connection for spacers (file <code>yumi_base_4.stl</code>),</li> <li>spacer holding a silo and the build plate (file <code>yumi_base_5.stl</code>),</li> <li>YuMi grippers with extensions to hold the tiles (file <code>grippers_extend.st</code>).</li> </ol> Robotics The folder contains a single file with a script created in RobotStudio (RobotWare Version: 6.08.01.00, SmartGripper Version: 3.55.0000.00) to assemble the plan with YuMi IRB 14000-0.5/0.5 left hand. Acknowledgement The related research, experiments, and code development were supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.

opencc-by-sa-4.0Apr 2020View details →

Datasets and images of publication: Additive manufacturing for self-healing soft robots

This entry contains the images and data used for the publication: Additive manufacturing for self-healing soft robots (DOI: 10.1089/soro.2019.0081). The datasets are named after the image they refer to and are available under the CC-BYSA 4.0 International license.

opencc-by-4.0Oct 2020View details →

Dataset for semantic segmentation of the laboratory model of manufacturing environment

This dataset includes images and labels used for semantic segmentation of the laboratory model of the manufacturing environment, at the University of Belgrade - Faculty of Mechanical Engineering. The dataset is gathered by using mobile robot RAICO (Robot with Artificial Intelligence based COgnition) and its stereo visual system made from two Basler acA1920-25uc cameras with Fujinon lens DF6HA-1B. The dataset includes close to 430 images with a resolution of 640x360. Images are acquired by both cameras at different mobile robot poses in the laboratory model of a manufacturing environment. Five classes are introduced in the dataset, machines 1 to 4, and a background class. The exact names of the classes are: classNames = ["Machine_1", "Machine_2", "Machine_3", "Machine_4", "Background"]; while the labels of the classes (RGB values of label images) are: labelIDs = [ ...     000 000 255; ... % "Machine 1"     000 255 255; ... % "Machine 2"     255 255 000; ... % "Machine 3"     255 000 000; ... % "Machine 4"     255 255 255; ...   % "Background"     ]; Image and label pairs are entitled 1 to 430, and e.g. label 5 corresponds to images 5. This dataset was developed with the support of the Science Fund of the Republic of Serbia, Grant No. 6523109, AI - MISSION4.0, 2020-2022.

opencc-by-4.0Dec 2016View details →

Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes

This file contains the data reported in the paper: A Bertei, F Tariq, V Yufit, E Ruiz-Trejo, N P Brandon, Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes, Journal of the Electrochemical Society (2016) All the data here reported can be reproduced by solving the equations reported in the manuscript with the corresponding parameters.

opencc-by-4.0Apr 2022View details →

World Map of Agricultural Robot Manufacturer & services

This Data Base is a World Map of all the manufacturers of agricultural robot for field. this data base was created by 3 student of the University of UniLaSalle Beauvais. You can fin the LINK of the GOOGLE EARTH "only reading" on the PDF document.

opencc-by-4.0May 2023View details →

Dataset for: "Additively manufactured degradable piezoelectric microsystems for sensing and actuating"

