How a new framework looks to mainstream additive repair

One of the most common uses of additive manufacturing (AM) is the 3D-printing of new parts. Less well known is additive repair, where AM technologies are used to repair high-value assets. New research at Rochester Institute of Technology’s (RIT) Golisano Institute for Sustainability (GIS) is set to change that—GIS researchers are exploring how additive repair can open new paths to the circular economy as part of the remanufacturing industrial space.   

The digital manufacturing leader Siemens recently observed that the “complete potential of additive repair is still largely unexplored.” With that in mind, researchers at Rochester Institute of Technology’s (RIT) Golisano Institute for Sustainability (GIS) are developing a new framework that will lay the foundation for bringing additive repair into the mainstream maintenance and sustainment space. They are working under a grant from the Office of Naval Research (ONR) to build a practical tool that will connect the dots between existing AM innovations and U.S. industry and defense goals for more efficient, effective maintenance practices for heavy-duty vehicles and equipment.

Already more than 30 years old, AM is a process whereby an object is made by sequentially layering material. Polymers and metals are the most common feedstock, but even food or human tissue can be used. Digital design software is essential to AM, typically in the form of a computer-aided design (CAD) platform. For this reason, AM is often equated with 3D printing, but it is actually a broader term for many technologies and applications.

Common AM methods include binder jetting, directed-energy deposition (DED), wire-arc additive manufacturing (WAAM), material extrusion, powder-bed fusion, sheet lamination, and vat polymerization. It employs three basic technologies: sintering (materials are heated without being fully melted), melting (materials are fully melted), and stereolithography (resin is hardened using high-powered a laser). Additive repair, as an AM process, relies on applications of some of these technologies, such as welding, laser additive, thermal spray, and cold spray. 

There are many “dots” that the framework will connect: available AM technologies, mechanical and microstructural properties of different materials, performance requirements for components, methods for determining a component’s overall condition, and many others. This means closing considerable knowledge gaps by processing a number of unique variables.

Fundamentally, the framework aligns with GIS’s systems-level approach that is common to its broader efforts to advance the circular economy. A systems perspective opens new opportunities for addressing one of the most persistent challenges when it comes to practically implementing sustainability: uncertainty.

De-risking additive repair

Additive repair, and AM more generally, remains a risky investment for most manufacturers. Without any other recourse, they are left to make sense of an immense variety of material types and technical processes. This can lead to unproductive and costly testing and evaluation; one combination of technology, approach, and materials may work well in one instance but not in another.

The U.S Department of Defense (DoD) understands firsthand the challenge of making informed repair decisions using additive repair. DoD equipment managers often lack technical specifications about the materials used in older, legacy vehicles. Knowledge gaps like these complicate repair decisions. A streamlined process for determining candidate process-material combinations—the goal of the project—would reduce the time and resources that organizations like the DoD need to devote to developing additive-repair solutions. GIS’s framework is a step towards managing additive repair’s inherent diversity and limitations by cataloging these combinations and tying them to best use-case scenarios.

One such combination that GIS researchers have explored in convergent research involves a sintering process known as high-speed laser-cladding. During laser cladding, materials are metallurgically—as opposed to mechanically—bonded to a target surface. Finely powdered stainless steel is deposited into a laser beam which creates a “precision weld” to build a surface back up using a laser. High-speed laser-cladding is different from standard options because it operates as much as ten times faster. This is a quality advantage because it means using less heat and energy, the principal causes of damage to a target surface. It also can achieve a much thinner coating, which means less materials are used and less finishing work has to be done.

The work GIS is performing is the foundation for future efforts that will make it easier for a company to find additive-repair solutions, like surface coating using high-speed laser-cladding with stainless steel powder. The GIS researchers aim to close the critical knowledge gaps that keep maintainers from the most cost-effective and reliable use of additive repair. The framework is a starting point for identifying the key factors that have the most influence on additive-repair decisions, like technology types and operating costs, bonding properties of materials, and wear characteristics.

