By: Brian Crotty, Manager Marketing & PR, and Anna Schröder, Junior Marketing & PR at 3YOURMIND
Technology advances alone are not enough to drive additive manufacturing into production workflows. But simple building blocks, such as best practices, management acceptance, and software to streamline production, can make the process of integrating additive into industrial manufacturing more seamless and direct.
The benefits of additive manufacturing have been validated by the leap into serial AM by almost every global manufacturer. But to bring additive into wider adoption, there are still several steps that have nothing to do with the intelligence of a single engineer. Instead, it will be a result of the entire industry building structures, best practices and a shared mindset that allows companies to pull the maximum value from this new technology.
Maximizing value using additive manufacturing
A strong additive manufacturing business case typically saves a company 20 to 80% on production costs or reduces lead time to one or two weeks. A struggle for almost every engineer is how to determine which parts in their inventory are a good fit for additive. As Deloitte mentioned in their report “Challenges of Additive Manufacturing”:
“Even though current engineering graduates may have learned about the technology, it is unusual to find potential recruits who understand the holistic capabilities of the technology. Additive Manufacturing is not a technology for specialist technicians like welding, for example, but rather a field for generalists able to combine such different disciplines as mechanical, fluid and material engineering.”
When we speak of maximizing AM value, it’s clear that cross-disciplinary thinking and an understanding of technical and economic influences are key ways to ‘find the gold’ in a company’s inventory. The increasing use of 3D printing for tooling, jigs, spare parts and production line innovations at companies like Heineken and Ford are great examples. But Design for Additive Manufacturing projects can also simplify assemblies or optimize component structures. These ‘backstage’ applications are opportunities to respond to real world logistic challenges and technical problems in near to real-time.
These backstage applications offer an immediate, profitable part. Helping to find that part are software programs such as CAD software that simulates and analyzes a part and PLM production programs that gather data for various methods, while other tools such as the AM Part Identifier, from 3Yourmind, review economic and technical specifications to score the suitability of parts for additive manufacturing. Companies like Additive Minds (EOS) and Additive Manufacturing Solutions (AMS) who analyze part information to select AM-suitable parts have also found success by balancing technical and economic criteria.
Tips on balancing technical and economic criteria:
Step 1: Train engineers to use data
‘Knowledge is power, but data are gold.’ Every industry on the path to digitalization needs to keep this saying as a mantra. Industrial manufacturing already uses some of the most advanced digital systems in the world for stock level management, logistics, predicting supply and demand, and aiding the humans on their production floor. But because of the scale of production, the huge cost of downtime, the complexity of production steps and increasing demands on product design, there’s little time left for integrating new systems – particularly ones that require fundamentally readjusting production workflows like additive manufacturing.
Only in the last several years has additive manufacturing been used at scale. That means that the principles of DfAM (Design for Additive Manufacturing) and the process steps are being validated for the first time. But companies don’t need to work in the dark – as long as they act like programmers by building in methods to anticipate and test production data ahead of time. Huge advances in the accuracy of simulation and PLM software can predict the results of AM production and post-processing, for example. Just as important is accepting there will be numerous challenges in an industry technically still in its infancy. Analyzing production data will be the best way to make the required solutions visible.
Manufacturing is complex, and it stands to make much faster gains by placing experienced engineers (who know the “O-tone” of production machines) alongside young engineers (who are digital natives to manage the data streams). Such a team will create a new treasure chest of intellectual property for overcoming technical issues associated with AM. As stated in the Wohlers’ report, at this stage the biggest challenges can only be overcome by trial and error.
We expect universities will continue to adjust their curriculum so mechanical engineering courses understand principles like dynamic design and data analysis for production evaluation. But also that business management and workflow engineering degrees will increase in prominence to control and optimize the input of data into these systems.
Step 2: A mindset of patience and endurance
It’s clear additive manufacturing workflows have a different set of requirements than traditional manufacturing – they require an even higher level of data management and distribution. Companies are currently solving this challenge by placing AM production in parallel to their current production. But what’s needed is a change management program to more closely integrate the two production systems.
Software will be a key driver in this change because it bridges the ‘knowledge gap’, commonly cited as the biggest inhibitor to scaling AM. Software helps to standardize data transfer between ERP, MES, AM production and design software. It will be able to translate information from older manufacturing techniques to additive. For example, the tolerance ranges for surfaces at the contact points – a key metric for quality assurance – becomes the maximize size and location of support structures in the AM processing stream. These systems and these data take time to build in a new organization, and it is important that the expectations are set correctly at the outset of the transition.
Over time, the software will become an assistant that guides the AM production process. Assistants always need a training period to be worked into a company though. Smart algorithms and machine learning are helping to speed up the process, but at the end of the day, there is no substitute for knowledge accrued from time spent on the production floor. And that transition to seamless production looks more like a marathon than a sprint.
Step 3: Software to enable AM production execution
Many companies have already proven that additive manufacturing can be integrated into serial production. In 2018, companies like Ford, Airbus and Adidas were putting 3D printed parts into serial production. They have crossed the initial barriers of dedication, training, experimentation and problem solving. Outside of this relatively small innovation group that established the production workflows, however, there are still bottlenecks to overcome – production access, management and tracking.
There is a general resistance to change which will shift naturally in the coming years as financial and technical benefits become visible. The sharing of knowledge in public forums like AMUG (Additive Manufacturing Users Group), America Makes in the US, and Additive Manufacturing Forum and Mobility Goes Additive in Europe will be instrumental in facilitating this change in mindset. These institutions bring successful applications into the spotlight and provide information about best-practices that can be extended into production.
Software will provide another important key to increasing production, as it’s only with a structured data system that the production parameters and customer requirements will be delivered to the right places on the value chain. Tracking using software will allow for the necessary quality assurance to integrate seamlessly into the complete process chain. For the craftspeople and technical engineers that understand the importance of steady, monitored processes, this level of detail and tracking is the only way to scale execution over the long term.
Long-term solutions to scale additive manufacturing
Since 2014, 3D printing machine investment for production has grown by another 50% to 2.9B, according to Deloitte research. But the majority of AM machines are running under capacity. The main hurdle is to connect the knowledge of the business, innovation and purchasing departments. Each holds a different piece of information on how to get the most value out of AM technology, and it becomes even more complicated when coordinating with production engineers and employees who need to put AM parts into products that are used every day. When that knowledge comes together, the resulting use cases become game-changers for manufacturing.
The past several years have proven that technology advances alone are not enough to drive additive manufacturing into existing production workflows. But if simple building blocks are put in place–using data to define best practices, a change in mindset at management level, and software structures to streamline production–the pathway to integrating additive into industrial manufacturing will be a far more seamless and direct process.