Metal AM Summer 2020

Page 148

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Operational Excellence in metal AM

Production efficiency 100% Traditional manufacturing

1 sec.


50% -

Metal AM

2 days


Fig. 2 Production efficiency shown by conventional Overall Equipment Effectiveness (OEE) and Magnitude’s Additive Manufacturing Index (AMI)

In the case of AM, this approach was adopted early on, since it was commonly understood as a ‘decent’ rule-of-thumb approach. However, unlike traditional methods, AM had many factors that made it difficult to implement.

basis. Complicating things further, there is a fundamental gap in the manufacturing industry’s knowledge of AM machines beyond prototyping; hence, factors such as scrap rate and production efficiency are typically ignored after the initial

“...with the CAM software from machine manufacturers being, in essence, a black box of proprietary algorithms, it is impossible to determine what could be done to improve the process.” To begin with, additive process steps take days to execute instead of mere hours, and vary significantly between different machines, materials and even production runs. Next, with the CAM software from machine manufacturers being, in essence, a black box of proprietary algorithms, it is impossible to determine what could be done to improve the process. Adding to this, a single project can take weeks to deliver and is mixed with other production parts. All this leads to an inevitable conclusion: accounting principles cannot be used to reveal the enormous losses in efficiency occurring on a daily


business case is made. Since the technology is still evolving by leaps and bounds, the rate of change in metal AM machines is making this problem more acute for manufacturers to track and price.

Where are we now? As the demand for production components has become a legitimate possibility, these questions began to enter discussions within AM projects, but it was tough to step out of the approach ingrained in our common consciousness; namely,

Metal Additive Manufacturing | Summer 2020

that we don’t charge for parts based on system efficiency directly, but rather use an hourly rate as a proxy. Unfortunately for manufacturers, these efficiency losses are significant; in the best case, they consume an organisation’s complete profit margins, and in the worst case, they amount to over 50% of machine capacity. In order to maximise the quality and profitability of AM machines, Magnitude developed the software solution Uptimo to quantify AM production performance down to the voxel and use this information with the help of predictive performance analytics to automate the decision making process in the production chain. With machine learning and empirical results, Uptimo anticipates failures before they occur in order to deliver higher profit margins, regardless of what is being manufactured. Based on our expertise, here we share the first level efficiency gains that any organisation can obtain to increase profit margins while improving productivity and production quality.

Why aren’t we measuring efficiency properly? For the most part, traditional manufacturing efficiency has relied on Operational Excellence metrics in order to quantify the efficiency of any production process. Through the use of Overall Equipment Effectiveness (OEE), we can quantify the productivity of a system and use that to adjust the pricing that we offer for services rendered. OEE lends itself elegantly to any manufacturing process because we can break down the problem into three simple measurements: uptime, speed and quality of product, all measured out of 100%, through what we refer to as Availability, Performance and Quality. Multiplying these values then gives us the production efficiency. This method, however, is not effective when running a metal AM system, specifically because of how the metrics are determined and the

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Vol. 6 No. 2