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Hmmm how could they possibly increase the shareholder equity?
Perhaps the 890k warrants to raise $1.3 Million, just the other day when the volume hit just over one million...I wonder if there's any relation....hmmm.
Phew that was a close one.
https://www.aero-mag.com/nadcap-certification-metal-3d-printing-aerospace/
"Completely reproducible processes with full traceability are essential wherever people’s lives depend on the operational safety of end products,”
The criteria for additive manufacturing in metal-powder bed procedures using lasers and electron beams comprise requirements concerning materials management, equipment, the process itself, the qualifications of personnel, and plant and equipment maintenance. Materials management covers everything from procurement and storage to handling and recycling. During the inspection of equipment, emphasis is placed on the testing of process variables, software (including regular updates) and the upkeep of facilities.
Attention is also given to the individual process steps. The manufacturing process is also inspected in terms of the method taken when changing the powder and the procedure followed in the event of system errors. From now on, this inspection will take place in line with Nadcap specifications at regular intervals, enabling the stringent standards to continue to be met in the future.
No worries, I did it in a morning stupor, with one eye open, using the speech-to- text function and fell back asleep for a couple hours immediately after lol.
I'll have to read over it again myself to see if I was making any sense.
Glta
SGLB
Because Farinia doesn't get paid to do marketing for Sigma Labs.
As we've seen, Sigma Labs spending money on marketing, does very little, as our software is very complex and even our current customers, as stated in the most recent conference call, needed a two day course to understand what our software is doing for the AM process.
When implementing a new process into any production method, it takes a lot of time, effort and training, to incorporate the process into a pre-existing process.
In my career, currently as a technician in a quality assurance/finishing division, it is very difficult to incorporate any new processes, even if they are more advanced, for multiple reasons.
Just to name a few, it's difficult to get the operators (similar to Sigma Labs stated issue) to understand why they need this new process, and what it does.
Secondly to that problem, is then education of the operators to then allow them to understand the new process and the value it provides to the company.
Eventually, they agree to adopt the new process, but it takes a lot of time to get through all these steps, especially the more complex the process, and with AM already being a new and extremely complex process, I can completely understand why it takes a decent amount of time.
Another issue is testing and evaluation of new processes.
As companies have already made major investments in the installation of additive manufacturing machines, training of personnel, development of products, processes, verification of said printers, so on and so forth..... The companies want to see a return on investment in the form of printing parts for customers.
Many companies are not willing to spend an extraordinary amount of money on R&D in the fabrication industry.
All of these additive manufacturing companies are either forced to spend a lot of money on research and developmentoh, because they realize they have a yield issue early on.
These are generally the larger companies and early adopters that Sigma Labs has already worked with over the last five years.
Honeywell, GE, Aerojet Rocketdyne, Pratt and Whitney, all have the extensive personnel and resources available to complete research projects that may take multiple years and multiple million dollars of their own money and time paying their own personnel to complete this type of research for a new process.
Other smaller companies however, do not have this resource pool available to them.
They are not able to purchase multiple million-dollar machines and then tell their investors, direct management personnel, and heads of their company, that they will need five years to develop a working process for AM.
They need to start printing Parts as quick as possible to show that they are capable of doing so, regardless of the near-term costs.
It's much easier to say, hey we need an half a million dollars for a CT scanner and a full-time operator to run the machine so that we can make sure we have viable product coming off the line.
As long as the printer is printing, it looks good.
It's a much harder sell for the engineer's attempting to create a viable process for additive manufacturing by incorporating and in process quality monitoring software by trying to convince the same personnel as above, to instead of asking for a simple CT scanner and an operator, to ask for 3 months of exclusive access to the printer or even multiple printers to install and validate a system such as print Rite 3D.
Our Personnel can successfully install and get a system up and running in much less time, as they are familiar with the parameters necessary for particular parts and materials. But for an outsider to use our software for a particular part, they may need to change the entire design if they determine that they are not achieving the desired material characteristics on any part in a build.
This is the world of AM, every new material and every new part requires a certain degree of trial and error and R&D, that no production company is used to spending.
They are used to processes like molding and casting, where once you have a designated material and process for manufacturing, you are then able to do the same thing over and over again with the same exact result.
This is not the case for additive Manufacturing.
Every build and every part needs to be verified during the process.
This is the only way to ensure that both machine parameters were kept, as well as the feature-based qualities of every individual part is kept.
People in production do not like this, because it sounds extremely extensive compared to the quality assurance processes that they are accustomed to with older technologies.
In all actuality it is very extensive compared to older Technologies, but, it is a necessary process required to ensure the quality of each individual part.
Having to spend the time and money on in process quality monitoring is a necessary "evil" for AM.
It is the price you pay for limitless design freedom, weight reduction, material reduction, increased strength of each part.
Additive manufacturing is an extraordinary technology. The learning curve is more complex than the average manufacturing process.
As we've seen, companies even with a multitude of printers, have been forced to do extensive post-process inspections just to create parts to show potential customers they are capable of printing a particular part.
No one is capable of effective mass production currently at this time.
