Evidence map›Paper›PMID 31779238›Full record

ArticleMaterials (Basel, Switzerland)2019

Tool Life Performance of Injection Mould Tooling Fabricated by Selective Laser Melting for High-Volume Production.

Mennatallah F El Kashouty, Allan E W Rennie, Mootaz Ghazy

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mennatallah F El KashoutyAl Fouad Co. for Automotive Spare Parts, Alexandria 23722, Egypt.
Allan E W RennieEngineering Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK.
Mootaz GhazyIndustrial and Management Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Abu Qir Campus, Alexandria 21913, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid Tooling processes are developing and proving to be a reliable method to compete with subtractive techniques for tool making. This paper investigates large volume production of components produced from Selective Laser Melting (SLM) fabricated injection moulding tool inserts. To date, other researchers have focused primarily on investigating the use of additive manufacturing technology for injection moulding for low-volume component production rather than high volume production. In this study, SLM technology has been used to fabricate four Stainless Steel 316L tool inserts of a similar geometry for an after-market automotive spare part. The SLM tool inserts have been evaluated to analyse the maximum number of successful injections and quality of performance. Microstructure inspection and chemical composition analysis have been investigated. Performance tests were conducted for the four tool inserts before and after injection moulding in the context of hardness testing and dimensional accuracy. For the first reported time, 150,000 injected products were successfully produced from the four SLM tool inserts. Tool inserts performance was monitored under actual operating conditions considering high-level demands. In the scope of this research, SLM proved to be a dependable manufacturing technique for most part geometries and an effective alternative to subtractive manufacturing for high-volume injection moulding tools for the aftermarket automotive sector.

Indexed as

Additive ManufacturingAutomotive IndustryInjection MouldingRapid ToolingSelective Laser MeltingTool Inserts

Identifiers

PMID31779238
PMCPMC6926645

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.