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Related Concept Videos

Plasticizers01:31

Plasticizers

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Water-reducers, or plasticizers, are chemical admixtures used in concrete to improve strength and workability. These additives reduce the water-cement ratio without compromising workability, lower the cement content while maintaining the same workability, or increase workability to assist concrete placement in inaccessible areas.
Plasticizers function by using surface-active agents to create repulsive electrostatic forces between cement particles. This dispersion enhances the concrete's...
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Superplasticizers01:30

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Superplasticizers are advanced admixtures that enhance the workability of concrete by lowering the water content without compromising the strength of the material. These substances are highly effective water reducers, improving concrete flow, making it easier to work with, and enabling concrete to reach inaccessible areas or densely reinforced sections without mechanical vibration. The key components in superplasticizers are either sulfonated melamine or naphthalene formaldehyde condensates,...
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When materials are subjected to forces that surpass their yield strength, they undergo a process known as plastic deformation. This results in a permanent alteration or strain in their structure. This concept can be specifically applied to circular shafts, where the deformation leads to a change in its shape. The precise evaluation of this plastic deformation requires understanding the stress distribution within the circular shaft, which is achieved by calculating the maximum shearing stress in...
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Quality Control01:05

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Quality Assurance01:19

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Related Experiment Video

Updated: Aug 29, 2025

A Soft Tooling Process Chain for Injection Molding of a 3D Component with Micro Pillars
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Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts.

Saeid Saeidi Aminabadi1, Paul Tabatabai2, Alexander Steiner2

  • 1Department of Polymer Engineering and Science, Montanuniversitaet Leoben, Otto Gloeckel str. 2, 8700 Leoben, Austria.

Polymers
|September 9, 2022
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Summary

This study introduces an automated Industry 4.0 injection molding system using AI for real-time quality control. The system successfully adjusts machine parameters to minimize defects and ensure zero-defect production.

Keywords:
AI quality controlclosed-loop quality controldeep neural networkdeep residual learningdimensional features predictionin-line quality controlinjection molding of plasticspredictive controlsurface quality predictionweight prediction

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Industry 4.0

Background:

  • Automatic in-line process quality control is vital for production efficiency in injection molding.
  • Industry 4.0 principles drive productivity and zero-defect goals in manufacturing.
  • Minimizing scrap rates is a key objective in the injection molding industry.

Purpose of the Study:

  • To develop a fully automated, closed-loop injection molding (IM) system compliant with Industry 4.0 standards.
  • To integrate AI for real-time process control and quality management.
  • To enhance production efficiency and achieve zero-defect outcomes.

Main Methods:

  • A closed-loop IM setup with OPC UA communication was established.
  • Inline measurements, data analysis, and an AI control system were implemented.
  • ResNet-18 neural network and eight machine learning models were used for quality prediction and sensor data analysis.

Main Results:

  • The AI control system, based on heuristic model predictive control (MPC), successfully adjusted machine parameters (injection speed, holding pressure, mold temperature).
  • Geometry and surface quality control were effectively demonstrated.
  • Predictive models accurately forecast part quality and sensor data.

Conclusions:

  • The developed Industry 4.0 compliant system enables automated, real-time quality control in injection molding.
  • The AI-driven approach significantly contributes to minimizing scrap and achieving high-quality production.
  • This system provides a robust framework for advanced manufacturing processes.