They can use brute force by purchasing hundreds of printers and using extensive CT scanning processes to ensure quality, but end up spending far too much on post-process inspection and also lose a lot of money and time by having to throw out or scrap multiple parts per build due to the lack of in process quality monitoring.
The only way to ensure a viable business model for additive manufacturing of critical Parts, is the combination of an in process quality monitoring technique, combined with only scanning parts with suspect welds, to ensure that although the parameters of the build might have only been met with 95% efficiency, the integrity of the part is high enough to handle critical applications the part will be used.
Sigma Labs has the first to market software for a web based IIoT in ln-situ quality monitoring technique to accurately verify an extensive variety of process parameters are met, to effectively show compliance to design intent, which proves the material characteristics of each individual part is capable to withstand the applications it was designed to function.
Glta SGLB
America Makes TRX event
Many speakers addressing creating a valid proof of concept for Qualification of the AM supply chain.
Morf3d is just one of them addressing this
Contract Manufacturing Supply Chain Use Case (Morf3D – invited)
Their presentation of a " Use Case" should be interesting.
Glta SGLB
Usually, it's common practice to have a counterpoint in an argument, not just claim - to be incorrect without any evidence as to why. Debate class 101..
A simple overlook on my part, could have been easily corrected by an individual, if one knew the correct answer. But, one only discovered an error was made, not the correct answer, which shows one might not know as much as one claims. One would look much smarter if one actually knew the correct answer, instead of just pointing at an error and saying "that's bad."
Similar to Sigma Labs software compared to the competition, sigma labs software is able to extract specific feature based characteristic data from each part, instead of our competitions software, whose software only recognizes when parameters are not met and says "that's bad".
One can surely understand that comparison.
Glta SGLB
Patents to keep an eye on going forward.
"Feature extraction method and system for additive manufacturing"
-- our latest software which specifically finds material characteristics in process that correlates to feature based Qualification of parts based on their individual requirements.
"OPTICAL MANUFACTURING PROCESS SENSING AND STATUS INDICATION SYSTEM"
a classification process that, from the features extracted by the first and second feature extraction processes, distinguishes features associated with a baseline or nominal operating condition from features associated with a deviant or off-nominal process condition; (d) a status indicator that is configured to communicate the results of the classification process at a given instant in time to a human-machine interface.
--shows a simple red/green, nominal/non-nominal weld in process.
Surprisingly, no OEM has a software that can even do this, especially to the detail that sigma labs software can, with their feature based extraction algorithms looking precisely on only the feature areas.
Both these applications have had USTPO action within the past month.
Everything moving forward.
Glta
SGLB
I misspoke, I was thinking of an effectiveness filing.
Thanks for being so diligent.
Glta
SGLB
Volume either coincidentally or could be the conversion of the shares as defined in the s1.
890k shares at 1.47
$1.3M to company if this is the case.
Just so happens Sigma filed multiple s1's right before,
And the price is hovering right around the exercise price.
Effect filing would have a few days before the release to ensure this is or isn't what happened today.
Volume just happens to be very very close to the warrants convertible at 1.47, right after multiple s1s put out, wouldn't be surprised if the corresponding s3 is released within the next 3 business days as required, stating they were converted and sold.
890,000 shares of common stock currently issuable upon the exercise of the Warrants held by the selling stockholders
"If all of the Warrants are exercised at the initial exercise price of $1.47 per share, then we will receive gross proceeds of approximately $1,308,300 to the extent the Warrants are exercised on a cash basis."
UTC to invest $15 Billion over the next 5 years
https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/lorenthompson/2018/05/23/united-technologies-to-hire-35000-in-u-s-stressing-commitment-to-american-workforce/amp/
United Technologies has been expanding steadily in recent years due to a combination of acquisitions and organic growth. It bought Goodrich in 2011, and then merged that operation with Hamilton Sundstrand operations to form a business unit called United Technologies Aerospace Systems. Once the Rockwell Collins transaction is completed later this year, it will be combined with the aerospace unit to create Collins Aerospace Systems.
In the process, UTC (as it is often called) will become the world’s leading aerospace supplier, providing a vast array of propulsion systems, communications gear, flight controls and other items to both military and commercial markets. The company’s Pratt & Whitney unit builds all of the jet engines for the Pentagon’s next-generation fighters, bombers and tankers, and is shaking up commercial markets with its fuel-efficient geared turbofan offerings
ASTM standards award Additive industries
https://additivemanufacturingtoday.com/shane-collins-receives-astm-award-of-merit-for-contributions-to-committee-f42-on-additive-manufacturing-technologies
Might be old info but I don't recall when it occurred.
Additive Industries is proud to announce that its General Manager of the North America Process & Application Development Center, Shane Collins, received the prestigious ASTM Award of Merit.
The ASTM International Board of Directors selected Collins for the Award of Merit for his contribution to Committee F42 on Additive Manufacturing Technologies. The award of Merit was established in 1949 by the Board of Directors and is the highest award granted by the Society to an individual member for distinguished service and outstanding participation in ASTM International committee activities.
“I am honored to be selected by the ASTM Board of Directors for this award and to receive the recognition for my contribution to Committee F42 and the additive manufacturing industry at large”, said Collins.
