1,721,048 research outputs found
COMPLEX PCA AS AN EXPLORATORY TOOL FOR 3-DIMENSIONAL PROFILES
No full textDuring the past few years, an increasing number of approaches and applications of profile monitoring have been proposed in the scientific literature. As a matter of fact, very often product and/or process quality is characterized by profiles or functional data. In this type of applications, the quality outcome (dependent variable) is actually a function of one or more spatial or temporal location variables (independent variables). Up to now, profile monitoring techniques have been constrained to situations in which the dependent variable is a scalar which can be modelled as a function of one or more independent variables via linear models or data-reduction approaches as PCA. As a matter of fact, PCA has been demonstrated to be an excellent exploratory method for interpreting and modelling profiles in a scalar field of data, obtained from manufacturing processes (Colosimo and Pacella 2007). However, when the quality of products is related to geometric tolerances, very often the profile cannot be simply modelled via a scalar variable, since the profile or curve lies in a 3-dimensional space. Examples range from the simplest requirement of axial straightness to very complex curves in free-form geometric tolerance. This paper explores problems arising when 3D curves has to be monitored over time proposing possible solutions. A real case dealing with the straightness of cylindrical components machined by turning is used as reference throughout the paper. In particular, an approach is discussed in which a generalization of PCA is used to model two-dimensional vector observations (i.e., directional data). The method is based on an appropriate use of complex, rather than real numbers in the analysis. The so-called “complex PCA” is routinely used in the case of horizontal velocity components in geophysical measurements (such an approach was firstly proposed by Hardy and Walton, 1978). Our aim is to explore the use of complex PCA also for modelling 3D profiles obtained from manufacturing processes.
References
Colosimo B. M. and Pacella M., 2007. On the Use of Principal Component Analysis to Identify Systematic Patterns in Roundness Profiles. Quality and Reliability Engineering International, 23(6), 707-725.
Hardy D. M. and Walton J. J., 1978. Principal Components of Vector Wind Measurements. Journal of Applied Meteorology, 17(8), 1153–1162
PIAM Editorial April 2023
No full textI am delighted to present the latest issue of Progress in Additive Manufacturing, which highlights the impressive growth of Additive Manufacturing (AM) in both academic and industrial spheres. AM has evolved significantly, embracing a wide range of technologies, materials, applications, and sectors, resulting in a rapidly expanding global community.
This issue features an array of research topics, with a specific emphasis on Material Extrusion. The papers investigate exciting research avenues, focusing on diverse materials, such as multimaterials, reinforced thermoplastic with continuous fiber, and Acrylic Butadiene Styrene. Furthermore, the issue examines process strategies and parameters, along with their impact on the final quality and performance of the printed parts. The issue also explores other AM processes for polymers, such as Digital Light Processing (DLP) and Selective Laser Sintering (SLS), as well as for metals, including electron- and laser-based Powder Bed Fusion and friction stir molding.
As an Associate Editor of PIAM since 2022, I have observed a remarkable surge of interest in AM, as evidenced by the growing number of submissions in the last year. From my perspective, the future of AM is exceptionally bright, as we anticipate ushering in a new generation of eco-friendly products. AM will serve as a crucial enabler of this transition, as it allows for the production of green products, using its “complexity-for-free”. With AM, we can manufacture (and repair) lightweight, durable, and highly efficient products with superior thermomechanical properties. Furthermore, AM can significantly reduce the number of components, thereby reducing the environmental footprint of transportation. Once post-processing and finishing stages are streamlined, printing on demand will become the norm, and almost net-shape printing will be attainable.
To support this green transition, we must enhance our process knowledge and engage in active optimization to minimize waste, scraps, and defective parts. In recent years, I have been exploring the advantages of in-situ data mining, monitoring, and control for zero-waste, zero-defect AM. I am confident that the convergence of big data, Artificial Intelligence (AI), machine learning, and AM will flourish in the coming years, provided we invest in robust and reliable tools to ensure repeatability and reproducibility of the newly developed solutions. AM will serve as the bridge that merges the green and digital aspects of the transition we are striving for.
