In Situ Additive Manufacturing Process Monitoring With an Acoustic Technique: Clustering Performance Evaluation Using K-Means Algorithm

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Hossein Taheri ◽  
Lucas W. Koester ◽  
Timothy A. Bigelow ◽  
Eric J. Faierson ◽  
Leonard J. Bond
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Clustering Algorithm ◽  
In Situ Monitoring ◽  
Process Conditions ◽  
Layer By Layer ◽  
Acoustic Signatures ◽  
Acoustic Technique

Additive manufacturing (AM) is based on layer-by-layer addition of materials. It gives design flexibility and potential to decrease costs and manufacturing lead time. Because the AM process involves incremental deposition of materials, it provides unique opportunities to investigate the material quality as it is deposited. Development of in situ monitoring methodologies is a vital part of the assessment of process performance and understanding of defects formation. In situ process monitoring provides the capability for early detection of process faults and defects. Due to the sensitivity of AM processes to different factors such as laser and material properties, any changes in aspects of the process can potentially have an impact on the part quality. As a result, in-process monitoring of AM is crucial to assure the quality, integrity, and safety of AM parts. There are various sensors and techniques that have been used for in situ process monitoring. In this work, acoustic signatures were used for in situ monitoring of the metal direct energy deposition (DED) AM process operating under different process conditions. Correlations were demonstrated between metrics and various process conditions. Demonstrated correlation between the acoustic signatures and the manufacturing process conditions shows the capability of acoustic technique for in situ monitoring of the additive manufacturing process. To identify the different process conditions, a new approach of K-means statistical clustering algorithm is used for the classification of different process conditions, and quantitative evaluation of the classification performance in terms of cohesion and isolation of the clusters. The identified acoustic signatures, quantitative clustering approach, and the achieved classification efficiency demonstrate potential for use in in situ acoustic monitoring and quality control for the additive manufacturing process.

In situ monitoring of material jetting additive manufacturing process via impedance based measurements

2019 ◽  
Vol 28 ◽  
pp. 456-463 ◽  
Author(s):  
Logan D. Sturm ◽  
Mohammed I. Albakri ◽  
Pablo A. Tarazaga ◽  
Christopher B. Williams
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Process ◽  
In Situ Monitoring

scholarly journals Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

2021 ◽  
Author(s):  
Matteo Bugatti ◽  
Bianca Maria Colosimo
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Defect Detection ◽  
High Speed ◽  
Data Extraction ◽  
In Situ Monitoring ◽  
Imaging Data ◽  
Computational Speed

AbstractThe increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection.

scholarly journals In situ monitoring methods for selective laser melting additive manufacturing process based on images — A review

China Foundry ◽  
2021 ◽  
Vol 18 (4) ◽  
pp. 265-285
Author(s):  
Bo Wu ◽  
Xiao-yuan Ji ◽  
Jian-xin Zhou ◽  
Huan-qing Yang ◽  
Dong-jian Peng ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Manufacturing Process ◽  
In Situ Monitoring ◽  
Monitoring Methods ◽  
Laser Melting

An Initial Study Towards In-Situ Ultrasonic Monitoring and Measurement of Part Properties During Photopolymer Based Additive Manufacturing

2019 ◽  
Author(s):  
Tong Su ◽  
Menghan Jiang ◽  
Qing-Ming Wang ◽  
Xiayun Zhao
Keyword(s):  
Elastic Modulus ◽  
Additive Manufacturing ◽  
Ultrasonic Transducer ◽  
Initial Study ◽  
In Situ Monitoring ◽  
Ex Situ ◽  
Process Conditions ◽  
Time Resolved ◽  
Monitoring And Measurement

Abstract This paper presents our recent preliminary study on using a novel in-house ultrasonic measurement technique to investigate ex situ the elastic modulus evolution during a photopolymer based additive manufacturing (PAM). Experiment is designed to study the effects of PAM process parameters on the elastic modulus of fabricated samples. A unique lab-built line-focused ultrasonic transducer based on time-resolved defocusing is employed to measure velocities of the surface waves (Rayleigh waves and longitudinal bulk waves) leaking from the samples. The samples’ elastic properties can be calculated from the obtained wave velocities. As a result, changes in elastic modulus with the varying PAM process conditions are successfully detected and quantified by this ex situ ultrasonic technique, revealing important information on both the “need-to” and “how-to” develop an in-situ monitoring and measurement system for part properties during PAM processes. The research outcome will not only enhance understanding about evolution of mechanical properties during PAM, but also offer insightful guidance on a future development based on the reported ex-situ ultrasonic technology towards an in-situ ultrasonic system for in-process measurement and advanced control of PAM.

scholarly journals Referencing of powder bed for in situ detection of lateral layer displacements in additive manufacturing