“Since taking the Chair position of F42.05 Material and Process in 2011, our subcommittee has published over a dozen standards with more on the way. This could only be possible with the dedicated efforts of the entire Subcommittee team for which I am extremely grateful. I would also like to thank the ASTM Staff Members for their help and guidance as we have evolved from a new Committee in 2009 to one that now includes complex development agreements with a number of standards development organizations including ISO”, he added.
The ASTM standards development process is consensus based and requires industry experts to volunteer their time and energy to write critical industry standards.
“What many people fail to understand is the financial contribution by the members’ employers, such as Additive Industries, for travel expenses and time at meetings, said Collins. Without their support consensus based standards development would not be possible”, he continued.
“Additive Industries manufactures the world’s first dedicated equipment for industrial metal additive manufacturing systems and we recognize the impact standards have on the commerce of parts produced additively. We are pleased to support Shane and his standardization efforts with ASTM that will impact the future of manufacturing”, said Daan A.J. Kersten, CEO of Additive Industries
Space expo in CA, Aerojet Rocketdyne.
@AerojetRdyne pioneered many advances in additive manufacturing & continues to drive innovation. W/ the advances we've applied, we're lowering costs, shortening production timelines, & rethinking elements of design. Along the way, @AerojetRdyne is transforming the launch paradigm https://t.co/dSfJDMFx9E
Nicholas Mulé, Aerojet Rocketdyne: the industry as a whole is very bullish on use of additive manufacturing, but no current standards for using it for aerospace-grade parts. We’ve worked to develop our own. #SpaceTechExpo
This session brings together a mix of launcher organizations and government officials, focusing on the
following topics:
Accelerating the manufacturing cycle – How can automation increase the production cycle
and where could this be integrated?
Lightweight materials – Which new materials should and can be used to keep launch and
flight cost down?
Additive manufacturing – Disrupting the way we manufacture components in all sorts of
industries, additive manufacturing (AM) has brought a new perspective to component and
subsystem design. With the first 3D-printed rocket being used for Rocket Lab's Electron
launcher and in Space Launch System’s RS-25 engine, how will AM disrupt the launcher
industry?
What other trends does the launcher market foresee in the coming years and how can the
supplier market respond to this?
Moderator
Sima Fishman , Managing Director, Euroconsult USA
Speakers
Michael S. Laidley , Vice President, OmegA Program, Orbital ATK
Bradley Schneider , Executive Vice President, USA, Rocket Lab
Col. Jon Strizzi , Chief Engineer, Launch Directorate, Space and Missile Systems Center
Prof. Dr.-Ing. Andreas Rittweger , Director, Institute of Space Systems, DLR
Nicholas Mulé , Program Manager, Additive Manufacturing, Aerojet Rocketdyne
I remember this webinar and according to my notes, the only thing I would even remotely consider a very vague dot would be the mention of a "closed loop quality assurance system being the 'future' technology" that is not yet available.
But, as we all know this technology isn't exclusive to Sigma Labs, and just like everyone else, isn't available yet.
The reps did go into decent detail about monitoring the Melt pool, but only mentioned a photographic system to monitor the melt pool.
Other than that, I don't recall a single thing mentioned that hasn't been mentioned before by Trumpf.
Just wondering if anyone can shed light on why this recent webinar was any different from the rest?
What the "dot" was?
Maybe it went over my head.
Thanks in advance for taking the time.
Glta SGLB
That's the strategy...always....
Good idea.
News week, something's a'brewing.
Makes sense to lay down a nice little contract last couple weeks before "summer" officially begins.
Collaboration, NDA, Honeywell continuation, anything would be nice.
Maybe even a production contract.
Mass production promises of major players to their own shareholders are beginning to come closer. They will need our systems and qualification of their printers verified before production begins, this type of verification. Requires test runs and specific printer modifications of process parameters, which can take anywhere from a week to a couple months, based on the complexity of the parts and the expertise of the end user, and number of printers.
If they want to start production before the end of the year as many of our current customers promised to their own investors, they will need to start moving forward.
All speculation besides the timeline facts of production companies based on their statements.
Timeline for software integration based on previous customers and statements from the company as well as the industry as a whole.
Glta SGLB
FDA guidance continues to support in-situ inspection and process parameters including melt pool monitoring data.
Standards and industry acceptance continue to point in the direction of the importance of IPQM data.
IPQA has proven time and time again it provides the most advanced in-process feature based characteristic data.
Continued advances within our software development have allowed to reach the last step before completely closing the loop.
Sigma Labs has the only web based IIoT compatible in process quality assurance software on the market and have proven to directly correlate to material characteristics.
Glta SGLB
https://www.youtube.com/embed/NBMVteWEKGw
Evidence on why it makes sense for a larger entity to *eventually* acquire software from the originator and most advanced solution.
I say eventually because I do not believe it is in Sigma Labs best interest to consider a buyout in the foreseeable future, as we don't have the sales to inflate our valuation to the level it is currently capable of being, with this validation.
