Development of 3D printed rapid tooling for micro-injection moulding

High requirements imposed by the competitive industrial environment determine the development directions of applied manufacturing methods. 3D printing technology, also known as additive manufacturing (AM), currently being one of the most dynamically developing production methods, is increasingly used in many different areas of industry. Nowadays, apart from the possibility of making prototypes of future products, AM is also used to produce fully functional machine parts, which is known as Rapid Manufacturing and also Rapid Tooling. Rapid Manufacturing refers to the ability of the software automation to rapidly accelerate the manufacturing process, while Rapid Tooling means that a tool is involved in order to accelerate the process. Abrasive processes are widely used in many industries, especially for machining hard and brittle materials such as advanced ceramics. This paper presents a review on advances and trends in contemporary abrasive machining related to the application of innovative 3D printed abrasive tools. Examples of abrasive tools made with the use of currently leading AM methods and their impact on the obtained machining results were indicated. The analyzed research works indicate the great potential and usefulness of the new constructions of the abrasive tools made by incremental technologies. Furthermore, the potential and limitations of currently used 3D printed abrasive tools, as well as the directions of their further development are indicated.

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A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07252-7 ◽

2021 ◽

Author(s):

Mert Gülçür ◽

Ben Whiteside

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Multiple Linear Regression ◽

Injection Moulding ◽

Industry 4.0 ◽

Supervised Machine Learning ◽

Process Conditions ◽

Learning Models ◽

Micro Injection Moulding ◽

Machine Learning Models

AbstractThis paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.

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APPLICATION OF 3D PRINTING TECHNOLOGY FOR RAPID TOOLING IN SHEET METAL FORMING

Fundamental and Applied Problems of Engineering and Technology ◽

10.33979/2073-7408-2020-341-3-20-30 ◽

2020 ◽

Vol 3 ◽

pp. 20-30

Author(s):

M.A. SEREZHKIN ◽

D.O. KLIMYUK ◽

A.I. PLOKHIKH

Keyword(s):

3D Printing ◽

Sheet Metal ◽

Sheet Metal Forming ◽

Metal Forming ◽

Rapid Tooling ◽

Printing Technology ◽

3D Printing Technology ◽

3D Printed ◽

Printed Materials ◽

Printing Parameters

The article presents the study of the application of 3D printing technology for rapid tooling in sheet metal forming for custom or small–lot manufacturing. The main issue of the usage of 3D printing technology for die tooling was discovered. It is proposed to use the method of mathematical modelling to investigate how the printing parameters affect the compressive strength of FDM 3D–printed parts. Using expert research methods, the printing parameters most strongly affecting the strength of products were identified for further experiments. A method for testing the strength of 3D–printed materials has been developed and tested.

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