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

In recent years, additive manufacturing (AM) has undergone a rapid growth, therefore several processes based on different working principles (e.g. photopolymerization, sintering, extrusion, material jetting, etc) are now available and allow to manufacture parts using a wide range of materials. Consequently, the so-called benchmark artifacts are necessary to assess the capabilities and limitations of each AM process or to compare the performance of different processes. This paper focuses on the benchmark artifacts for evaluating the geometrical performance of AM processes and proposes an extensive review of the available literature, analyzing the design of such test parts in detail. The investigated test parts are classified according to the process aspect that they are able to evaluate (dimensional/geometrical accuracy, repeatability, minimum feature size) and the combination AM process/materials for which they have been used. In addition, the paper draws a summary of guidelines to design benchmark artifacts for geometrical performance evaluation.

A review on benchmark artifacts for evaluating the geometrical performance of additive manufacturing processes

Micromilling is one of the most versatile tooling processes being able to effectively manufacture three-dimensional complex features on moulds and dies achieving a good accuracy performance. Typical and challenging features for these microcomponents are high aspect ratio thin walls but no systematic approaches, as the one presented in this paper, exist in literature dealing with the relationship between nominal workpiece characteristics/process parameters, cutting forces, and workpiece quality. The present study focuses on 0.4 % carbon steel (C40) thin wall micromilling and evaluates two approaches for the thin wall geometrical quality improvement: a direct approach (relating process parameters, material and nominal workpiece characteristics to the workpiece quality characteristics) and a force-based approach (relating the same quantities through the cutting forces determination). The force-based approach relates the process parameters to the workpiece quality introducing physical quantities as cutting forces, which are suitable for monitoring and controlling purposes. A suitable experimental campaign has been designed in order to statistically analyze the cutting force responses, and a proper technique (ANalysis of COVAriance) has been applied to remove the tool wear effect. The relationship between cutting forces and workpiece quality has been quantitatively studied; this way, the feasibility of a general approach able to meet tolerances by controlling forces has been demonstrated.

Thin wall geometrical quality improvement in micromilling

Complex parts can be successfully manufactured by means of Additive Manufacturing (AM) techniques based on thermoplastic polymer extrusion, whose use for mass production is restricted by their slow printing speed. To address this limitation, a flexible AM platform for big plastic objects has been realized mounting an industrial screw-based extruder on an anthropomorphic robot. An experimental campaign has been performed to set a suitable range of relevant process parameters, with the aim of ensuring a regular deposited layer geometry. Moreover, a closed-loop control strategy has been developed to correct the robot height based on data measured during the material deposition, thus further improving the process parameter setting and compensating the material shrinkage or other unexpected defects. Eventually, an online re-slicing algorithm has been implemented to preserve the desired height of the manufactured object, despite the layer height changes. The proposed approach allows a deposition flow rate up to 1250 cm3/h within a building volume limited only by the robot workspace.

Lara Rebaioli

Papers

A review on benchmark artifacts for evaluating the geometrical performance of additive manufacturing processes

Thin wall geometrical quality improvement in micromilling

Process parameters tuning and online re-slicing for robotized additive manufacturing of big plastic objects

Microcutting Force Prediction by Means of a Slip-line Field Force Model☆

Robotic AM System for Plastic Materials: Tuning and On-line Adjustment of Process Parameters