Once our value is increased by having tangible evidence of the revenues, combined with the scientific evidence of the validity of our software for mass production within the AM industry, the consideration will be much more reasonable for any of our current customers and large AM entities to purchase a company that provides software capable of real time verification of each part in a build.
http://www.fabbaloo.com/blog/2018/5/12/a-most-strategic-3d-print-merger-the-first-of-many?utm_source=dlvr.it&utm_medium=twitter&utm_campaign=fabbaloo
Glta SGLB
From previous post
Processing parameters have
been shown to influence the microstructure, geometric structure and mechanical properties of direct laser deposition structures (Qiu et al., 2015b).
“Machine manufacturers recognise that in-process monitoring is a key area
for development” (Participant 10).
“In simple terms, a layer by layer
green-red light system is required indicating, good powder, good spread
and good fusion with no defects” (Participant 8).
In turn, informative and
accurate in-process monitoring, will lead to robust quality statistics.
The PrintRite3D® technology (Sigma Labs Inc., Santa Fe, New
Mexico, USA) gives an example of the current industrial standard.
Optical
monitoring detects deviations of the geometry of the build from a
reference ‘gold standard’ image, up to a resolution of 100 µm in-plane
(Sigma Labs, 2017). Changes in the temperature of the melt pool are
detected from a change in the emissivity.
***"The PrintRite3D® technology (Sigma Labs Inc., Santa Fe, New
Mexico, USA) gives an example of the current industrial standard."***
The barriers to the progression of additive manufacture: Perspectives from
UK industry. (Link at bottom) 2018 paper with Sigma Labs specifically mentioned.
This research has drawn the following conclusions:
Knowledge of AM held by current engineering graduates is insuffi-
cient. In industry, this problem is exacerbated because knowledge is
rarely unified and comprehensive, instead it exists in pockets which is
highly dependent on personal experience. This barrier requires a
paradigm shift in education to satisfy the need for graduates with
deeper understanding and experience of AM.
Cost-benefit analysis is highly application specific and incorporates
an element of uncertainty, compounded by the lack of well-rounded
AM knowledge filtering into industry.
DfAM necessitates that designers have a new perspective, requiring
increased creativity, underpinned by AM specific knowledge and
experience.
Software for AM is currently severely fragmented in industry and
research. The process requires streamlining through the design,
optimisation and build preparation processes to create software that
is tailored to DfAM yet platform independent.
The lack of materials available for AM either inhibits the manufacture
of certain parts or requires them to be redesigned for different ma-
terials. Industrial application requires a parameter dependent oper-
ational window for materials to speed up the translation of concept
through the end product.
Bed size and the speed of AM are limitations for mass manufacture.
In-process monitoring systems are crucial to ensuring good quality
statistics. In the long-term, industry requires closed loop systems
which compensate for in-build fluctuations.
Quality control is predominately dictated by in-house specifications
and requires more guidance from overarching standards. FEA can no
longer support the validation of designs for AM, where the mechan-
ical properties are highly dependent on the processing parameters.
Thermomechanical modelling can aid design, but it is time and
computationally intensive. Currently the most robust method of statistically quantifying the mechanical properties, in the presence of
uncertainties in the build process, is material equivalence.
Line of sight difficulties for inspection and finishing are a key focus in
both research and industry. Where internal features can be finished by
mechanical, magnetic, electrical or chemical techniques, surface qual-
ity quantification is still limited by deficiencies in imaging resolution.
https://www.sciencedirect.com/sdfe/pdf/download/file/pii/S0925527318300926/1-s2.0-S0925527318300926-main.pdf
Software sector of AM projected to catch up to growth of hardware and materials sectors, (~80% and 44% respectively)
Software trailing at 26% but will grow as market catches up from black box printers to production.
https://3dprintingindustry.com/news/forecast-says-asia-pacific-spend-3-6-billion-3d-printing-next-3-years-128165/
Rolls Royce on the importance of collaborations, partnerships, acquisitions.
http://www.rolls-royce.com/media/our-stories/discover/2018/can-big-business-innovate-like-a-start-up.aspx
Make sure to click thru every "users" tab.
http://www.ccam-va.com/industry-members/
Melissa Orme (Morf3d)
Directly mentions the future of Additive being IIoT and the collection of in situ monitoring data necessary for AM.
Followed by Aerojet Rocketdyne speaking on several applications they are using AM.
More confidence combined with a much more advanced product, combined with production contracts in hand with VERY significant companies.
No longer just jtda's and nda's and "dots", we're talking numbers with real big fish about purchasing PR3D software for their AM production purposes.
Looking Great.
May take some time, but talking contracts is much better than just collaborations and r&d.
Very close to some real production $$
Or OEM royalties, or software royalties...or all of the above.
Glta SGLB
Fantastic call.
Improved relationships and developments with our biggest clients (ie. Pratt and Whitney, Siemens) understanding the value of our software
Combined with Sigma putting in the time and efforts of a 2 day seminar to educate the production teams of customers to fully understand what the software is doing for them.
John stated the major focus on integration directly with OEMs, as well as the possibility of being integrated into software platforms such as Siemens snuck in there at the end of the call.
Representative from the Maxim Group, a spin off of Ernst and Young, who control 2.3 Billion in client assets, and generally invest in companies from $50 million valuations and up to the mid cap range had many questions, which is always good.
