While industrial additive manufacturing, often known as 3D printing,
has progressed to greater technological heights
, quality control and monitoring of manufacturing errors remain a critical concern.
3D printing is useful in a variety of industries, including, aerospace,
biomedical, automotive, and others,
since it allows for the low-cost fabrication of items with complicated geometry,
internal structural details, and individually tailored designs in small production batches.
However, at present,
3D printing’s ability to make complex
designs with minimum material waste
cannot be fully harnessed when making high-quality parts
that must meet stringent specifications.
Various manufacturing faults might jeopardize the uniformity, dependability,
and performance of 3D-printed materials, depending on
the kind of 3D printing technology used in the production cycle.
The quality of the 3D printing process is influenced by
a multitude of elements.
These can be caused by
the quality of the source materials employed
(plastic filaments, metal powder, or liquid photopolymers) or by process issues like over- and under-extrusion,
gas pockets in the sintered material, layer separation (lack of adhesion), and so on.
In most cases, these flaws cause the manufactured item to become more porous and have poor mechanical qualities.
Creating Process Control
First and foremost, the quality of the material used determines whether or not 3D-printed components fulfil the required criteria. For most additive manufacturing enterprises, source material quality management is a constant concern.
Furthermore, the entire additive manufacturing workflow
, from the initial design to 3D printing and postprocessing
, is filled with factors that might alter the quality of the finished product.
For example,
the filament extrusion speed or the route and intensity of the sintering laser (in a direct metal sintering process) (in a fused filament fabrication process).
The design of the support structures,
as well as the number of times the metal powder
has been recovered and reused,
are all possible considerations.
Currently,
the most popular method for optimizing the 3D printing process to ensure uniform and repeatable component quality is trial and error.
However, t
his approach usually involves the repetition of production steps and extensive testing of the final part.
The end process is an expensive and sloppy method
that reduces the major advantage of 3D printing: -cost-efficient production of small units
Eliminating Human errors
Almost all 3D printing processes require human intervention. from the conception stage to the finished product.
Experts in 3D printing realize the need for more sustainable and viable processing and quality control approaches.
Using artificial intelligence (AI) algorithms to automate the most crucial processes in the 3D printing process is one of the most encouraging approaches to attaining this
.
Higher Producrivity in Prefabrication
By applying machine learning algorithms in the so-called generative design technique,
AI-based software packages such as Netfabb from Autodesk and Agile Metal Technology from Sculpteo (a BASF subsidiary) can analyse and produce design files for 3D printing.
Manufacturers can enter their desired design parameters,
and AI will analyze them to discover the most cost-effective manufacturing path.
Closed-Loop Control and Automated Defect Detection
Closed-loop control systems have long been a significant aim for 3D printing engineers,
and they’ve become increasingly attainable in recent years because of the rapid development of advanced AI applications.
Researchers at GE’s Niskayuna Additive Research Lab created a proprietary machine-learning platform
that uses high-resolution cameras to monitor the printing process layer by layer and detect streaks, pits,
holes, and other problems that are typically invisible to the naked eye.
The data is compared in real-time to a pre-recorded
flaw database utilizing computer tomography (CT) imaging.
The AI system may be trained to forecast
difficulties and detect flaws
throughout the printing process using the high-resolution image and CT scan data.
Ai Build is a London-based business specializing in the development of automated
AI-based 3D printing technology used a similar integrated machine learning technique to produce a smart extruder for additive manufacturing.
It’s a high-precision attachment for industrial robotic arms that allows them to 3D print huge objects quickly and accurately.
The smart 3D printing extruder can identify any difficulties and make autonomous decisions to
get the highest possible print quality by combining advanced AI algorithms with real-time
sensor data processing.
Artificial Intelligence Develops New 3D Printing Materials
Intellegens, a University of Cambridge spin-off, uses machine learning algorithms in its Alchemite platform to develop new 3D printing materials.
The company has successfully employed the AI platform to develop
a unique nickel-based alloy that can be manufactured using direct laser deposition.
Alchemite’s deep learning capabilities allow it to analyze a massive library of material attributes to determine the best alloy composition for a given application.
Up Until this point,
the goal of using AI in 3D printing has been to improve design,
increase the efficiency of 3D printing processes,
and enable autonomous manufacture.
Advanced AI solutions will soon be able to assist in reducing design complexity,
lowering the expertise required for additive manufacturing sectors,
and improving field cybersecurity.
For further reading, you can check out more research papers on Nature’s sciences and this research paper
