{"data":{"id":"10.7910/dvn/wbdkn6","type":"dois","attributes":{"doi":"10.7910/dvn/wbdkn6","prefix":"10.7910","suffix":"dvn/wbdkn6","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Hofmann, Bernd","nameType":"Personal","givenName":"Bernd","familyName":"Hofmann","affiliation":["Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems (FAPS)"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-0666-6149","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Crimp Force Curve Dataset"}],"publisher":"Harvard Dataverse","container":{},"publicationYear":2025,"subjects":[{"subject":"Computer and Information Science"},{"subject":"Engineering"},{"subject":"Time Series, Manufacturing, Quality Control, Crimping"}],"contributors":[{"name":"Hofmann, Bernd","nameType":"Personal","givenName":"Bernd","familyName":"Hofmann","affiliation":["Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems (FAPS)"],"contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2025-03-21","dateType":"Submitted"},{"date":"2025-03-21","dateType":"Available"},{"date":"2025-07-11","dateType":"Updated"},{"date":"2025","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["149311209","149289365","1241","149269880","27186"],"formats":["application/octet-stream","application/octet-stream","application/json","application/octet-stream","image/png"],"version":"3.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"lang":"en","rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\u0026lt;p\u0026gt;The \u0026lt;b\u0026gt;\"Crimp Force Curve Dataset\"\u0026lt;/b\u0026gt; is a comprehensive collection of univariate time series data representing crimp force curves recorded during the manufacturing process of crimp connections. This dataset has been designed to support a variety of applications, including anomaly detection, fault diagnosis, and research in data-driven quality assurance.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;A salient feature of this dataset is the presence of high-quality labels. Each crimp force curve is annotated both by a state-of-the-art crimp force monitoring system - capable of binary anomaly detection - and by domain experts who manually classified the curves into detailed quality classes. The expert annotations provide a valuable ground truth for training and benchmarking machine learning models beyond anomaly detection.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;The dataset is particularly well-suited for tasks involving time series analysis, such as training and evaluating of machine learning algorithms for quality control and fault detection. It provides a substantial foundation for the development of generalisable, yet domain-specific (crimping), data-driven quality control systems.\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;The data is stored in a Python pickle file \u0026lt;code\u0026gt;crimp_force_curves.pkl\u0026lt;/code\u0026gt;, which is a binary format used to serialize and deserialize Python objects. It can be conveniently loaded into a pandas DataFrame for exploration and analysis using the following command:\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;\u0026lt;code\u0026gt;df = pd.read_pickle(\"crimp_force_curves.pkl\")\u0026lt;/code\u0026gt;\u0026lt;/p\u0026gt;\n\u0026lt;p\u0026gt;The DataFrame consists of 2,439 rows (force curves) and 11 columns:\u0026lt;/p\u0026gt;\n\n\u0026lt;ul\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;CrimpID:\u0026lt;/strong\u0026gt; Unique identifier assigned by the crimp force monitoring system as integer.\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Wire_cross-section_conductor:\u0026lt;/strong\u0026gt; Wire cross-section of the conductor as float [mm²].\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Force_curve_raw:\u0026lt;/strong\u0026gt; Raw force curve with 3,566 datapoints as NumPy array with integer values [Force sensor value].\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Force_curve_baseline:\u0026lt;/strong\u0026gt; Baseline curve for comparability as NumPy array with integer values [Force sensor value].\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Force_curve_RoI:\u0026lt;/strong\u0026gt; Region of interest curve for crimp quality evaluation with 500 datapoints as NumPy array with integer values [Force sensor value].\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Main_label_string:\u0026lt;/strong\u0026gt; Quality class (\u0026lt;em\u0026gt;OK\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;Missing Strands\u0026lt;/em\u0026gt;, or \u0026lt;em\u0026gt;Crimped Insulation\u0026lt;/em\u0026gt;) manually assigned by the authors based on the preparation steps, as strings.