John stated the production companies and Sigma found mutually agreed results during these educational seminars which shows continued use of Sigma Labs software.
Like I just stated today, our most recent product releases were are most significant, as John also stated, was just recently proven within the last 4-5 months, that especially showed Sigma Labs software is capable of mass production in-process quality assurance of AM parts.
John spoke of current "negotiations" with our current production customers, showing sales are coming based on usage and the value Sigma provides.
He also mentioned dialogues and collaborations occuring with production companies.
Sigmas most recent product release allows for both OEM and software platform integration. The "Intel" inside.
Recent funds used to acquire personnel and equipment assets for closed loop capabilities.
Enjoy your summer, I'll be planning a vacation.
Glta
SGLB
Services in connection with our obligations as an SEC reporting company cost us slightly over $550,000. However, other legal fees of $333,046 were paid in 2017 compared to $126,992 in 2016. The increase in these fees was primarily from those paid conjunction with our February 2017 public offering that resulted in net proceeds of approximately $5,225,650.
Our net loss was approximately 4.6M
Nearly 900K in one time obligations from SEC and legal fees,
Plus 750k investment in Morf3d and Jaguar.
= Over 1.5M expenses, of which 750k we are getting back.
So, 4.6 - 1.5M in one time expenses = 3.1 million if you remove these one time expenses.
3M per year burn rate is extremely low, and once production $$ comes in, profitability will be plausible within the next couple years or less.
Depends how the market reacts to our first announcement of a multi-machine installation for production to Honeywell, Woodward, Siemens, or Pratt and Whitney or anyone else we've signed NDAs with...
I'd say the reaction should be impressive to say the least.
Scientific documents that prove Sigma Labs software is a validated way to inspect additive manufactured Parts in process and ensure their quality is not speculation.
It has been proven through upwards of a dozen third-party studies utilizing Sigma Labs software.
Including but not limited to projects completed by the USAF, NIST, DARPA, NASA, 3DSim, and other 3rd party scientists, researchers, and engineers.
These studies validate Sigma Labs software over and and over again.
Sigma labs contracts with multiple companies for production proves the studies are correct.
Many of these were done in 2015 to 2017, so yes I can understand that those invested before that time would be very upset as Sigma Labs was clearly still doing R&D on their own products as well as with others.
I've been researching Sigma Labs and the AM industry since around 2014, and realized at that time they were still very much an R&D company with a product that was not ready for commercialization, because these studies that validated their software was not yet complete, and they had multiple software application still in development. This was well known and stated by the company, and not a lie like it is being insinuated.
The first print Rite 3D release with industrial Internet of Things capabilities was in 2016 and was still a 2d mapping. DEFORM was still in development until mid 2016.
In May of 2017, just one year ago (very short time) print Rite 3D 2.0 including contour was released and announced with IIoT capabilities.
This was the first time in the industry that a real time in-process quality monitoring software was made available to the market.
One. Year. Ago.
The latest release of 3.0 and 3.02 that everyone mocked was actually a significant leap in our software.
The Version 3.0 hardware package offers a much-reduced footprint and lower costs. A stand-alone computer console at the machine is no longer required, the company notes. The on-board sensor suite collects and feeds raw data to a computer that performs Stage 1 data pre-processing and sends reduced-order feature data to a cloud-based server for Stage 2 analysis and learning.
And with the 3.02 version of PrintRite3D INSPECT® also provides a complete production-level package of statistical process control (SPC) software apps, which digitally supports serial production quality monitoring at a build, part, layer or scan level, thereby ensuring continuous operation within the qualified processing window.
Serial production.
IIoT Web based solution
For real time Quality Assurance
Of AM produced parts.
First to market, only of its kind available in the industry at the moment.
So as you can see, those who were doing actual due diligence on the company around 2014 should have been able to realize that both the industry and the company we're still developing their main products, as well as the industry was not yet in mass production.
When the first iiot compatible software was released in 2016, Sigma was still on the OTC exchange and a speculative investment.
Sigma Labs raised significant funds and successfully uplisted to NASDAQ in early 2017. Just over one year ago. Very short time.
With are very recent release of version 3.02, that is specifically for mass production, which is where the most $$ in the industry will be made, Sigma Labs has successfully upgraded their software to meet every need for a real time 3D mapping of Feature based qualities necessary for the verification of each individual part over multiple machines.
That was released 2 months ago.
So again, those who have done actual due diligence on the industry as well as Sigma Labs understand that the value of our software thrives in mass production, and has been verified for years both by the company as well as a plethora of DoD entities, large corporations, third-party scientific studies, over multiple countries.
All investors know that it is imperative to study the complete industry as well as to include third-party studies as well as investigating the opposition such as CT scanning, x-ray, other in process nde techniques, and so forth.
Throughout every study I've come across, the most effective way to verify the quality of additive manufactured Parts in process is with Sigma Labs technology which uses feature-based quality extraction to reduce the amount of data and allow for real time IIoT, In-process Quality Assurance over multiple machines for real mass production capabilities.
