Robust in-line qualification of lattice structures manufactured via laser powder bed fusion

The shape complexity enabled by AM would impose new part inspection systems (e.g., x-ray computed tomography), which translate into qualification time and costs that may be not affordable. However, the layerwise nature of the process potentially allows anticipating qualification tasks in-line and in-process, leading to a quick detection of defects since their onset stage. This opportunity is particularly attractive in the presence of lattice structures, whose industrial adoption has considerably increased thanks to AM. This paper presents a novel methodology to model the quality of lattice structures at unit cell level while the part is being built, using high resolutions images of the powder bed for in-line geometry reconstruction and identification of deviations from the nominal shape. The methodology is designed to translate complex 3D shapes into 1D deviation profiles that capture the “geometrical signature” of the cell together with the reconstruction uncertainty

Complex geometries in additive manufacturing: A new solution for lattice structure modeling and monitoring

The production of novel types of complex shapes is nowadays enabled by new manufacturing paradigms such as additive manufacturing, also known as 3D printing. The continuous increase of shape complexity imposes new challenges in terms of inspection, product qualification and process monitoring methodologies. Previously proposed methods for 2.5D free-form surfaces are no longer applicable in the presence of this kind of new full 3D geometries. This paper aims to tackle this challenge by presenting a statistical quality monitoring approach for structures that cannot be described in terms of parametric models. The goal consists of identifying out-of-control geometrical distortions by analyzing either local variations within the part or changes from part to part. The proposed approach involves an innovative solution for modeling the deviation between the nominal geometry (the originating 3D model) and the real geometry (measured via x-ray computed tomography) by slicing the shapes and estimating the deviation slice by slice. 3D deviation maps are then transformed into 1D deviation profiles enabling the use of a profile monitoring scheme for local defect detection. The feasibility and potential of this method are demonstrated by focusing on a category of complex shapes where an elemental geometry regularly repeats in space. These shapes are known as lattice structures, or metamaterials, and their trabecular shape is thought to provide innovative mechanical and functional performance. The performance of the proposed method is shown in real and simulated case studies

A novel method for in-process inspection of lattice structures via in-situ layerwise imaging

Lattice structure are among most promising geometrical features to take full advantage of the design freedom enabled by additive manufacturing. However, their several benefits can be adversely affected by local defects and geometrical deviations, which can be hardly identified via ex-situ metrology. This paper is the first to prove that lattice structure inspection can be implemented in-situ, tackling the uncertainty of in-process imaging. The method combines powder bed image segmentation with robust statistical modelling to translate the in-line 3D geometry reconstruction into a 1D representation of unit cell's properties. Results demonstrate the agreement between in-situ modelling and ex-situ ground-truth inspections

Fingerprint analysis for machine tool health condition monitoring

One of the pillars of the smart factory concept within the Industry 4.0 paradigm is the capability to monitor the health conditions of production systems and their critical components in a continuous and effective way. This could be enabled through the implementation of innovative diagnosis, prognosis and predictive maintenance actions. A wide literature has been devoted to methodologies to monitor the manufacturing process and the tool wear. A parallel research field is dedicated to isolate the health condition of the machine tool from the production process and external source of noise. This study presents a novel solution for machine health condition monitoring based on the so-called “fingerprint” cycle approach. A fingerprint cycle is a pre-defined test cycle in no-load conditions, where the axes and the spindle are activated in a sequential order. Several signals are extracted from the machine controller to characterize the current health state of the machine. The method is suitable to separate drifts, trends and shifts in CNC signals caused by a change in machine tool health condition from any variation related to the cutting process and external factors. A machine learning method that combines Principal Component Analysis and statistical process monitoring allows one to quickly detect degraded conditions affecting one or multiple critical components. A real case study is presented to highlight the potentials and benefits provided by the proposed approach

Impact of the Base Resistance Noise and Design of a -190-dBc/Hz FoM Bipolar Class-C VCO

This letter presents the analysis of phase noise generated from the base resistance in the bipolar transistor Class-C oscillator. It is found that the efficient pulsed current of transistors enhances this noise contribution. Moreover, the impact of the base resistance noise rises quickly with the oscillation amplitude, limiting the benefits of rising the supply voltage to reduce phase noise and penalizing the Figure of Merit (FoM). Furthermore, the amplitude stability problem is addressed and a simple expression for the maximum tail capacitance to prevent instability is derived. The phase noise analysis is validated by the measurements on a 12-GHz Class-C HBT VCO. With 3.4-V supply, the VCO reaches -187-dBc/Hz FoM, limited by the base resistance noise. The impact of this noise source is reduced by decreasing the supply voltage, and with 1.8-V supply, the VCO maintains a state-of-the-art phase noise (-120 dBc/Hz at 1 MHz) with an excellent -190-dBc/Hz FoM