\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Main_label_encoded:\u0026lt;/strong\u0026gt; Encoded quality classes (\u0026lt;em\u0026gt;OK\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;Missing Strands\u0026lt;/em\u0026gt;, or \u0026lt;em\u0026gt;Crimped Insulation\u0026lt;/em\u0026gt;) as integers from 0 to 2.\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Sub_label_string:\u0026lt;/strong\u0026gt; Fine-grained quality class (\u0026lt;em\u0026gt;OK\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;One Missing Strand\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;Two Missing Strands\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;Three Missing Strands\u0026lt;/em\u0026gt;, or \u0026lt;em\u0026gt;Crimped Insulation\u0026lt;/em\u0026gt;) manually assigned by the authors based on the preparation steps, as strings.\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Sub_label_encoded:\u0026lt;/strong\u0026gt; Encoded sub-classes (\u0026lt;em\u0026gt;OK\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;One Missing Strand\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;Two Missing Strands\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;Three Missing Strands\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;Crimped Insulation\u0026lt;/em\u0026gt;) as integers from 0 to 4.\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;Binary_label_encoded:\u0026lt;/strong\u0026gt; Binary encoded quality classes (\u0026lt;em\u0026gt;OK\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;NOK\u0026lt;/em\u0026gt;) for anomaly detection, manually assigned by the authors as integers from 0 to 1.\u0026lt;/li\u0026gt;\n \u0026lt;li\u0026gt;\u0026lt;strong\u0026gt;CFM_label_encoded:\u0026lt;/strong\u0026gt; Binary encoded quality classes (\u0026lt;em\u0026gt;OK\u0026lt;/em\u0026gt;, \u0026lt;em\u0026gt;NOK\u0026lt;/em\u0026gt;) assigned by the crimp force monitoring system as integers from 0 to 1.\u0026lt;/li\u0026gt;\n\u0026lt;/ul\u0026gt;\n\n\n\u0026lt;p\u0026gt;This dataset is a valuable resource for researchers and practitioners in manufacturing engineering, computer science, and data science who are working at the intersection of quality control in manufacturing and machine learning.\u0026lt;/p\u0026gt;","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.7910/DVN/WBDKN6</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Hofmann, Bernd</creatorName>
      <givenName>Bernd</givenName>
      <familyName>Hofmann</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org">https://orcid.org/0009-0009-0666-6149</nameIdentifier>
      <affiliation>Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems (FAPS)</affiliation>
    </creator>
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  <titles>
    <title>Crimp Force Curve Dataset</title>
  </titles>
  <publisher>Harvard Dataverse</publisher>
  <publicationYear>2025</publicationYear>
  <subjects>
    <subject>Computer and Information Science</subject>
    <subject>Engineering</subject>
    <subject>Time Series, Manufacturing, Quality Control, Crimping</subject>
  </subjects>
  <contributors>
    <contributor contributorType="ContactPerson">
      <contributorName nameType="Personal">Hofmann, Bernd</contributorName>
      <givenName>Bernd</givenName>
      <familyName>Hofmann</familyName>
      <affiliation>Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems (FAPS)</affiliation>
    </contributor>
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  <dates>
    <date dateType="Submitted">2025-03-21</date>
    <date dateType="Available">2025-03-21</date>
    <date dateType="Updated">2025-07-11</date>
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    <description descriptionType="Abstract">&amp;lt;p&amp;gt;The &amp;lt;b&amp;gt;&amp;quot;Crimp Force Curve Dataset&amp;quot;&amp;lt;/b&amp;gt; is a comprehensive collection of univariate time series data representing crimp force curves recorded during the manufacturing process of crimp connections. This dataset has been designed to support a variety of applications, including anomaly detection, fault diagnosis, and research in data-driven quality assurance.&amp;lt;/p&amp;gt;
&amp;lt;p&amp;gt;A salient feature of this dataset is the presence of high-quality labels. Each crimp force curve is annotated both by a state-of-the-art crimp force monitoring system - capable of binary anomaly detection - and by domain experts who manually classified the curves into detailed quality classes. The expert annotations provide a valuable ground truth for training and benchmarking machine learning models beyond anomaly detection.&amp;lt;/p&amp;gt;