With these compelling avenues for future research, I am confident that the future of AM is bright and full of potential
A statistical learning method for image-based monitoring of the plume signature in laser powder bed fusion
Full text linkThe industrial breakthrough of metal additive manufacturing processes mainly involves highly regulated sectors, e.g., aerospace and healthcare, where both part and process qualification are of paramount importance. Because of this, there is an increasing interest for in-situ monitoring tools able to detect process defects and unstable states since their onset stage during the process itself. In-situ measured quantities can be regarded as “signatures” of the process behaviour and proxies of the final part quality. This study relies on the idea that the by-products of laser powder bed fusion (LPBF) can be used as process signatures to design and implement statistical monitoring methods. In particular, this paper proposes a methodology to monitor the LPBF process via in-situ infrared (IR) video imaging of the plume formed by material evaporation and heating of the surrounding gas. The aspect of the plume naturally changes from one frame to another following the natural dynamics of the process: this yields a multimodal pattern of the plume descriptors that limits the effectiveness of traditional statistical monitoring techniques. To cope with this, a nonparametric control charting scheme is proposed, called K-chart, which allows adapting the alarm threshold to the dynamically varying patterns of the monitored data. A real case study in LPBF of zinc powder is presented to demonstrate the capability of detecting the onset of unstable conditions in the presence of a material that, despite being particularly interesting for biomedical applications, imposes quality challenges in LPBF because of its low melting and boiling points. A comparison analysis is presented to highlight the benefits provided by the proposed approach against competitor methods
In-Situ Process Monitoring in Metal Powder Bed Fusion Processes by Means of Multi-Sensor Data Mining Methods
No full textThanks to rapid technological advances, metal additive
manufacturing (AM) technologies enable the production
of complex shapes, topologically optimized and
lightweight structures that are of industrial interest for
advanced applications in different sectors, like
aerospace and health-care. However, stringent quality
standards and aerospace process qualification
requirements impose defect-free and first-time-right
capabilities that are still challenging to achieve with
state-of-the-art AM systems. This paper reviews
different methods to gather and make sense of in-situ
data from different sensors during powder bed fusion
processes. These methods aim to enhance the embedded
intelligence of the AM system by integrating the
capability of automatically detecting and localizing
process defects since their onset stage
Predicting the roughness of overhanging surfaces in laser powder bed fusion via in-situ thermal imaging
Full text linkThe production of overhanging surfaces in Laser Powder-Bed Fusion (LPBF) has long been a challenging task due to poor heat dissipation and lack of support of loose powder, resulting in surface defects and increased roughness due to dross formation and sintering. Surface quality is a critical aspect of AM mechanical components that undergo fatigue loading, as a rough surface can act as a preferential crack initiation site and lead to premature failure. Predicting the quality of the as-built surfaces could be used to identify critical areas that require rework or post-processing, or to find regions that require optimization of the process parameters to improve the final quality. The orientation of the surface itself (i.e., the degree of inclination of the surface) could be used to predict the final surface quality and will be employed as benchmarking reference throughout the work (referred to as “geometry-based” model). This study demonstrates the effectiveness of using data mining on high-speed thermal video images to create a real-time predictive model based on “in-situ” data for estimating surface roughness (Sa) of overhanging surfaces printed at different inclinations. The results showed that the model based on “in-situ” data has a prediction accuracy that is more than 2 times higher than the one obtained with a model that is purely based on geometric data, i.e., a model that relies only on the inclination angle of the surface during the print. The proposed method is tested on different materials (AISI 316L stainless steel and AlSi10Mg) and process conditions (continuous and pulsed laser, low and high power) to show the flexibility and extended applicability of the proposed solution. The newly developed method opens new possibilities for in-situ quality control and process optimization of surface quality in Laser Powder Bed Fusion (LPBF)
Statistical Process Monitoring of Powder Bed Fusion Processes via In-situ Video Imaging
Full text linkA new method for in-situ process monitoring of AM cooling rate-related defects
Full text linkThe increasing popularity of additive manufacturing (AM) is pushing the industry to provide new solutions to improve the process stability. In the past, process monitoring and control has proved to be a fundamental tool to enhance the repeatability of many manufacturing processes, however the typical AM fast dynamics require a high spatiotemporal resolution data flow to accurately describe the process and these new types of data are presenting new challenges for standard statistical process monitoring (SPM) techniques. In this work, the capabilities of a new machine learning (ML) based framework for the detection of cooling rate-related defects in metal additive manufacturing processes via in-situ high-speed cameras are presented and discussed
An SPC Procedure Based on Multisensor Metrology Data Fusion
No full textIn the last years, we are assisting to a continuous drive toward the use of hybrid metrology systems, which can combine noncontact and contact sensors to take advantage of the speed of 3-D optical sensors and of the accuracy of traditional contact solutions. We show how approaches for multisensor data fusion can be combined to control charting in order to detect (and distinguish between) out-of- control states due to the measurement and/or to the manufacturing processes
A cost model for the economic evaluation of in-situ monitoring tools in metal additive manufacturing
Full text linkThe paper presents a cost model to evaluate the economic impact of defects and process instability in metal Additive Manufacturing (AM). The proposed model formulation adopts the main framework of previous seminal studies and extends it by considering the contribution of scrap fractions and in-situ monitoring performances on process and material costs, including pre- and post-processing operations. Three real industrial case studies (from dental, aerospace, and machinery sectors) were assessed to determine how the model can be used in the real industrial practice to (i) enhance the economic advantages of metal AM technologies by tackling process instability issues, (ii) assess the effectiveness of in-situ monitoring in the development of next generation metal AM systems, and (iii) define the performance specifications of in-situ monitoring solutions that yield sustainable cost savings in specific industrial applications. A further experimentation is presented to validate the cost model with respect to a benchmark reference
Powder bed irregularity and hot-spot detection in electron beam melting by means of in-situ video imaging
Full text linkThe electron beam melting process has been successfully applied in various sectors to produce high-value-added products. Being a hot process operating in vacuum environment with x-rays and material vaporization among by-products, in-situ sensing and monitoring presents more challenges than in laser powder bed fusion. However, an automated and robust detection of unstable process conditions represents a key capability to meet challenging qualification requirements imposed by industry. This study presents novel in-situ monitoring methods based on high spatial resolution imaging for powder bed homogeneity monitoring and high temporal resolution video-imaging for hot-spot detection, i.e., the detection of anomalous local heat accumulations
- …