Repair before replace

ONR, the body within the U.S. Department of the Navy and Marine Corps that is responsible for advancing science and technology, is charting new territory within maintenance and sustainment of high-value assets through a series of comprehensive studies with GIS and other universities. The framework will aid maintainers in making decisions on how best to use AM to repair—as opposed to replace—components in vehicles and machinery. The difference between repair and replace might seem trivial, but it’s not.

It’s not uncommon to use AM to maintain and sustain fleets of high-value assets, especially within the aerospace and heavy-duty equipment (e.g., farming and mining) industries. However, in many industries component repair isn’t widely practiced; damaged parts are replaced and discarded. Even if an organization appreciates the value of repair in terms of material efficiency, the logistical and practical complexity can make it a hard sell. The GIS framework project is an effort to change this by showing how AM can be effectively used as a cost- and resource-efficient strategy for prolonging the life and value of high-value equipment through additive repair.

U.S. Marine Corps depots manage the maintenance of thousands of heavy-duty vehicles and equipment. Although maintenance schedules vary between types of assets, most pieces of equipment are completely disassembled—overhauled—at regular intervals. Every component is cleaned and, if damaged or unusable, replaced before the unit is reassembled for field use.

Conventional methods for repair—like welding, adding sleeves, or replacing bushings—are often the easiest and least expensive. But these options are not feasible for many damaged or worn parts. Military vehicles, for example, frequently require repairs while in the field. In such instances, spare parts are often not on hand. If a vehicle or heavy machinery is older, new parts may no longer be available for it. Additive repair is advantageous in these situations because it can be flexible and adaptable to many different kinds of repairs while being more cost-effective than fabricating a new part.

Yet, despite its advantages, navigating the relative costs, benefits, and risks of different additive-repair options remains daunting. The framework will serve as a map that fleet managers and maintainers can use to find their way to the right solution. Whatever path they take, the first step is always the same: Assess a worn or broken part’s condition.

Wear and tear is the result of a complex interplay of conditions; environment, patterns of use, and age all impact how material degrades. Material type, function, and severity of operation also influence how components fatigue. Similar components in two vehicles of the same model will not deteriorate in the same ways if where and how they are used differ enough. These variables are only a sample of what needs to be taken into account as part of a typical repair decision-making process. 

The framework will help maintainers make sense of critical variables and information so they can make informed decisions about additive repair. Its aim is to give them a way to quickly access a prioritized list of potential solutions based on the performance of various combinations of AM technologies and deposited materials relative to different repair contexts. At its root, this work comes down to closing critical knowledge gaps, a problem GIS engineers have long experience solving through remanufacturing, a key research focus of the institute.  

AM as part of a remanufacturing process

Remanufacturing recovers used or worn products from consumer or commercial markets—known as “cores”—and fully restores them to a like-new or better-than-new condition. The remanufacturing process follows a highly engineered methodology: A remanufacturer systematically disassembles, cleans, and inspects every component for wear and material degradation in order to determine discrete restoration methods. GIS has found that a number of unique AM technologies offer considerable promise for fast-tracking repair during the remanufacturing process.

In the early 2000s, GIS worked with the U.S. Marine Corps to develop an AM process to extend the life of drive shafts in an eight-wheeled, amphibious, light armored vehicle (LAV) model, the LAV-25. Up until that point, corrosion had forced the Marines to replace the shafts in six out of every eight vehicle overhauls they performed. With more than 700 LAV-25s to maintain, the issue cost the military hundreds of thousands of dollars a year. GIS’s solution drew on flame-spray plastic coating, an AM technology, as a means to restore corroded drive shafts back to full service. Compared to a new drive-shaft replacement, this solution reduced costs by as much as 95 percent and extended average service life by 40 percent.

In a 2018 project , GIS researchers applied AM to successfully repair the drive gear in a milling machine that was more than 50 years old. Five teeth on the positioning drive gear broke, taking the unit offline at a crucial moment for the machine-tooling business that owned it. The component, long obsolete, was no longer produced by the original equipment manufacturer (OEM). Likewise, a traditional solution—subtractively manufacturing it entirely from scratch—proved to be costly and time consuming for the firm with little probability that it would perform as reliably as the original.