<h2>Dataset for "Additively manufactured degradable piezoelectric microsystems for sensing and actuating"</h2>Morgan Monroe1,2, Nicolas Fumeaux1, L. Guillermo Villanueva2, and Danick Briand11Soft Transducers Laboratory (LMTS), EPFL, Switzerland<a href="mailto:morgan.monroe@epfl.ch">morgan.monroe@epfl.ch</a>, <a href="mailto:danick.briand@epfl.ch">danick.briand@epfl.ch</a>2Advanced NEMS Laboratory (A-NEMS), EPFL, Switzerland This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled "Additively manufactured degradable piezoelectric microsystems for sensing and actuating" DOI: 10.1002/admt.202300745-------------------------------------------------------------------------------------------------------------------------------<h3>Manuscript Abstract: </h3>The increasing global overabundance of electronic waste and concerns regarding the energy and material-intensive processes associated with traditional electronics manufacturing is driving the development of solution processed, degradable electronics. In particular, solution processed, degradable piezoelectrics have widespread potential in sustainable electronics, due to their diverse use in both sensing and actuating applications and the current industry predominance of lead-based materials. Yet current eco-friendly multi-material printing processes are limited by both the conventional challenges of multilayer process integration as well as the low-temperature thermal constraints of biodegradable materials. In this study, we present a novel approach to the fabrication of additively manufactured and sustainable piezoelectric devices made with degradable electrode materials on paper substrates. The screen-printed, eco-friendly KNbO3 piezoelectric transducers are combined with degradable carbon- or zinc-based conductive inks. We evaluate the physical, dielectric, and piezoelectric properties of the devices, assessing the influence of electrode material on device performance. We report on effective piezoelectric coefficients as high as 4.6 pC N-1 and 5.1 pC N-1 for printed piezoelectric devices on paper substrates with carbon and zinc electrodes respectively. We then demonstrate the applicability of the developed technology in both sensing and actuating applications. Thus, we present the first instance of sustainable fully additively manufactured piezoelectric force sensors and acoustic speakers. By demonstrating entirely printable piezoelectric devices compatible with various green electrode materials, we work to develop more complex sustainable printed piezoelectric technologies in the future. <h3>The data set consists of the following folders:</h3><ul><li>Device design files</li><li>Physical characterization data</li><li>Dielectric characterization data</li><li>Force Sensor Demonstrator data</li><li>Speaker Demonstrator data</li></ul>Below is a detailed description of the data types and contents of each folder. In many files, the naming convention includes one of two key material indicators. In reference to ink properties, the term "Ink Active Material" indicates the primary ingredient in the ink being characterized. This is either KNbO3, Zinc, or Carbon. The specific ink compositions can be found in the Methods portion of the associated manuscript. In reference to devices being characterized, the term "Electrode Material" indicates the primary component of the printed or deposited electrode layers of the devices (as the piezoelectric layer is always the printed KNbO3 film). The electrode materials are either "Gold" (referring to thermally evaporated Gold of approximately 100nm thickness) which was used as a reference electrode material, "Zinc" (referring to screen printed zinc ink as described in the manuscript), or "Carbon" (Referring to screen printed carbon ink as described in the manuscript).-------------------------------------------------------------------------------------------------------------------------------<h3>Data types</h3>There are 13 file types in this data set: .pdf, .png, .tif, .txt, .dat, .csv, .svg, .stl, .py, .vi, .dwf3work, .aup3, .wav<ul><li>.pdf files <ul><li>pdf files in this repository contain summaries of images used in physical characterization, aggregated for ease of visualization. These are exported from Photoshop files and thus include full image information.</li></ul></li><li>.png and .tif files <ul><li>These files contain scanning electron microscopy images of the printed samples on silicon, in cross-section.</li></ul></li><li>.txt, .csv, and .dat files <ul><li>These files contain raw data from device characterization. These file types are comma delimited and the files can be opened with text editors such as Notepad++. Column headers elaborate on the data contained within each file.  </li></ul></li><li>.svg files <ul><li>These files contain vector data displaying the full designs of devices fabricated in this study. This data can be opened with a vector graphics editor such as InkScape. The various layers of each file denote a different layer of the device design, and can be treated individually as masks for the relevant layers.