The dataset is constructed from images of defective production items that were provided and annotated by Kolektor Group d.o.o.. The images were captured in a controlled industrial environment in a real-world case.

The dataset consists of 399 images at 500 x ~1250 px in size.

Please cite our paper published in the Journal of Intelligent Manufacturing when using this dataset:

@article{Tabernik2019JIM, author = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and Sko{\v{c}}aj, Danijel}, journal = {Journal of Intelligent Manufacturing}, title = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}}, year = {2019}, month = {May}, day = {15}, issn={1572-8145}, doi={10.1007/s10845-019-01476-x} }

International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. 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Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

In intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and typically have an earliest and latest possible start time. We introduce and analyze four algorithmic ideas for this class of time/sequence-dependent over-subscribed scheduling problems with time windows: a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS), a new randomization strategy for neighbourhood operators, a partial sequence dominance heuristic, and a fast insertion strategy. This dataset contains the source code of our hybrid algorithm: ALNS/TPF. If you use this code, please cite the following paper: He L , De Weerdt M , Yorke-Smith N. Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm[J]. Journal of Intelligent Manufacturing, 2019, DOI: 10.1007/s10845-019-01518-4.

International Journal of Engineering and Advanced Technology FAQ - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level agreements (drafting,

# Dataset and scripts: A zone-based Wi-Fi fingerprinting indoor positioning system for factory noise mapping

## Overview

This dataset supports research on zone-based Wi-Fi fingerprinting indoor positioning systems and factory noise mapping. It includes Wi-Fi RSSI (Received Signal Strength Indicator) data, noise levels, zone information, and timestamps, which can be used to develop models for indoor positioning, zone classification, and noise mapping within factory environments.

## Dataset

1. RSSI_rawdata_4days_sample200_37zone.csv

Raw RSSI measurements collected across 37 zones over four days. Each row contains RSSI values from six access points (AP1-AP6) and the corresponding zone label.

Columns:

AP1-AP6: RSSI values (in dBm) for six Wi-Fi access points.

label: Zone ID (integer) where the data was collected.

Purpose:

Used for training and evaluating machine learning models for indoor positioning and zone classification.

2. RSSI_fluctuate_6AP_Sample400.csv

Wi-Fi RSSI fluctuations collected from six access points (AP1-AP6) at the same location, with 400 samples to analyze signal stability.

Columns:

AP1-AP6: RSSI values (in dBm) for six access points.

Purpose:

Analyzed for signal stability and noise mapping, supporting indoor positioning and signal fluctuation evaluation in factory environments.

## Scripts

1. model.py

Processes the RSSI_rawdata_4days_sample200_37zone.csv file and evaluates machine learning model performance for indoor positioning and zone classification.

This script evaluates machine learning model performance as discussed in Section 4.3 ("ML Model Performance Evaluation").

2. logic.py

Processes the RSSI_fluctuate_6AP_Sample400.csv file to evaluate Wi-Fi signal fluctuations, supporting signal stability analysis for noise mapping and positioning.

This script analyzes Wi-Fi signal fluctuations as discussed in Section 4.2 ("Wi-Fi Signal Analysis") of the paper.

## Citation

If you use this dataset, please cite:

"A zone-based Wi-Fi fingerprinting indoor positioning system for factory noise mapping," Journal of Intelligent Manufacturing, 2025.

## License

This project is licensed under the MIT License. See the LICENSE file for details.

This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen.

In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data.

Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from one CPS component (i.e., production station) at one point in time.

The data set contains the following files

• low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators
  • *.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory
• camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution
• camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution

Check the following publications to learn more about our research using the model factory:

Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18 (pp. 95-107). Springer International Publishing.

Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 20-26). IEEE.

Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. Journal of Manufacturing Systems, 63, 575-592.

This is about 90 mins worth of data collected via the MQTT interface of the Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen. Each entry in the file corresponds to one message (as JSON object) received on a specific topic via MQTT.

