The Ultimate Use Case for Additive Manufacturing: Predictive and Condition Based Maintenance
Additive manufacturing has been communicated to us, as the driver that will enable the fourth industrial revolution. We are flooded by information, news, stories, academic research, and whatnot, all telling us that our lives are about to positively change, thanks to the implementation of additive manufacturing. No more off-shoring of mass-manufactured products, but localized and consumer-specific, on-demand manufacturing, that will make products better, cheaper, and sustainable, significantly reducing waste, pollution, and carbon footprint. This futuristic vision relates to most aspects of our lives, including, but not limited to our health, our homes, all different means of transportation, food, consumer goods, electronics, fashion, and so on.
Accordingly, we keep hearing that additive manufacturing is growing. More materials become manufacturable, using this method (or more accurately, the many methods of which additive manufacturing is made of), we read about medical miracles enabled thanks to additive manufacturing, and watch news about additively manufactured affordable housing.
It is all true.
Nevertheless, how many of us already live in a “3D printed” home, drive a car that contains a massive number of additively manufactured components, use additively manufactured eyewear, play with printed toys, or wear 3D printed clothes?
It is a matter of time and justification. There is no point in replacing traditional mass manufacturing with additive manufacturing unless there is a justification to do that. Justification can be cost, environmental considerations, supply chain, improved functionality, etc. (there is no necessity to prioritize these different possible justifications). It will take time until vendors design their products in a way that can justify the migration from traditional to additive manufacturing. This will happen with the improvement of technologies and the progress of design for additive manufacturing education. Many initiatives will fail due to not producing enough justification, but over time, a growing number of applications will prevail.
Nevertheless, in certain manufacturing (as opposed to prototyping) areas, additive manufacturing already dominates, such as in hearing aids, Surgical planning, surgical guides, luxury jewelry, interior design, orthotics, manufacturing tooling, some mechanical spare parts etc.
The common denominator of most of the above-mentioned applications is that these are Low Volume – High Mix products/applications. Therefore, it can be assumed, that the key for additive manufacturing penetration to the mainstream of manufacturing, is the development of an ever-growing funnel of low volume – high mix products – diverting from mass-manufacturing to mass-customization, justified by the values/benefits specified above. As an exception, improved supply chains and sustainability, will justify migration to additive manufacturing, even if no customization is introduced, for example, if we can, by additively manufacturing parts, save the need for spare parts inventories and/or transportation.
Spare parts’ inventories are a burden to most product vendors in many industries. There is a minimum order batch, that makes spare parts costly, there is a cost to manage the inventory (insurance, etc.), risks (wear, loss, no demand), real estate, and logistics costs. In some cases, there are no remaining spare parts in inventory, and the manufacturing tooling, as well as drawings, are no longer available. This will open a major opportunity serving wide aftermarkets.
Spare parts are natural migration candidates to additive manufacturing. Spare parts are an inherent case of low volume - high mix. A digital inventory is materially less expensive and risky than a tangible parts’ inventory, it allows to produce parts on-demand and save logistics (while increasing sustainability) by distributed manufacturing, using local service bureaus. A digital inventory can better support very large and expensive parts, parts that are rarely used, and even parts for legacy products such as old machines, classic cars, old military equipment, etc.
Not all spare parts can be additively manufactured, and the conversion of parts to additive manufacturing might not be a trivial effort. In some cases, it will be just digital design adaptations, and, in other cases, complete reverse engineering, based on scanning of an old part or assembly. There is reason to assume, that this effort will be well rewarded for, by the advantages of the digital inventory accompanied by locally on-demand manufacturing of the parts. As more companies will migrate original parts to be additively manufactured, in the future, no effort will be required to add such items to digital inventories. Currently, it is estimated that ~5% of spare parts can be digitalized and additively manufactured. With the spread of design for additive manufacturing, this number will continually significantly grow.
So, if the future of spare parts additive manufacturing is bright in respect to standard and preventive maintenance, it will dramatically improve once Predictive Maintenance (using data science and predictive analytics to estimate when a piece of equipment might fail) and Condition based Maintenance (a maintenance strategy that monitors the real-time condition of an asset to determine what maintenance needs to be performed and when) will be widely applied. Additive manufacturing will address the need to manufacture certain spare parts prior to failure happening, allowing to provide the part just in time and in place, avoiding intricate logistics and a long supply chain. A digital additively manufactured part can come with a complete digital identity, to allow easy tracing along the part lifetime. Later, adding data accumulated while using the part and worn part analysis coupled with the use of digital twins, will allow for an ongoing parts improvement process, thus enhancing asset performance, extending mean time between failures, and lengthening uptime.
Considering the efficiency, sustainability, cost savings, adaptability, and equipment up time that will all be achieved by migrating spare parts inventories, and digitally manufacturing, utilizing local additive manufacturing, the challenges of additive manufacturing, such as low productivity (compared to molding) and cost per part, will be well outweighed by the above-specified savings and efficiencies.
I thank Prof. Ron Kenett, Ariel Kenett, and Itamar Yona, for their support