Do DD
Glta
SGLB
It's actually today, but we will see the 500k from Morf3d in the books, any revenue from recurring contracts, Jaguar 250k was recently due, will mention recent 850k raised, all that combined with current cash puts us thru 2018 easily.
Many of our expenses in 2017 were a result of the uplisting to NASDAQ and expenses associated with the raising of over $5 million.
Our expenses for this year will be solely for the company, not SEC one time payments and those associated with that necessary uplisting that unfortunately costs a significant amount of $ to do.
With Sigma Labs now over that hurdle, as well as the EAP discounts ending, and focused on production contracts, our margins will increase.
Sigma improved their position as they transitioned which was pretty impressive by John Rice and others involved. The strategy to hire strong IP entity for our patents secures our position.
We now have the proper funds and personnel to commercialize our product across multiple markets and countries.
Production is coming.
Which is, and has been, Sigma Labs target market. That is where out technogies are most effective for our customers.
Mass production= more printers = companies printing more parts = higher need for verified data for each part = more PR3D installations = more $$
Glta
SGLB
Sigma Labs is transitioning from R&D to production.
Previous revenues were low because R&D doesn't pay.
More printers, more parts, more money.
Sigma Labs included.
Hmmmm you must not have spoken with many AM companies heading into production lately.
Mass production is the current topic of discussion.
Currently, companies are installing printers at facilities with these exact, or more capacity.
Do DD
Glta SGLB
Production contracts with 4 multi-billion dollar operations doesn't qualify for sustainable revenue?
The EAP programs all end this year... production money will follow.
R&D time is over, the past revenues will not compare to real production runs.
Multiple OEM contracts as printer sales increased 80% YoY, those contracts were signed for several years ongoing.
Yes I know we have yet to see revenues from that, because Additive industries had a late release.
Trumpf customers will need real IPQM
EOS customers will need real IPQM.
SLM customers will need real IPQM.
Farsoon customers will need real IPQM.
Whether it's offered from the OEM or purchased directly from Sigma, our Technology is needed for real mass production of AM to be financially viable.
Does that not qualify for sustainable?
Glta
SGLB
Where in the industry has mass production of AM for highly critical parts been done without extremely expensive post process inspection occurred?
It hasn't.
Any company claiming they are able to mass produce parts and ensure Quality without extensive post process inspection is either at extreme risk of producing parts that are not of Quality nor do they have the evidence to prove each part was printed correctly.
Compliance to design intent.
Read Honeywell's studies on the importance of this.
Read Moog's presentations on this for a global supply chain.
Sigma Labs software shows EVIDENCE the parameters of the part were printed correctly.
These Aerospace, medical, and military entities need to show evidence each individual part was printed correctly.
Sigma Labs is the first, and at this time only, with REAL TIME QUALITY ASSURANCE WITH ACTIONABLE DATA OVER MULTIPLE MACHINES.
This is mass production.
This is what everyone needs to complete actual mass production runs.
Web-based, integrated software to ensure Quality in real time. Not after the fact. Not after any wasted time or material, especially when you're talking about building simultaneously over 10, 20, 100 machines.
Glta SGLB
Do you consider our contract with LZN
(multi-billion dollar operation with ties to every major AM player in Germany)
specifically for standardization for mass production of additive manufacturing purposes not a success story?
Do you consider our contract with NIST specifically for standards in the US not a success story?
To consider our production contracts with Pratt & Whitney, Woodward, Siemens, solar turbines, Honeywell not a success story?
Do you consider our work on three phases of a DARPA projects not a success story?
Do you consider our work with the USAF and aerojet Rocketdyne for high-end Aerospace parts not a success story?
Do you consider the years of research papers and scientific Publications written by scientists, engineers, researchers, all with decades of experience in their field and master's degrees or PhDs, that specifically use Sigma Labs software and the data they collected shows direct correlation to our software and a Quality Part not a success story?
Do you consider our collaboration with 3D Sim who was acquired by ansys who are currently in collaboration with Sigma Labs to continue to validate their prediction software directly correlates to our in process quality assurance software not a success story?
When the industry enters real mass production we will see the money.
All of the contracts that we currently hold signify that these companies understand the value of our software and will use it in these production settings.
The United States isn't ready because of the entities involved in the standardization are lacking the personnel and proper data to complete them.
Sigma Labs just signed a contract with NIST to solve the identified Gap in the Technology of AM to qualify powder and parts in real time.
The next meeting is November of this year.
Printer sales increased 80% year-over-year.
Companies are talking about creating mass production facilities with hundreds of printers. Not just 10 or 20, the major players are talking about facilities with upwards of 400 printers (was the highest quote I heard at RAPID).
As the number of metal am machines continues to grow exponentially, Sigma Labs software will capture a percentage of that market and be a very successful company.
Sigma Labs already has contracts for production with some of the largest US companies in the industry.
That is your success story, the money will come when production begins.
Sigma Labs continues to secure patents to ensure they separate themselves from the competition.
Glta SGLB
Based on AM activity acceleration in other countries, ie. Germany, Singapore, Sweden, China, I would not be at all surprised if adoption of IPQA with our customers abroad happens sooner than in the US.
The US kind of dropped the ball on standardization of this technology and NIST and ASTM openly admitted it during the AMC conference.
