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

Dead metal cap plays an important role in the microcutting process because target material piled up on the tool–chip–workpiece interface can alter the cutting geometry. The target of this study is to model and simulate the micro-orthogonal cutting process in the presence of dead metal cap in order to investigate the effects of this phenomenon on the micromachining process outputs (cutting force, thrust force and chip thickness) and stress distribution, equivalent plastic strain and temperature inside the workpiece shear zones. For this purpose, the finite element method with explicit dynamic solution and adiabatic heating effect along with arbitrary Lagrangian–Eulerian approach is used. It is shown that the finite element models with current state-of-the-art assumptions cannot take into account the dead metal cap by default. For this reason, dead metal cap is artificially introduced on the rounded tool edge in this study for carrying out a proper analysis. Several simulations with different dead metal cap...

Finite element modeling of micro-orthogonal cutting process with dead metal cap:

Scaffolds are structures used for tissue engineering. Their size, shape and geometry are customized according to the patient pathology. A deep knowledge of their properties is essential for a corre...

Relationships between size and mechanical properties of scaffold-like structures

Mechanical micromachining is a very flexible and widely exploited process, but its knowledge should still be improved since several incompletely explained phenomena affect the microscale chip removal. Several models have been developed to describe the machining process, but only some of them consider a rounded edge tool, which is a typical condition in micromachining. Among these models, the Waldorf's slip-line field model for the macroscale allows to separately evaluate shearing and ploughing force components in orthogonal cutting conditions; therefore, it is suitable to predict cutting forces when a large ploughing action occurs, as in micromachining. This study aims at demonstrating how this model is suitable also for micromachining conditions. To achieve this goal, a clear and repeatable procedure has been developed for objectively validating its force prediction performance at low uncut chip thickness (less than 50 μm) and relatively higher cutting edge radius. The proposed procedure makes the model ...

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Applicability of an orthogonal cutting slip-line field model for the microscale

Micromilling is one of the most geometrically versatile tooling processes, in fact three-dimensional features can be effectively manufactured on molds and dies achieving a good accuracy performance. Typical and challenging features for these microcomponents are high aspect ratio thin walls. The present study evaluates the effect of wall thickness, milling strategy and tool path on cutting forces in 0.4 % carbon steel (C40) thin wall micromilling. An experimental campaign has been designed in order to statistically analyse the cutting force responses and a proper technique (ANalysis of COVAriance) has been applied to remove the tool wear effect. Considerations drawn in the present paper on micromilling cutting forces have been coupled with previous knowledge of the same authors, regarding the process parameters direct link with the thin wall geometrical quality, in order to study the feasibility of a general approach able to meet tolerances by controlling forces.

Lara Rebaioli

Papers

Finite element modeling of micro-orthogonal cutting process with dead metal cap:

Relationships between size and mechanical properties of scaffold-like structures

Applicability of an orthogonal cutting slip-line field model for the microscale

Process parameters effect on cutting forces and geometrical quality in thin wall micromilling

Calibration and Validation of a Mechanistic Micromilling Force Prediction Model