GIS engineers approached the problem as an opportunity for additive repair. They turned to an application of DED known as laser-engineered net-shaping (LENS), in which material is added to a surface by melting metal powder directly onto it. In this low-stress application, new teeth were added to the drive gear, saving the company from having to reverse engineer and fabricate the part or purchase another machine while returning the drive gear to operation.

GIS’s most recent project with ONR, launched in 2018, is an opportunity to investigate AM as a remanufacturing enabler by scaling up the findings from the projects described above into a generalized framework. This line of study is just one thrust of a larger initiative at ONR to transition the military’s sustainment strategy to a model known as condition-based maintenance (CBM).

CBM is an approach to maintenance where an asset’s condition is continually monitored in order to support smarter sustainment decisions. Different techniques, often with digital tools, are used to measure changes in performance that may indicate degradation or potential failure. Signs of degradation trigger maintenance events—often part replacement or repair—in order to maintain optimal system reliability. Harvesting data in real-time from equipment, products, and components allows parts to be removed from service for remanufacturing before they are excessively damaged. In this way, CBM has an important role to play within the remanufacturing environment, where components are often recovered after failure has occurred. The availability of component condition data and usage histories allows a remanufacturer to quickly assess a component’s remanufacturing value. This, in turn, informs what steps should be taken in the remanufacturing process.

Additive repair (and AM more generally) applied within the remanufacturing space can support wider strategies to transition industry to the circular economy. It is inherently efficient when it comes to material use—repairing existing products and materials means less raw material extraction for new production and less waste in landfills. It also gives remanufacturers a cost-effective option when making decisions to return components and products to use at the end of a full life cycle. GIS has already shown how additive repair, strategically deployed, can be used to extend the service life of high-value vehicles and equipment, but its potential for everyday commodities with shorter life cycles and lower overall value remains largely unexplored.

A public resource for U.S. industry

ONR first engaged GIS in 1998 to evaluate alternatives to fuel sensors used in LAVs. Since then, 11 more research projects have been conducted in support of ONR’s modernization, maintenance, and sustainment of vehicles and equipment. While this research and development is immediately driven by the goals of ONR, it also serves a broader purpose: technology and knowledge transfer to U.S. industry.

Additive repair is far from a go-to for manufacturers today. Even companies that appreciate its potential struggle to make the case for it as a worthwhile investment. Again, this comes down to the uncertainty following the many unknowns and variables that come with practically putting it to work.

GIS’s study sets down a framework that achieves two critical objectives. First, it is a framework for eventually organizing the immense amount of data and variables—AM technologies, material options, mechanical and microstructural properties, and more—that fall under additive repair. Second, it is an important step towards creating an automated decision tool for asset managers and manufacturers alike to make practical sense of that massive store of information in light of their immediate repair needs. The researchers understand these two objectives to be mutual; the more information about AM technologies, parts, and materials that is added to the tool’s database by users, the more accurate and versatile it will become for others.

The GIS researchers envision a future where a tool will be developed from this framework that manufacturers of all sizes can use to find the best additive-repair pathway for a specific component. If successful, such a widely available tool would not only simplify the disparate, complex landscape of additive repair, but it will also reverse the conventional notion that component repair, as a whole, is always lower quality and more costly and resource-intensive. GIS’s work shows that additive repair can be used to resolve two critical challenges facing high-value asset maintenance and sustainment today. One, it can be a more cost-effective option than replacing broken or worn components with new ones. Two, additive repair can be quicker than fully fabricating components using AM or conventional machining. 

Most immediately, GIS’s additive-repair framework will support fleet managers and maintainers in their efforts to modernize and streamline its maintenance and sustainment operations. But that’s not the only benefit of the project: As a research project funded by the U.S. federal government, the results of the work will be publicly available, setting the stage for further development by academics and industry leaders alike. The framework is not only a first step towards making additive repair a standard of maintaining high-value assets; it is an excellent example of how putting the circular economy into practice comes down to taming data and removing uncertainty for decision-makers.

Funding Acknowledgement and Disclaimer:

This material is based upon work supported by the Office of Naval Research under Award No. N00014-18-1-2339.  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research.