Procedia CIRP最新文献_第10页

A prediction method of surface geometric deviation for additive manufacturing parts based on knowledge-integrated deep learning algorithm 基于知识集成深度学习算法的增材制造零件表面几何偏差预测方法

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.005

Zhicheng Huang , Yingyu Cao , Yuda Cao , Kai Guo , Lihong Qiao

{"title":"A prediction method of surface geometric deviation for additive manufacturing parts based on knowledge-integrated deep learning algorithm","authors":"Zhicheng Huang , Yingyu Cao , Yuda Cao , Kai Guo , Lihong Qiao","doi":"10.1016/j.procir.2024.10.005","DOIUrl":"10.1016/j.procir.2024.10.005","url":null,"abstract":"<div><div>Compared with traditional machining processes, additive manufacturing (AM) has received widespread attention in recent years because of its high degree of modeling freedom. However, due to the multiple manufacturing errors and complex physical state changes involved in the process, the geometric deviation on the AM part surface is a challenge for controlling product geometrical quality. To address this problem, data-driven machine learning (ML) techniques have been widely studied in product quality controlling. However, traditional ML greatly depends on the training sample data, and suffers the risk of violating physical mechanisms due to the lack of domain knowledge. In order to take the best advantage of domain knowledge, prior information and deep learning algorithm, this paper proposes a knowledge-integrated deep learning algorithm and constructs the geometric deviation prediction model of the AM part surface. After that, the method was verified with design of experiments. The results show that compared with the data-driven neural network (DDNN), the knowledge-integrated neural network (KINN) has fewer iterations during the training process, less sample data requirement and more accurate prediction results.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"129 ","pages":"Pages 19-24"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Price competition for service provision with different bargaining abilities 不同议价能力的服务价格竞争

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.08.253

Jianyong Teng , Ryuichiro Ishikawa

引用次数: 0

Digital Battery Cell Manufacturing Systems: Approaching a scalable and versatile IT Architecture Design 数字电池芯制造系统：接近可扩展的多功能 IT 架构设计

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.119

Leon Mohring , Wilhelm Jaspers , Arno Schmetz , David Roth , Nils Christian Hamacher , Achim Kampker

{"title":"Digital Battery Cell Manufacturing Systems: Approaching a scalable and versatile IT Architecture Design","authors":"Leon Mohring , Wilhelm Jaspers , Arno Schmetz , David Roth , Nils Christian Hamacher , Achim Kampker","doi":"10.1016/j.procir.2024.10.119","DOIUrl":"10.1016/j.procir.2024.10.119","url":null,"abstract":"<div><div>The transition from a fossil-fuel powered economy towards decentralized renewable energy sources and electric mobility creates a global demand for battery cells. As cell manufacturers ramp up production capacity, they are facing the challenge of scaling their IT systems in pace with the growing demand. Innovations such as new cell types and enhanced production technology as well as incorporating ever-evolving data-driven methods, e.g., Artificial intelligence, require a versatile IT architecture, capable of adapting. In the light of these challenges, this paper introduces a methodology aimed at building an IT architecture tailored to the requirements of a given battery cell manufacturing use case. It provides different approaches with respect to several dimensions, considering among others the requirements of shopfloor connectivity, data acquisition and storage strategies, as well as IT systems integration design. Further covered requirements deal with computational capacities close to the shopfloor, real-time aspects, data ingestion and integration, and dynamic resource allocation. This paper presents the application of the methodology to a multi-site high-scale production at Fraunhofer FFB, describing the resulting IT architecture. The factories combined will host four manufacturing lines producing a GWh-scale battery cell output per year. To sustain the workload on the FFB IT architecture incurred by this throughput, the design of the IT architecture pivots away from a strictly hierarchical structure, bringing critical systems and databases closer to the shopfloor. This and other design choices have been made based on the developed methodology. Overall, the presented methodology and the derived FFB IT architecture show a path towards building a battery cell production IT architecture capable of scaling and rapidly adapting to new technologies.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"130 ","pages":"Pages 492-497"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The role of maintenance in company-specific production systems 维护在公司特定生产系统中的作用

