A new chapter in pharmaceutical manufacturing: 3D-printed drug products.

Additive manufacturing (AM) is increasingly used in the development of new products: from conceptual design to functional parts and tooling. However, today, variability in part quality due to inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for high-value or mission-critical applications. Although process control in general can limit this variability, it is impeded by a lack of adequate process measurement methods. Process control today is based on heuristics and experimental data, yielding limited improvement in part quality. The overall goal is to develop the 630measurement science
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 necessary to make in-process measurement and real-time control possible in AM. Traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of AM processes. As a precursor, this report presents a review on the AM control schemes, process measurements, and modeling and simulation methods as it applies to the powder bed fusion (PBF) process, though results from other processes are reviewed where applicable. The aim of the review is to identify and summarize the measurement science needs that are critical to real-time process control. We organize our research findings to identify the correlations between process parameters, process signatures, and product quality. The intention of this report is to serve as a background reference and a go-to place for our work to identify the most suitable measurement methods and corresponding measurands for real-time control.

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Measurement science needs for real-time control of additive manufacturing powder-bed fusion processes

Additive manufacturing technologies are increasingly used in the development of new products. However, variations in part quality in terms of material properties, dimensional tolerances, surface roughness and defects limit its broader acceptance. Process control today based on heuristics and experimental data yields limited improvement in part quality. In an effort to identify the needed measurement science for real-time closed-loop control of additive manufacturing (AM) processes, this paper presents a literature review on the current AM control schemes, process measurements and modelling and simulation methods as it applies to the powder bed fusion process, though results from other processes are reviewed where applicable. We present our research findings to identify the correlations between process parameters, process signatures and product quality. We also present research recommendations on the key control issues to serve as a technical basis for standards development in this area. Complimentary deta...

/pdf/a-review-on-measurement-science-needs-for-real-time-control-w9fqvr5382.pdf

A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes

The powder bed fusion additive manufacturing process enables fabrication of metal parts with complex geometry and elaborate internal features, the simplification of the assembly process, and the reduction of development time; however, its tremendous potential for widespread application in industry is hampered by the lack of consistent quality. This limits its ability as a viable manufacturing process particularly in the aerospace and medical industries where high quality and repeatability are critical. A variety of defects, which may be initiated during powder bed fusion additive manufacturing, compromise the repeatability, precision, and resulting mechanical properties of the final part. One approach that has been more recently proposed to try to control the process by detecting, avoiding, and/or eliminating defects is online monitoring. In order to support the design and implementation of effective monitoring and control strategies, this paper identifies, analyzes, and classifies the common defects and their contributing parameters reported in the literature, and defines the relationship between the two. Next, both defects and contributing parameters are categorized under an umbrella of manufacturing features for monitoring and control purposes. The quintuple set of manufacturing features presented here is meant to be employed for online monitoring and control in order to ultimately achieve a defect-free part. This categorization is established based on three criteria: (1) covering all the defects generated during the process, (2) including the essential contributing parameters for the majority of defects, and (3) the defects need to be detectable by existing monitoring approaches as well as controllable through standard process parameters. Finally, the monitoring of signatures instead of actual defects is presented as an alternative approach to controlling the process “indirectly.”

Common defects and contributing parameters in powder bed fusion AM process and their classification for online monitoring and control: a review

A review of ultrasonic testing applications in additive manufacturing: Defect evaluation, material characterization, and process control.

Safe life and damage tolerance aspects of railway axles – A review

Inspection of additive-manufactured layered components

From atmospheric corrosive attack to crack propagation for A1N railway axles steel under fatigue: Damage process and detection

Laser Powder Deposition (LDP) techniques are being adopted within aerospace and automotive manufacturing to
produce innovative precision components. Non-destructive techniques (NDT) for detecting and quantifying flaws within these components enables performance and acceptance criteria to be verified, improving product safety and reducing ongoing maintenance and product repair costs. In this work, software enabled techniques are presented for in-process analysis of NDT laser ultrasonic signals and pulsed laser thermography images of sequential metallic LPD layers. LPD tracks can be as thin as 200μm while deposited at a rate of 500 mm/min, requiring ultrafast inspection and processing times. The research developed analysis algorithms that allow senior engineers to develop inspection templates and profiles for in-process inspection, as well as an end-to-end, user friendly interface for engineers to perform complete manual Laser Ultrasonic or Laser Thermographic inspections. Several algorithms are offered to quantify the flaw size. location and severity. The identified defects can be imported into a sentencing engine which then automatically compares analysis results against the user defined acceptance criteria so that the manufacturing products can be verified. Where both laser ultrasonic and laser thermographic NDT data is available further statistical tools could increase the confidence level of the inspection decision.

Defect detection in laser powder deposition components by laser thermography and laser ultrasonic inspections

Laser powder deposition is a process where a powder is sprayed on to a substrate at the same time as a laser is used to melt it. In this way layers of the powder material are built up into structures that can be very fine and complex. For critical requirements it is useful to inspect the structure as the layers are deposited as the final component may have considerable added value. The main challenge for the NDT methods are the small sizes involved, which in turn lead to detection of small flaws being required. Laser ultrasonics, laser thermography and eddy currents were identified as methods that might be deployed for the inspection. This paper describes the results obtained from these methods on a specially developed set of reference samples and on specific deposition samples with induced flaws.

John Rudlin

Papers

Safe life and damage tolerance aspects of railway axles – A review

Inspection of additive-manufactured layered components

From atmospheric corrosive attack to crack propagation for A1N railway axles steel under fatigue: Damage process and detection

Defect detection in laser powder deposition components by laser thermography and laser ultrasonic inspections

Inspection of Laser Powder Deposited Layers