• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

聚合物材料注塑成型制造过程的电力消耗估计:实证模型与应用

Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application.

作者信息

Elduque Ana, Elduque Daniel, Pina Carmelo, Clavería Isabel, Javierre Carlos

机构信息

BSH Electrodomésticos España S. A., Avda. de la Industria, 49, 50016 Zaragoza, Spain.

i+, Department of Mechanical Engineering, EINA, University of Zaragoza, C/María de Luna, 3, 50018 Zaragoza, Spain.

出版信息

Materials (Basel). 2018 Sep 16;11(9):1740. doi: 10.3390/ma11091740.

DOI:10.3390/ma11091740
PMID:30223602
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6164802/
Abstract

Polymer injection-molding is one of the most used manufacturing processes for the production of plastic products. Its electricity consumption highly influences its cost as well as its environmental impact. Reducing these factors is one of the challenges that material science and production engineering face today. However, there is currently a lack of data regarding electricity consumption values for injection-molding, which leads to significant errors due to the inherent high variability of injection-molding and its configurations. In this paper, an empirical model is proposed to better estimate the electricity consumption and the environmental impact of the injection-molding process. This empirical model was created after measuring the electricity consumption of a wide range of parts. It provides a method to estimate both electricity consumption and environmental impact, taking into account characteristics of both the molded parts and the molding machine. A case study of an induction cooktop housing is presented, showing adequate accuracy of the empirical model and the importance of proper machine selection to reduce cost, electricity consumption, and environmental impact.

摘要

聚合物注塑成型是生产塑料制品最常用的制造工艺之一。其电力消耗对成本以及环境影响有很大影响。降低这些因素是材料科学和生产工程当今面临的挑战之一。然而,目前缺乏关于注塑成型电力消耗值的数据,由于注塑成型及其配置固有的高度变异性,这会导致重大误差。本文提出了一个经验模型,以更好地估计注塑成型过程的电力消耗和环境影响。这个经验模型是在测量了广泛的零部件的电力消耗之后创建的。它提供了一种估计电力消耗和环境影响的方法,同时考虑了成型零部件和成型机的特性。给出了一个电磁炉外壳的案例研究,展示了经验模型的足够准确性以及正确选择机器对于降低成本、电力消耗和环境影响的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/9c6423405e80/materials-11-01740-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/f6d4e8f12eda/materials-11-01740-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/20d26380ccaa/materials-11-01740-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/c7e148435246/materials-11-01740-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/9097d9d77644/materials-11-01740-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/9c6423405e80/materials-11-01740-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/f6d4e8f12eda/materials-11-01740-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/20d26380ccaa/materials-11-01740-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/c7e148435246/materials-11-01740-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/9097d9d77644/materials-11-01740-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cdc/6164802/9c6423405e80/materials-11-01740-g005.jpg