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.133

Anderson Leal , Jon Bokrantz , Anders Skoogh

{"title":"The role of maintenance in company-specific production systems","authors":"Anderson Leal , Jon Bokrantz , Anders Skoogh","doi":"10.1016/j.procir.2024.10.133","DOIUrl":"10.1016/j.procir.2024.10.133","url":null,"abstract":"<div><div>Company-Specific Production Systems (XPS) are in use in several manufacturing companies around the globe, as a reference to variations of the Toyota Production System. XPS is a continuous improvement program responsible for increasing the general performance of companies. Maintenance has an important contribution to XPS by delivering technical availability at a rational cost. However, the connections between all the elements of the XPS and the corresponding contributions from maintenance are not crystal clear. Providing such clarity could increase the focus on improvements that would create real benefits for the company. The current study aims to bridge the XPS literature to maintenance applications, thereby substantiating the role of maintenance in XPS. Firstly, a theoretical framework of XPS is created and explained based on previous literature. The framework outlines three core elements of an XPS: content, management, and outcomes. Also, it presents the interconnections between the elements. Secondly, the framework acts as a guide to an empirical study at an automotive company in Sweden. The study maps the role of maintenance and its contribution to the XPS in place. For each of the XPS elements, a maintenance correspondent was selected and connected to the XPS framework. Thirdly, based on the results of the empirical study, the paper proposes a set of critical research directions, both guiding the design and execution of future research studies and supporting the long-term competitiveness of the company.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"130 ","pages":"Pages 584-590"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Concept drift monitoring for industrial load forecasting with artificial neural networks 利用人工神经网络进行工业负荷预测的概念漂移监测

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.065

Robin Zink , Borys Ioshchikhes , Matthias Weigold

引用次数: 0

Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings 工业语言图像数据集 (ILID)：针对工业环境调整视觉基础模型

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.084

Keno Moenck , Duc Trung Thieu , Julian Koch , Thorsten Schüppstuhl

{"title":"Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings","authors":"Keno Moenck , Duc Trung Thieu , Julian Koch , Thorsten Schüppstuhl","doi":"10.1016/j.procir.2024.10.084","DOIUrl":"10.1016/j.procir.2024.10.084","url":null,"abstract":"<div><div>In recent years, the upstream of Large Language Models (LLM) has also encouraged the computer vision community to work on substantial multimodal datasets and train models on a scale in a self-/semi-supervised manner, resulting in Vision Foundation Models (VFM), as, e.g., Contrastive Language–Image Pre-training (CLIP). The models generalize well and perform outstandingly on everyday objects or scenes, even on downstream tasks, tasks the model has not been trained on, while the application in specialized domains, as in an industrial context, is still an open research question. Here, fine-tuning the models or transfer learning on domain-specific data is unavoidable when objecting to adequate performance. In this work, we, on the one hand, introduce a pipeline to generate the Industrial Language-Image Dataset (ILID) based on web-crawled data; on the other hand, we demonstrate effective self-supervised transfer learning and discussing downstream tasks after training on the cheaply acquired ILID, which does not necessitate human labeling or intervention. With the proposed approach, we contribute by transferring approaches from state-of-the-art research around foundation models, transfer learning strategies, and applications to the industrial domain.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"130 ","pages":"Pages 250-263"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Enhancement for Machine Vision and Industrial Image Processing 用于机器视觉和工业图像处理的图像增强技术

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.085

Daniel Weerts , Maren Petersen

引用次数: 0

Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment 利用机器学习预测动态电价环境下多步骤生产流程的耗电量

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.080

Muhammad Abdullah Shah , Hendro Wicaksono

{"title":"Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment","authors":"Muhammad Abdullah Shah , Hendro Wicaksono","doi":"10.1016/j.procir.2024.10.080","DOIUrl":"10.1016/j.procir.2024.10.080","url":null,"abstract":"<div><div>Rising energy costs drive a compelling demand for energy-efficient manufacturing across sectors, paralleled by increasing consumer preferences for eco-friendly products. To remain competitive, companies are actively enhancing their energy efficiency. Integrating dynamic pricing in manufacturing, aimed at optimizing renewable energy use, requires strategic adjustments in production planning for sustainability. This research highlights the importance of incorporating dynamic pricing into production planning, emphasizing the need to shift processes to time slots when the energy prices are low or optimal. This study focuses on predicting the power consumption of multi-step CNC machine operations within a production cycle. Utilizing advanced Machine Learning (ML), including neural networks, statistical, and additive models, this research found unique time series characteristics influencing model performance across production steps. A practical use case within a German manufacturing Small and Medium Enterprises (SME) demonstrates how prediction results can optimize production processes in a dynamic pricing environment, providing a blueprint for diverse machinery forecasting models. This research’s insights extend to any industry managing production schedules for multiple machines with various steps in a process cycle. Industries with high energy consumption will benefit significantly through aligning operational efficiency with environmental sustainability goals.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"130 ","pages":"Pages 226-231"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Digital Thread Framework for Implementing Intelligent Machining Applications 实现智能加工应用的数字线程框架