2021 ◽  
Vol 10 (2) ◽  
pp. 247-259
Author(s):  
Martin Lerchen ◽  
Julien Schinn ◽  
Tino Hausotte
Keyword(s):  
Additive Manufacturing ◽  
Data Acquisition ◽  
Reference System ◽  
Contour Detection ◽  
In Situ Monitoring ◽  
Measuring System ◽  
Layer By Layer ◽  
Powder Bed ◽  
Measurement Systems

Abstract. An increasing number of additive manufacturing (AM) applications leads to rising challenges for the process-accompanying quality assurance. Beside post-processing measurement systems, in situ monitoring systems in particular are currently requested to ensure feedback controlling during AM processes. For data acquisition and subsequent evaluation, a high data quality is of importance. It depends on a high resolution and accuracy of measurement systems, adapted measurement conditions and a reference to the powder bed or component for geometric measurements. Within this scientific study, a new reference system has been implemented into the powder bed to reduce measurement deviations by an abbreviated metrological loop. After data acquisition and image processing layer by layer, the position stability of the reference system has been analysed in relation to the optical measuring system. Based on a contour detection of the reference markers, the evaluation of geometrical process deviations is presented as an essential basis for a closed-loop controlling system. Thermally induced and mechanical drifts within the manufacturing process can be verified by the reference system in the powder bed. As an outlook, two methods are suggested for a process-accompanying referenced detection of the melting pool and resulting contour displacements during additive manufacturing.

scholarly journals Blade Segment with a 3D Lattice of Diamond Grits Fabricated via an Additive Manufacturing Process

2020 ◽  
Vol 33 (1) ◽  
Author(s):  
Bin Chen ◽  
Peng Chen ◽  
Yongjun Huang ◽  
Xiangxi Xu ◽  
Yibo Liu ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Service Life ◽  
Manufacturing Process ◽  
Cutting Speed ◽  
Inspection System ◽  
Layer By Layer ◽  
Filling Rate ◽  
Pressure System ◽  
Manufacturing Equipment ◽  
Diamond Blade

Abstract Diamond tools with orderly arrangements of diamond grits have drawn considerable attention in the machining field owing to their outstanding advantages of high sharpness and long service life. This diamond super tool, as well as the manufacturing equipment, has been unavailable to Chinese enterprises for a long time due to patents. In this paper, a diamond blade segment with a 3D lattice of diamond grits was additively manufactured using a new type of cold pressing equipment (AME100). The equipment, designed with a rotary working platform and 16 molding stations, can be used to additively manufacture segments with diamond grits arranged in an orderly fashion, layer by layer; under this additive manufacturing process, at least 216000 pcs of diamond green segments with five orderly arranged grit layers can be produced per month. The microstructure of the segment was observed via SEM and the diamond blade fabricated using these segments was compared to other commercial cutting tools. The experimental results showed that the 3D lattice of diamond grits was formed in the green segment. The filling rate of diamond grits in the lattice could be guaranteed to be above 95%; this is much higher than the 90% filling rate of the automatic array system (ARIX). When used to cut stone, the cutting amount of the blade with segments made by AME100 is two times that of ordinary tools, with the same diamond concentration. When used to dry cut reinforced concrete, its cutting speed is 10% faster than that of ARIX. Under wet cutting conditions, its service life is twice that of ARIX. By applying the machine vision online inspection system and a special needle jig with a negative pressure system, this study developed a piece of additive manufacturing equipment for efficiently fabricating blade segments with a 3D lattice of diamond grits.

Evaluation of Parts Produced by a Novel Additive Manufacturing Process

2013 ◽  
Vol 315 ◽  
pp. 63-67 ◽  
Author(s):  
Muhammad Fahad ◽  
Neil Hopkinson
Keyword(s):  
Additive Manufacturing ◽  
Rapid Prototyping ◽  
Manufacturing Process ◽  
High Speed ◽  
Three Dimensional ◽  
Coordinate Measuring Machine ◽  
Layer By Layer ◽  
Nylon 12 ◽  
Measuring Machine ◽  
End Use

Rapid prototyping refers to building three dimensional parts in a tool-less, layer by layer manner using the CAD geometry of the part. Additive Manufacturing (AM) is the name given to the application of rapid prototyping technologies to produce functional, end use items. Since AM is relatively new area of manufacturing processes, various processes are being developed and analyzed for their performance (mainly speed and accuracy). This paper deals with the design of a new benchmark part to analyze the flatness of parts produced on High Speed Sintering (HSS) which is a novel Additive Manufacturing process and is currently being developed at Loughborough University. The designed benchmark part comprised of various features such as cubes, holes, cylinders, spheres and cones on a flat base and the build material used for these parts was nylon 12 powder. Flatness and curvature of the base of these parts were measured using a coordinate measuring machine (CMM) and the results are discussed in relation to the operating parameters of the process.The result show changes in the flatness of part with the depth of part in the bed which is attributed to the thermal gradient within the build envelope during build.

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