相似文献

1
Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application.聚合物材料注塑成型制造过程的电力消耗估计:实证模型与应用
Materials (Basel). 2018 Sep 16;11(9):1740. doi: 10.3390/ma11091740.
2
Correction: Elduque, A., et al. Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application.更正:埃尔杜克,A.等人。聚合物材料注塑成型制造过程的电力消耗估算:实证模型与应用。
Materials (Basel). 2020 Jun 3;13(11):2548. doi: 10.3390/ma13112548.
3
Experimental Development of an Injection Molding Process Window.注塑成型工艺窗口的实验开发
Polymers (Basel). 2023 Jul 28;15(15):3207. doi: 10.3390/polym15153207.
4
Manufacturing Signatures of Injection Molding and Injection Compression Molding for Micro-Structured Polymer Fresnel Lens Production.用于微结构聚合物菲涅耳透镜生产的注塑成型和注射压缩成型的制造特征
Micromachines (Basel). 2018 Dec 10;9(12):653. doi: 10.3390/mi9120653.
5
Injection Barrel/Nozzle/Mold-Cavity Scientific Real-Time Sensing and Molding Quality Monitoring for Different Polymer-Material Processes.用于不同聚合物材料工艺的注料筒/喷嘴/模具型腔科学实时感应和成型质量监测。
Sensors (Basel). 2022 Jun 24;22(13):4792. doi: 10.3390/s22134792.
6
Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding.基于生成式机器学习的注塑成型能量与质量多目标工艺参数优化
Environ Sci Pollut Res Int. 2023 Apr;30(18):51518-51530. doi: 10.1007/s11356-023-26007-3. Epub 2023 Feb 22.
7
Hybrid Process Chain for the Integration of Direct Ink Writing and Polymer Injection Molding.用于直接墨水书写与聚合物注塑成型集成的混合工艺链
Micromachines (Basel). 2020 May 18;11(5):509. doi: 10.3390/mi11050509.
8
Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network.基于多层感知器神经网络的注塑成型质量预测
Polymers (Basel). 2020 Aug 12;12(8):1812. doi: 10.3390/polym12081812.
9
Pressure Analysis of Dynamic Injection Molding and Process Parameter Optimization for Reducing Warpage of Injection Molded Products.动态注塑成型的压力分析及用于减少注塑产品翘曲的工艺参数优化
Polymers (Basel). 2017 Mar 7;9(3):85. doi: 10.3390/polym9030085.
10
Intelligent Predicting of Product Quality of Injection Molding Recycled Materials Based on Tie-Bar Elongation.基于拉杆伸长量的注塑成型再生材料产品质量智能预测
Polymers (Basel). 2022 Feb 10;14(4):679. doi: 10.3390/polym14040679.

引用本文的文献

1
Energy Consumption Prediction of Injection Molding Process Based on Rolling Learning Informer Model.基于滚动学习Informer模型的注塑成型过程能耗预测
Polymers (Basel). 2024 Nov 2;16(21):3097. doi: 10.3390/polym16213097.
2
Predictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks.注塑成型质量评估的预测方法:线性回归与神经网络对比
Polymers (Basel). 2023 Sep 28;15(19):3915. doi: 10.3390/polym15193915.
3
Prediction of electricity energy consumption including COVID-19 precautions using the hybrid MLR-FFANN optimized with the stochastic fractal search with fitness distance balance algorithm.

本文引用的文献

1
Life Cycle Assessment for Proton Conducting Ceramics Synthesized by the Sol-Gel Process.溶胶-凝胶法合成质子传导陶瓷的生命周期评估
Materials (Basel). 2014 Sep 16;7(9):6677-6685. doi: 10.3390/ma7096677.
2
Life Cycle Assessment of Completely Recyclable Concrete.完全可回收混凝土的生命周期评估
Materials (Basel). 2014 Aug 21;7(8):6010-6027. doi: 10.3390/ma7086010.
3
The Influence of Different Recycling Scenarios on the Mechanical Design of an LED Weatherproof Light Fitting.不同回收方案对LED防雨灯具机械设计的影响
使用基于适应度距离平衡算法的随机分形搜索优化的混合MLR-FFANN预测包括新冠疫情防控措施在内的电能消耗
Concurr Comput. 2022 Jul 10;34(15):e6947. doi: 10.1002/cpe.6947. Epub 2022 Mar 22.
4
A Critical Review on Wood-Based Polymer Composites: Processing, Properties, and Prospects.木质基聚合物复合材料的批判性综述:加工、性能与前景
Polymers (Basel). 2022 Jan 31;14(3):589. doi: 10.3390/polym14030589.
5
Correction: Elduque, A., et al. Electricity Consumption Estimation of the Polymer Material Injection-Molding Manufacturing Process: Empirical Model and Application.更正:埃尔杜克,A.等人。聚合物材料注塑成型制造过程的电力消耗估算:实证模型与应用。
Materials (Basel). 2020 Jun 3;13(11):2548. doi: 10.3390/ma13112548.
Materials (Basel). 2014 Aug 11;7(8):5769-5788. doi: 10.3390/ma7085769.