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.091

Jeongin Koo , Soohyun Nam , Hoon-Hee Lee , Dong Yoon Lee

{"title":"The Digital Thread Framework for Implementing Intelligent Machining Applications","authors":"Jeongin Koo , Soohyun Nam , Hoon-Hee Lee , Dong Yoon Lee","doi":"10.1016/j.procir.2024.10.091","DOIUrl":"10.1016/j.procir.2024.10.091","url":null,"abstract":"<div><div>The digital transformation of manufacturing industry has led to vast amounts of data, making effective data utilization and analysis crucial for gaining a competitive advantage. However, the diverse formats and complex structures of manufacturing data pose significant challenges to data integration and interoperability. This paper presents a digital thread framework for intelligent machining applications based on a common process planning data model derived from the ISO 14649 standard. The framework integrates various data used in the process planning stage and enables contextual connection of data generated at each stage of the machining process, including virtual machining, machining monitoring, and geometric dimensioning and tolerancing (GD&T). The common data model is constructed by parsing the EXPRESS data model from ISO 14649-1/11 into an OpenAPI Specification JSON format and generating classes in individual programming languages. The digital thread focuses on connecting and restoring the context of operation data, extending the ISO 14649 data model to incorporate tool and equipment information for various applications. The monitoring data is synchronized with the virtual machining data, and the monitoring reference information is mapped to the digital thread project data. The effectiveness of the proposed framework is demonstrated through a reference chattering application, which utilizes parameters from the stability lobe diagram (SLD), machine tool, and virtual machining data. The framework facilitates data analysis and utilization by contextually connecting data generated at each stage of the machining process, ultimately supporting the development of intelligent applications based on monitoring data.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"130 ","pages":"Pages 301-306"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Deployment of Machine Learning Models in Manufacturing and Industrial Environments using ROS 使用 ROS 在制造和工业环境中高效部署机器学习模型

Procedia CIRP Pub Date : 2024-01-01 DOI: 10.1016/j.procir.2024.10.074

Marvin Frisch , Jan Baumgärtner , Imanuel Heider , Alexander Puchta , Jürgen Fleischer

{"title":"Efficient Deployment of Machine Learning Models in Manufacturing and Industrial Environments using ROS","authors":"Marvin Frisch , Jan Baumgärtner , Imanuel Heider , Alexander Puchta , Jürgen Fleischer","doi":"10.1016/j.procir.2024.10.074","DOIUrl":"10.1016/j.procir.2024.10.074","url":null,"abstract":"<div><div>This paper presents a deployment concept that aims to overcome the challenges in the implementation of Machine Learning (ML) models in manufacturing and industrial environments. In these contexts, robots are not typically viewed as production machines. However, the potential for applying advanced techniques such as condition monitoring extends beyond production lines to encompass robotic systems. As a result, there arises a need for a modular solution that integrates into the existing ecosystem while accommodating the requirements of robotic environments. By embracing modularity and interoperability, our proposed deployment concept not only addresses the challenges specific to industrial robotics but also fosters a holistic approach to enhancing operational efficiency and performance in diverse manufacturing settings.</div><div>For this, an easily customizable and adjustable system that handles both data acquisition and data transfer is needed. By using the Robot Operating System (ROS) for all necessary data handling, we achieve a highly modular, efficient, and easy-to-use low-code deployment pipeline. Our approach splits the different processing steps into separate nodes and automatically sets up all necessary communication channels, achieving high interchangeability and a quick time-to-deploy. The approach is explained in detail and demonstrated for the real use case of deploying models to monitor handling robots.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"130 ","pages":"Pages 188-193"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0