The US is now playing catch-up and trying to sort thru the mess of multiple programs that were government funded that basically were investigating the same issues.
Other countries were much more organized and had much better research funding.
Luckily, Sigma Labs has worked and is working with several foreign companies who are directly involved in standardization, and have been involved in many research projects on the subject.
Global adoption of IPQA for mass production seems very plausible with our contracts with the largest players in multiple countries.
Germany is a huge market, and we have a strong foothold.
Siemens has an extremely strong global presence.
Additive Industries continues to grow and their end to end platform and open architecture is very advanced compared to basic black box OEMs.
Pratt and Whitney has basically unlimited resources.
USAF and Aerojet Rocketdyne have very highly critical applications which come with big $$ per part.
Honeywell, also global and unlimited resources and DoD connections along with our DARPA program.
This is only the beginning.
Glta SGLB
Sigma, Sigma, Sigma.
Some great reads.
With our new Rep in Germany, I think we should keep our eye on DMG Mori
Glta SGLB
https://www.google.com/url?sa=t&source=web&rct=j&url=http://www.metalliskamaterial.se/globalassets/3-forskning/rapporter/2016-03898---state-of-the-art-for-additive-manufacturing-of-metals-2_1.pdf&ved=2ahUKEwiH9dWUsobbAhWL8YMKHdtEDBEQFjANegQIABAB&usg=AOvVaw2sANafUakSMgvBeBREieZJ
https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.hsfk.de/fileadmin/HSFK/hsfk_publikationen/prif144.pdf&ved=2ahUKEwiBv5vatobbAhXC4IMKHe-FDL04ChAWMAZ6BAgDEAE&usg=AOvVaw2CF2jAldwrbegVhEV7jcmp
Application 4893 update
05-14-2018 Reasons for Allowance
USTPO site description
"The reasons for allowance is the examiner’s
statement explaining the findings of patentability
for the claimed invention
We have seen that the courts do consider examiners’ statements of reasons for allowance,
thus clarity of the record will assist the courts and the public in assessing the extent of the rights being granted"
Extremely small US based company with low over head as a software company, with a continuing global distribution network.
Impressive.
China, Germany, Korea, Singapore, Sweden, Netherlands, US of course...I know I'm missing some but you get the idea.
Semi- applies to Sigma Labs, exactly what Sigma does, just specifically for AM.
This was the main point of many presentations at the AMC meeting I just attended in Pittsburgh. Especially when it came to the presentation titled accelerating additive manufacturing standardization by ASTM. This is the recent collaboration between ASTM, EWI, NIST, Auburn University, and NASA.
As we all know, EWI, NIST, and NASA, have all worked, or are currently working with Sigma Labs technology.
All have put out scientific research papers specifically mentioning Sigma labs inspection Technologies as being effective for in process quality assurance.
Our specialized software allows for
Selection of particular data and extracting it, comparing it to nominal conditions and reporting back errors in real time.
Complex algorithms to select specific data sets and make the amount of data reduce from terabytes per layer to megabytes.
Real time qualification over an IIoT system , enabling mass production over multiple machines simultaneously, with actionable data.
Here is an article touching on the subject on the importance of analytical data, precise selection of said data, and using it to make actionable decisions to improve operations.
Beyond the Black Box of Predictive Maintenance
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By Beth Stackpole , Contributing Writer, on May 9, 2018
The Industrial Internet of Things promises to usher in an era of predictive maintenance, but manufacturers are moving slowly, struggling to transform asset data into actionable information.
As part of the crescendo of noise surrounding the Industrial Internet of Things (IIoT), predictive maintenance is generally viewed as one of the most compelling initial arguments for moving forward. As manufacturers make their way into implementations, however, they’re finding that collecting data from connected assets is just the tip of the iceberg. Identifying the right data, contextualizing that data so it’s mapped to desired objectives, and linking the entire process back into existing workflows is where the real heavy lifting comes in, creating obstacles for all but the most progressive companies.
The sheer magnitude of the leap—moving from decades-old, clipboard-based data collection and maintenance processes performed by onsite plant personnel to digital workflows that can be automated and orchestrated by remote workers—requires a certain level of confidence and digital infrastructure maturity not yet pervasive among a majority of manufacturers. Many legacy industrial assets are still not outfitted with sensors, let alone connected, which impedes any potential data collection. In addition, most manufacturers don’t yet have a clear picture of how to create and apply machine learning and predictive models to drive these next-generation maintenance workflows.
“Despite the IIoT buzz, there’s a lot of FUD (fear, uncertainty and doubt),” says Kevin Starr, advanced service global program director for ABB. “Companies know there really is an industrial revolution on the horizon and there’s a lot of discussion, but they don’t want to make a mistake and have to redo their efforts.”
Manufacturers have latched on to the concept of predictive maintenance as a strong return on investment (ROI) use case for IIoT, hoping to benefit by eliminating downtime, streamlining service calls and reducing maintenance costs. Although those metrics are a viable upside to IIoT-enabled predictive maintenance applications, manufacturers are realizing that it’s a lot harder to scale from pilot project to production, acknowledges Rob Patterson, vice president of strategic marketing for the ThingWorx IoT platform at PTC.
