Geometrical metrology for metal additive manufacturing

https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1759&context=eispapers1

A review of modern advancements in micro drilling techniques

The mechanical testing and performance analysis of polymer-fibre composites prepared through the additive manufacturing

The forming system of wire and arc additive manufacture (WAAM) based on cold metal transfer (CMT) is a high build rate system for production of near-net shape components layer by layer, which is composed of industrial robot operation system and 3D path simulation software. In the 3D path simulation software, the working layout of the off-line virtual robot is carried for the imported three-dimensional model to screen the model wall thickness, correct process library, set process parameters, slice, layer, plan deposition path, form simulation and upload program to execution system. Among the whole system, the 3D path simulation software provides essential database for process control and an innovative planning path to coordinate industrial robot platform to build parts layer by layer. Furthermore, the experiment is also performed to study the stability of aluminum alloy forming using the WAAM-CMT system by establishing different experimental models and changing process parameters and process modes of industrial robot and Fronius digital welding machine embedded on robot operation platform.

Cold Metal Transfer (CMT) Based Wire and Arc Additive Manufacture (WAAM) System

Additive Manufacturing (AM) is becoming data-intensive while increasingly generating newly available data. The availability of AM data provides Design for AM (DfAM) with a newfound opportunity to construct AM design rules with improved understanding of AM’s influence on part qualities. To seize the opportunity, this paper proposes a novel approach for AM design rule construction based on machine learning and knowledge graph. First, this paper presents a framework that enables i) deploying machine learning for extracting knowledge on predictive additive manufacturability from data, ii) adopting ontology with knowledge graphs as a knowledge base for storing both a priori and newfound AM knowledge, and iii) reasoning with knowledge for deriving data-driven prescriptive AM design rules. Second, this paper presents a methodology that constructs knowledge on predictive additive manufacturability and prescriptive AM design rules. In the methodology, we formalize knowledge representations, extractions, and reasoning, which enhances automated and autonomous construction and improvements of AM design rules. The methodology then employs a machine learning algorithm of Classification and Regression Tree on measurement data from National Institute of Standards and Technology for construction of a Laser Powder Bed Fusion-specific design rule for overhang features. This work supports AI related decision-making in additive manufacturability analysis and (re-)design for AM and guides machine learning to addressing problems related to AM design rules. This work is also meaningful as it provides sharable AM design rule knowledge with the AM society.

Machine learning and knowledge graph based design rule construction for additive manufacturing

All the resources involved in micromilling operations (machine tool, tool, fixture, workpiece) have to be accurately managed to improve the final workpiece quality. Special attention has to be paid also to system thermal stability, micromilling process parameters and machining strategies, workpiece and tool measurement accuracy. The present paper proposes an easy and industry-oriented procedure to maximize the achievable workpiece accuracy working in absolute coordinates. Accurate plane surfaces with a strict flatness deviation are machined on a CuNi12Zn30Pb1 benchmark workpiece to demonstrate the procedure effectiveness.

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Lara Rebaioli

Papers

Improvement of Procedures for High Accuracy Micromilling of Flat Surfaces