The description of the MQTT interface can be found here: https://github.com/fischertechnik/txt_training_factory/blob/master/TxtSmartFactoryLib/doc/MqttInterface.md

Check the following publications to learn more about our research using the model factory:

Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18 (pp. 95-107). Springer International Publishing.

Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 20-26). IEEE.

Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. Journal of Manufacturing Systems, 63, 575-592.

Test results of spatial correlation of intelligent manufacturing enterprises.

This is the dataset used for the evaluation of Radiant: A Domain-specific Language for Detecting Process Activities from Sensor Streams in IoT, a paper submitted for review to the Internet of Things journal by Elsevier.

The dataset contains:

• a Radiant application with patterns to detect activities from CPS sensor data + a corresponding configuration file
• the corresponding, executable Siddhi apps for activity detection generated from the Radiant applications
• the IoT sensor logs (in JSON) used for evaluation
• the ground truth files (in XES) generated manually or from the workflow management system
• XES files with the activities detected from replaying the CPS sensor logs and processing them in the Siddhi apps
• Scripts to run and results of the evalution as CSV files

each for the described smart manufacturing IoTsetup and the smart healthcare IoT setup.

In the contemporary manufacturing landscape, the advent of artificial intelligence and big data analytics has been a game-changer in enhancing product quality. Despite these advancements, their application in diagnosing failure probability and risk remains underexplored. The current practice of failure risk diagnosis is impeded by the manual intervention of managers, leading to varying evaluations for identical products or similar facilities. This study aims to bridge this gap by implementing advanced data analysis techniques on maintenance data from an aluminum extruder. We have employed text embedding, dimensionality reduction, and feature extraction methods, integrating the K-means algorithm with the Silhouette Score for risk level classification. Our findings reveal that the combination of Word2Vec for embedding and Contractive Auto Encoder for dimensionality reduction and feature extraction yields high-performance results. The optimal cluster count, identified as three, achieved the highest Silhouette Score. Statistical analysis using one-way ANOVA confirmed the significance of these findings with a p-value of 5.3213 × e−6, well within the 5% significance threshold. Furthermore, this study utilized BERTopic for topic modeling to extract principal topics from each cluster, facilitating an in-depth analysis of the clusters in relation to the extruder’s characteristics. The outcome of this research offers a novel methodology for facility managers to objectively diagnose equipment failures. By minimizing subjective judgment, this approach is poised to significantly enhance the efficacy of quality assurance systems in manufacturing, leveraging the robust capabilities of artificial intelligence.

Based on the social network theory, this study utilizes knowledge absorption capacity as the mediating variable and technology turbulence as the moderating variable; furthermore, it focuses on China’s intelligent manufacturing industry data to explore the effect of the intelligent manufacturing enterprise innovation network on technology innovation performance and the regulating mechanism of technology turbulence. Based on the patent data obtained from Derwent Database (survey period: 2016–2020), the empirical analysis indicates the following: (1) Network relationship, network location, and network density are significantly and positively correlated with technology innovation performance; however, network size exerts no significant effect on technology innovation performance. (2) Network relationship strength, network location, and network density exert significantly positive effects on the two dimensions of knowledge absorption capacity, namely the In-degree and the Out-degree. Network size exerts no significant effect on knowledge absorption capacity. (3) Knowledge absorption capacity exerts a partial mediating effect on the relationship between innovation network and technology innovation performance. (4) The three dimensions of innovation network that exert a significant effect on technology innovation performance are positively correlated with the interaction terms of technology turbulence, which indicates that the interaction terms, namely innovation network and technology turbulence, exert a positive impact on technology innovation performance through knowledge absorption capacity, and that the moderating effect of technology turbulence exerts a role through knowledge absorption capacity. Finally, this study postulates implementations and policy proposals for enhancing the innovation performance of intelligent manufacturing enterprises.