</li></ul></li><li>.stl files <ul><li>These files contain design information for 3D components. These files can be opened with any 3D design software such as FreeCAD. All .stl files included in this repository are reproduced from Shannon Ley (<a href="https://pinshape.com/items/36134-3d-printed-3d-printed-headphones">https://pinshape.com/items/36134-3d-printed-3d-printed-headphones</a>).</li></ul></li><li>.py files <ul><li>These files contain python scripts used to process the raw data after collection. These scripts can be opened and edited with standard python scripting interfaces. Each script is commented with descriptions of the overall file as well as in-line comments for user orientation. </li></ul></li><li>.vi files <ul><li>These files contain LabVIEW scripts used to collect raw data during measurements. These scripts can be opened and edited with LabVIEW from version 2020. The script is commented with descriptions of the overall file as well as in-line comments for user orientation. Only one file in this dataset is of this filetype.</li></ul></li><li>.dwf3work files<ul><li>These files contain Digilent Waveforms workspaces used to collect raw data during measurements. These scripts can be opened using the opensource Digilent Waveforms software (<a href="https://digilent.com/shop/software/digilent-waveforms/">https://digilent.com/shop/software/digilent-waveforms/</a>) to view the recorded data and all relevant recording parameters at time of measurement. </li></ul></li><li>.aup3 files <ul><li>These files contain Audacity workspaces used to collect and process audio data during speaker characterization measurements. These files can be opened using the opensource Audacity software (<a href="https://www.audacityteam.org/">https://www.audacityteam.org/</a>).</li></ul></li><li>.wav files <ul><li>These files are audio files of the data exported from Audacity during speaker device characterization and can be opened with any audio processing software.</li></ul></li></ul>-------------------------------------------------------------------------------------------------------------------------------<h3>01 Characterization Device Design</h3>This folder contains the design files associated with the fabrication of the basic printed devices used for characterization studies.<h4>01 CapacitorsALL.svg</h4>A vector file containing the whole-device design of the capacitor style devices used in primary characterization. Each layer of the file is a layer of the device, and was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.<h4>02 BottomElectrode.svg</h4>A vector file containing the design of solely the bottom electrode layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.<h4>03 PiezoelectricLayer.svg</h4>A vector file containing the design of solely the piezoelectric layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.<h4>04 TopElectrode.svg</h4>A vector file containing the design of solely the top electrode layer for devices used in primary characterization. This design was used for the ordering and fabrication of associated screen printing meshes and shadowmasks.-------------------------------------------------------------------------------------------------------------------------------<h3>02 Physical Characteristics</h3>This folder contains all the data associated with characterizing the physical properties of the piezoelectric devices in this manuscript.<h4>01 Particle Size Analysis</h4>01 Images: A folder of SEM images as .png files. These images are cross-sectional SEM images of samples used to evaluate the particle size distribution for the KNbO3, Zinc, and carbon powders using in device fabrication. A second set of images with an appended filename ("traces") in the same folder portrays the sizing lines used to randomly sample particles for sizing.02 ParticleSizeDistribution_RAW.csv: A data file containing the raw measurements collected using the above images in tandem with ImageJ processing software. Data was used to produce histograms of particle size distributions for the KNbO3, Zinc, and Carbon particles. <h4>02 Profilometry</h4>01 Profilometry Data: A folder of raw data collected as profilometry measurements in the form of .dat files. Data files are labelled using the convention "Profile_[Electrode Material]_DeviceStack_Sample[#].dat" (Ex: "Profile_Carbon_DeviceStack_Sample2.dat"). All measurements begin with a measure of the paper substrate as reference before approaching the sample, where it crosses all 3 layers of the sample before returning to the paper substrate, creating a series of layer-cake-like steps from which layer thicknesses can be determined.02 ProfilometeryDataPlotter.py: a python script to batch import and plot the above collected raw profilometry data.  <h4>03 Cross-section Optical Images</h4>A folder containing optical microscopy images of the printed devices in cross-section when printed on paper substrates. The naming convention used is "Optical_[Electrode Material]_[microscope Magnification]_[Image number in that condition].tif" (Ex: "Optical_Carbon_x50_01.tif"). <h4>04 Cross-section SEM Images</h4>A folder containing scanning electron microscopy ("SEM") images of the printed devices in cross-section when printed on silicon substrates. The naming convention used is "SEM_[Electrode Material]_[microscope Magnification]_[Image number in that condition].tif" (Ex: "SEM_Carbon_1k_01.tif"). <h4>05 Ink Rheology </h4>Summary data collected during rheological measurements of the KNbO3, Zinc, and Carbon inks, as .txt files. The naming convention is "Viscosity_[Ink Primary Component]Ink.txt" (Ex: "Viscosity_ZincInk.txt") <h4>06 Degradation Study </h4>01 DegradationStudy_MassChange.csv: A data file containing the raw measurements collected during the degradation studies. Data includes the specific samples under test and their measured mass at specific dates, as measured after drying.02 Degradation Study Images.pdf: a .pdf file containing the raw degradation images used in this study, correlated to the dates of imaging and text conditions. -------------------------------------------------------------------------------------------------------------------------------<h3>03 Electrical Characteristics</h3>This folder contains all the data associated with characterizing the dielectric and piezoelectric properties of the piezoelectric devices in this paper.<h4>01 Impedance Data</h4>01 ImpedanceDataAnalysis.py The python analysis script used to batch analyze and plot the raw impedance data collected for these samples. Takes the files in the associated folder as input and outputs arrays of measured capacitance and permittivity values for the data analyzed, as well as plots of the processed impedance data as a function of frequency.02 Raw Impedance DataThe raw impedance data used to evaluate the dielectric properties of the devices, including two file types: ".csv": Impedance data collected for capacitor style devices of each electrode type, in the after exporting the relevant impedance, phase, and capacitance data from the raw collection file format. Naming convention is "Impedance_[electrode material]_ALL.csv" (Ex: "Impedance_Carbon_ALL.csv"). ".dwf3work": Raw Impedance data collected for capacitor style devices of each electrode type, in the original files as collected using Digilent Waveforms Software (open source). Data is split into two files based on the size of the capacitors being measured, (either 5 and 10 mm2 devices, or 20 and 30mm2 devices). The naming convention used is "Impedance_[Electrode Material]__[capacitor surface areas tested].dwf3work" (Ex: "Impedance_Zinc_5&10mm2.dwf3work"). <h4>02 Berlincourt Data</h4>This folder contains all the data associated with characterizing the piezoelectric properties of the devices in this manuscript. It includes 3 files, sorted by the electrode material of the devices under test, and reports the average d33,eff values (over 3 repetitions) measured for those devices based on the applied poling Voltage and field. The naming convention used is "BerlincourtData_[Electrode Material].csv" (Ex: "BerlincourtData_Carbon.csv").-------------------------------------------------------------------------------------------------------------------------------<h3>04 Force Sensor Demonstrator</h3>This folder contains all the data associated with force sensor demonstrator reported on in the associated manuscript.<h4>01 TouchGrid.svg</h4>A vector file containing the whole-device design of the force sensor device used in this sensing demonstration. Each layer of the file is a layer of the device, and was used for the ordering and fabrication of associated screen printing meshes. <h4>02 VoltageMeasurement.vi</h4>A LabView script file used for recording the output voltage of the piezoelectric devices as a function of time. To be used in combination with an Agilent 34410A or 34411A Multimeter. Outputs Voltage response as a function of time from the initiation of the recorded measurement. <h4>03 Single Force Data</h4>This folder contains data associated with characterizing the force sensor demonstrators reported on in this manuscript. It includes 3 sub-folders, sorted by the electrode material of the devices under test. The naming convention used for the sub-folders is "SingleForce_[Electrode Material]" (Ex: "SingleForce_Zinc"). Within each sub-folder is a series of data files (.csv) detailing the test and data conditions. Each sample was compressed with a set force in a series of pulses. The naming convention used for the files in the sub-folders is "[Electrode Material]_[Max Applied Force]_[MeasuredData].csv" (Ex: "Gold_12.5N_MM.csv"), where the measured data is either "_F" or "_MM" for Force data or Multimeter data respectively."_F.csv": The files include data recorded by the Instron Pull tester of the force applied to the sample as a function of time. This data is associated with the measured voltage in the paired "_MM.csv" file. "_MM.csv": The files include data recorded by a LabVIEW script in tandem with an Agilent multimeter of the voltage produced by the sample associated with the incident force in the paired "_F.csv" file.  <h4>04 Stepped Force Data</h4>This folder contains data associated with characterizing the force sensor demonstrators reported on in this manuscript. It includes 3 sub-folders, sorted by the electrode material of the devices under test. The naming convention used for the sub-folders is "SteppedMeasurements_[Electrode Material]" (Ex: "SteppedMeasurements_Gold"). Within each sub-folder is a series of data files (.csv) detailing the test and data conditions. Each sample was compressed with a series of pulses successively increasing in applied force. The naming convention used for the files in the sub-folders is "[Electrode MaterialSteppedSweep[#]_[MeasuredData].csv" (Ex: "Carbon_SteppedSweep1_F.csv"), where the measured data is either "_F" or "_MM" for Force data or Multimeter data respectively, and the # indicates the repetition number of that specific measurement. Other details are the same as those described above for the subfolder data of 03 Single Force Data.-------------------------------------------------------------------------------------------------------------------------------<h3>05 Speaker Demonstrator</h3>This folder contains all the data associated with speaker demonstrator reported on in the associated manuscript.<h4>01 Speaker Design</h4>01 SpeakersBuzzers.svg A vector file containing the design of the components used to fabricate the piezoelectric buzzer for the speaker demonstrator. This includes a baseplate onto which the piezoelectric devices were adhered, two rings as standoffs, and two long traces used for the "contact wires". All components were lasercut from cardboard or cardstock components.02 3D Design Files A folder containing the design files (.stl) for 3D printing of the headphone chassis, including the headband, ear cans, and baffles. All designs were provided open source by Shannon Ley (https://pinshape.com/items/36134-3d-printed-3d-printed-headphones). <h4>02 Speaker Data</h4>01 RawAudioRecordings A folder containing the raw audio recordings used in speaker characterization, in the form of .aup3 files, directly from the recording software (Audacity). File naming convention is "[Electrode Material]_[Device Size used]_[Recording sampling rate]_RAW.aup3" (Ex: "Zinc_5x30mm2_44.1khz_RAW.aup3").02 Exported Audio A folder containing the exported audio recordings used in speaker characterization after trimming to a consistent length of 15s, and exporting from the recording software (Audacity) in the form of .wav files. Each sub-folder has naming convention of "[Electrode Material]_[Device Size used]_[Recording sampling rate]_export" (Ex: "Zinc_5x30mm2_44.1khz_export"), and contains a number of files. Each file within these folders is labelled with the frequency at which the speaker was actuated for that recording data. These files were then imported into Origin, where an FFT was used to extract the amplitude of the recorded data at that specific actuating frequency. <h4>03 Speaker LDV</h4>A folder containing the laser doppler vibrometry data collected for speakers with either Zinc or Carbon-electroded piezoelectric actuators. The data comes in two formats: as-produced from Digilent Waveforms (.dwf3work), or exported (.csv) files. The naming convention for the raw data is "[Electrode Material]_[Speaker active area]_LDV_Raw.dwf3work" (Ex: "Zinc_5x30mm2_LDV_Raw.dwf3work"). The naming convention for the exported data is "[Electrode Material]_[Speaker active area]_LDV_Export.csv" (Ex: "Zinc_5x30mm2_LDV_Export.csv").

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Image repository for "Towards advancing Translators' Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context"

The files on this trusted repository  are provided by the authors of the manuscript with the title “Towards advancing Translators’ Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context” that was received by the MDPI journal Sustainability (ISSN 2071-1050) on 29 January 2024, got the manuscript ID sustainability-2872279, and is intended to become part of the special issue “Sustainable Materials, Manufacturing and Design” accessible under the link <a href="https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design">https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design</a>. The authors Paul-Ludwig Michael Noeske, Alexandra Simperler, Welchy Leite Cavalcanti, Vinicius Carrillo Beber, Brendon Weager, Tasmin Alliott, Peter Schiffels, and Gerhard Goldbeck aim at facilitating common access to the files representing high-resolution microscopy images (corresponding to the light microscopy (LM) and scanning electron microscopy (SEM) images shown in Figure 9 and Figure 12 in the manuscript or complementing them) given in .jpg and .tif format, respectively. Moreover, this repository comprises a .csv file containing the data points underlying the values presented in Table A1 of this manuscript and their description. The authors indicate here that following the sixth step of the translation process in materials modelling the translator may provide these data in this presentation that is adapted to the process-centric perspective required by representatives of an enterprise manufacturing prepregs and to their background knowledge disclosed to the translator beforehand.“