The Final Step Toward Quality in Additive Manufacturing

ISE Magazine April 2019 Volume: 51 Number: 4

By Prahalada Rao

Humankind knows three ways to make things: Eroding away material from a bulk raw material to create a shape, called subtractive manufacturing or machining; changing the shape of a bulk material by either rolling, forging, extrusion, casting and the like, called formative manufacturing; and joining bits of material to make the final shape, called additive manufacturing or 3D printing.

There are four main ways in which the layers can be stitched together:

  • Supplying heat with an energy source, such as a laser, electron beam or ultrasonic vibration
  • Gluing the material using an epoxy or photopolymer binder
  • Initiating a photochemical reaction in layers of thermoset-ting plastic resins and biological materials
  • Thermal initiation of polymerization of thermoplastics

The key idea is to manufacture a part with zero defects by monitoring and correcting process faults through data collected from multiple sensors inside the machine. If this concept of qualify-as-you-build can be realized, it will take additive manufacturing into the profitable realm of industrial-scale production as opposed to the prototype-demonstrator role the technology is largely confined to currently.

AM’s advantages, unique capabilities

Additive manufacturing is unique because the microstructure of the part and its shape are created incrementally and simultaneously. In other words, the microstructure obtained in an AM part is influenced by the part’s geometry, as well as the process parameters. This singular ability to shape the geometry and structure of the part comes with advantages and disadvantages.

The advantages of AM as an enabling technique stems from the following so-called freedoms:

  • Freedom of design complexity. It takes the same effort to make a square hole as a round hole, conformal internal channels, steep overhang features and lattices.
  • Freedom of material selection. A wide range of materials can be made on the same machine. For instance, it is conceivable to switch from processing of a conventional material, such as steel, to a high-temperature nickel-based super alloy by adjusting a few parameters, such as laser power.
  • Freedom to tailor the material structure to the application. The structure and composition of material can be varied across the part, called functionally gradient structures, leading to unusual bulk properties, such as negative coefficient of thermal expansion.
  • Freedom from waste due to worn tools and minimized energy consumption. Unlike subtractive machining, the AM process does not require a cutting tool to contact the material, which then wears due to chemical and thermal phenomena.
  • Freedom from set batch sizes. Because no specialized tooling has to be built, the fixed cost per unit is small; hence, the break-even point is smaller because production does not have to be spread over a large batch.
  • Freedom from waste of energy and materials. The amount of material and energy needed is magnitudes smaller. For instance, the buy-to-fly ratio in AM – i.e., the ratio of material processed to the final weight of the part – is as small as 7 to 1 compared to 20 to 1 with traditional machining.
  • Freedom from assembly. The number of component parts can be reduced by several orders of magnitude.
  • Freedom from variability due to breakdowns and maintenance. Because there is no variability due to tooling, controlling the input material is sufficient to reduce variability. Furthermore, the uniformity of machines, regardless of part design, has a great impact on the reliability and maintainability aspects of the production line.

These advantages herald a paradigm shift and accompanying challenges in a host of manufacturing-related domains, ranging from modeling, materials, metrology, sensing, analytics and logistics, among others.

Impending challenges of AM

Despite these groundbreaking abilities, an underlying challenge to additive manufacturing is the uncertainty and variability in the final part properties.

The variability in AM processes can be narrowed to the following four reasons:

  1. Material purity-related part inconsistency. In pow-der-based processes, such as powder bed fusion and directed energy deposition, foreign material inclusions and residue from previous builds can contaminate the powder feed-stock.
  2. Machine calibration errors. The spot size of the laser relative to its position on the bed may vary in powder bed fusion. Likewise, the powder might not be evenly distributed if the bed is inclined. Indeed, AM researchers often report variation in identically shaped parts processed under identical conditions across the build plate.
  3. Improper selection of process parameters. Process parameters such as laser power, hatch spacing, layer height and scan speed in LPBF determine the mechanics of defect formation. For instance, a high laser power will lead to uniform, spherical-shaped pores due to vaporization of material; this is called keyhole porosity. In contrast, if the laser power is insufficient to melt the material, uneven and irregular-sized pores are observed; these are called lack-of-fusion porosity.
  4. Ill-considered part design. The part feature geometry determines the direction and magnitude of heat flow in the part, called heat flux. The thermal history of the part in turn is directly responsible for the variation in microstructure, called microstructure heterogeneity; as a consequence, the functional properties, such as surface finish of the part, will vary.

Based on the following reasoning. The first two, material and machine calibration errors, are closely related to control and kinematics of the input materials and machine, respectively. They can thus be termed as “small v” variation. In contrast, the last two factors, related to the process factors and part geometry, govern the process dynamics and can be called a “big V” variation.

Unless both the big V and small v variations are mapped and controlled, industries – especially those such as aerospace and defense where adherence to specifications is contractually mandated – will remain reluctant to use AM-produced parts in mission-critical assemblies.

To overcome these quality-related challenges, the current tack taken by manufacturers is to narrow down the process parameters by building simple-geometry test artifacts and subsequently characterizing their properties through offline techniques, such as XCT and destructive materials testing.

AM opportunities for industrial engineers

A solution to overcome these quality-related impediments in AM is to qualify the integrity of each layer as it is being built using data acquired from sensors built into the process.

Other incarnations of qualify as you build include, Born Qualified from Sandia National Labs; In-process Quality Assurance, a registered trademark of Sigma Labs in New Mexico; and Certified Additive Manufacturing of Materialize Inc.

To realize the qualify-as-you-build paradigm requires fundamental understanding of each link in the AM process chain, a research need that intrinsically encompasses the knowledge of the industrial engineering profession: Process conditions, process phenomena, process signatures, part microstructure (defects), process control (rectification) and part performance.

  • Process conditions. Parameter settings, material contamination, part design, machine errors
  • Process phenomena. Vaporization, incomplete fusion, melt pool instability, thermal gradients
  • Sensor data signatures. Melt pool thermal profile, meltpool shape, spatter pattern extracted from heterogeneous in-process sensors such as thermal and high-speed cameras, photodetectors
  • Part microstructure/defects. Pinhole pores, acicular pores or distortion caused by the above phenomena
  • Process control. Changing the process parameters, scan strategy, part design and support structures to compensate for defects
  • Part performance or quality. Fatigue life and surface finish

From qualify as you build to correct as you build

The qualify-as-you-build approach integrating sensing and closed-loop process control in AM is being in-tensely studied. However, a correction-based strategy might be needed to transcend process sensing and control. Process control due to inherent delays in the feedback and parameter adjustment loop may be too slow to counteract the faster thermomechanical phenomena that cause defects.

Thus, despite process sensing and control, a build might still be scrapped due to defects. The key idea of this approach, called “correct as you build,” is to correct defects such as porosity by leveraging the intrinsic phenomena of the process to remelt previously deposited layers.

For instance, once lack of fusion porosity is detected in a layer by sensors built into the machine, the subsequent layers can be deposited at a higher energy density, leading to melting of unfused particles. For defects such as cracking and pinhole porosity not liable to be corrected by remelting, a hybrid additive-subtractive approach can be used.

The recently acquired hybrid metal AM systems acquired by University of Nebraska-Lincoln (Matsuura Avance 25 and Optomec Hybrid system) have an integral subtractive machining head, which can be used to remove a defect afflicted layer. Through this hybrid AM approach it is possible to envision a correct-as-you-build paradigm beyond qualify as you build to ensure defect-free parts.