&amp;lt;p&amp;gt;The dataset is particularly well-suited for tasks involving time series analysis, such as training and evaluating of machine learning algorithms for quality control and fault detection. It provides a substantial foundation for the development of generalisable, yet domain-specific (crimping), data-driven quality control systems.&amp;lt;/p&amp;gt;
&amp;lt;p&amp;gt;The data is stored in a Python pickle file &amp;lt;code&amp;gt;crimp_force_curves.pkl&amp;lt;/code&amp;gt;, which is a binary format used to serialize and deserialize Python objects. It can be conveniently loaded into a pandas DataFrame for exploration and analysis using the following command:&amp;lt;/p&amp;gt;
&amp;lt;p&amp;gt;&amp;lt;code&amp;gt;df = pd.read_pickle(&amp;quot;crimp_force_curves.pkl&amp;quot;)&amp;lt;/code&amp;gt;&amp;lt;/p&amp;gt;
&amp;lt;p&amp;gt;The DataFrame consists of 2,439 rows (force curves) and 11 columns:&amp;lt;/p&amp;gt;

&amp;lt;ul&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;CrimpID:&amp;lt;/strong&amp;gt; Unique identifier assigned by the crimp force monitoring system as integer.&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Wire_cross-section_conductor:&amp;lt;/strong&amp;gt; Wire cross-section of the conductor as float [mm²].&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Force_curve_raw:&amp;lt;/strong&amp;gt; Raw force curve with 3,566 datapoints as NumPy array with integer values [Force sensor value].&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Force_curve_baseline:&amp;lt;/strong&amp;gt; Baseline curve for comparability as NumPy array with integer values [Force sensor value].&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Force_curve_RoI:&amp;lt;/strong&amp;gt; Region of interest curve for crimp quality evaluation with 500 datapoints as NumPy array with integer values [Force sensor value].&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Main_label_string:&amp;lt;/strong&amp;gt; Quality class (&amp;lt;em&amp;gt;OK&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;Missing Strands&amp;lt;/em&amp;gt;, or &amp;lt;em&amp;gt;Crimped Insulation&amp;lt;/em&amp;gt;) manually assigned by the authors based on the preparation steps, as strings.&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Main_label_encoded:&amp;lt;/strong&amp;gt; Encoded quality classes (&amp;lt;em&amp;gt;OK&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;Missing Strands&amp;lt;/em&amp;gt;, or &amp;lt;em&amp;gt;Crimped Insulation&amp;lt;/em&amp;gt;) as integers from 0 to 2.&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Sub_label_string:&amp;lt;/strong&amp;gt; Fine-grained quality class (&amp;lt;em&amp;gt;OK&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;One Missing Strand&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;Two Missing Strands&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;Three Missing Strands&amp;lt;/em&amp;gt;, or &amp;lt;em&amp;gt;Crimped Insulation&amp;lt;/em&amp;gt;) manually assigned by the authors based on the preparation steps, as strings.&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Sub_label_encoded:&amp;lt;/strong&amp;gt; Encoded sub-classes (&amp;lt;em&amp;gt;OK&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;One Missing Strand&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;Two Missing Strands&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;Three Missing Strands&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;Crimped Insulation&amp;lt;/em&amp;gt;) as integers from 0 to 4.&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;Binary_label_encoded:&amp;lt;/strong&amp;gt; Binary encoded quality classes (&amp;lt;em&amp;gt;OK&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;NOK&amp;lt;/em&amp;gt;) for anomaly detection, manually assigned by the authors as integers from 0 to 1.&amp;lt;/li&amp;gt;
  &amp;lt;li&amp;gt;&amp;lt;strong&amp;gt;CFM_label_encoded:&amp;lt;/strong&amp;gt; Binary encoded quality classes (&amp;lt;em&amp;gt;OK&amp;lt;/em&amp;gt;, &amp;lt;em&amp;gt;NOK&amp;lt;/em&amp;gt;) assigned by the crimp force monitoring system as integers from 0 to 1.&amp;lt;/li&amp;gt;
&amp;lt;/ul&amp;gt;


&amp;lt;p&amp;gt;This dataset is a valuable resource for researchers and practitioners in manufacturing engineering, computer science, and data science who are working at the intersection of quality control in manufacturing and machine learning.&amp;lt;/p&amp;gt;</description>
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