“Many companies get stuck in the pilot purgatory of IoT projects,” Patterson says, explaining that manufacturers can get tripped up by miscalculating what it takes to design and build the predictive maintenance models while lacking the on-staff data scientists and domain experts who are critical to getting projects off the ground. “People have the perception that it’s a magic block box that ultimately produces outcomes and predictions with very little involvement from human hands. But this a fundamentally flawed perception of what machine learning entails.”
Beyond data and analytics, creating new closed-loop workflows is key to getting predictive maintenance applications right. Source: GE Digital
The predictive maintenance stack
As with any new and transformative initiative, the fact that there isn’t a packaged, out-of-the-box solution for predictive maintenance is a hurdle for most manufacturers. Mature predictive maintenance applications require more than just connected and sensored industrial assets, which in itself is no easy task. An effective solution is architected around a cloud platform for ingesting and aggregating all the various data points, analytics and machine learning capabilities for combing through data and unearthing insights, integration capabilities for access to other core enterprise systems like enterprise resource planning (ERP) or service platforms, a well-designed user experience based on dashboards that empower the maintenance worker to easily make the required fixes, and process changes to tie the predictive insights into existing maintenance service systems and workflows.
“This requires a fairly decent level of IoT technical maturity,” notes Alan Griffiths, senior industry analyst specializing in digital transformation and IoT for Cambashi, a global research firm. “The technology is coming along, and some companies are using it effectively. But not many are putting implementations together at a mass production level.”
In addition to asset data, contextual data from external sources and enterprise systems is also critical to creating predictive maintenance models. Source: Rockwell Automation
Griffiths says some manufacturers have been doing a form of predictive maintenance for some time—tracking assets via sensor data to understand, for example, when a particular motor type should be replaced. But this is typically done more at an aggregated level as opposed to getting a window into the condition and failure potential of one specific asset. As companies move into the realm of IoT-enabled predictive maintenance, they typically first do simple monitoring to issue alerts if a temperature goes above a certain threshold, for example, and then move on to control and optimization, whereby they apply analytics to the sensor data to gain insights into failure patterns and preemptive fixes, he explains.
Though many customers of Fluke, which provides computerized maintenance management software (CMMS) and enterprise asset management (EAM) software, are talking about predictive maintenance, they don’t fully understand the concept nor have they built a proper condition monitoring foundation to capture the base data and provide context for whether something is about to fail or deteriorate, says Kevin Clark, vice president of Accelix. Accelix is a new integration layer that connects Fluke’s eMaint CMMS with a variety of connected tools such as sensors as well as third-party systems.
Customers can easily start collecting and aggregating data on their assets via the Fluke Connect Cloud, Clark explains. The hard part is knowing what that data is telling you. “The basics of predictive is understanding the data,” he says. “This is why predictive hasn’t taken off—because it hasn’t been simple enough.”
Condition-based data in the Fluke Connect Cloud can send alarms if an asset is running too hot or vibrating too much, triggering a workflow to an operator, which lets companies move from condition-based monitoring to condition-based maintenance. Even so, Clark admits that’s still not predictive. To get there, companies need to go through the proper exploration to figure out what kinds of failure modes they want to detect and then what kind of data, beyond what’s collected by sensors, is critical for creating context for a more actionable analysis. “It takes a study of your equipment and the processes around that asset to truly understand what a predictive point is,” he says. “Most haven’t gotten there yet because it’s hard.”
Manufacturers should first take a step back and identify what assets are the most critical, and thus ripe for predictive maintenance. Implementers should distill the equipment by manufacturer and equipment type, understand which equipment impacts the business most and clarify what possible failure modes are the most dominant, says Joe Nichols, chief operating officer of industrial applications for GE Digital. Once that exercise is complete, there’s a need to identify data that can contextualize the findings—whether that’s information from another enterprise system that can shed light on past maintenance records or on-going quality issues, for example, or even external resources such as weather or geospatial information.
Subject matter expertise on issues like when to make repairs, the most common periods for asset downtime, and the root cause of common failures is also critical to creating predictive maintenance models, according to Phil Bush, product manager for remote monitoring and analytics services at Rockwell Automation. “You need to look at patterns to develop correlations between certain performance data and events you’ve seen in the past and what normal patterns of behavior are,” he explains. “From there, you can tell if something is wrong or not.”
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Uptake’s cloud-based analytics platform ingests data from machine sensors, adds contextual data, and helps build models for predictive maintenance. Source: Uptake?
The data dilemma
Getting the underlying data right is also critical to fueling the right analytics. Not only is it important to identify the right data resources, you also need to ensure the data is accurate and at a granular enough level to enable predictions, notes Michael Donohue, vice president for thermal energy at Uptake. Uptake markets a cloud-based system that overlays existing ERP or supervisory control and data acquisition (SCADA) systems, ingesting and analyzing sensor, enterprise and even contextual data such as weather and lightning strikes to deliver insights related to performance, energy optimization and predictive maintenance.
MidAmerican Energy, an energy provider serving nearly 1.5 million electric and natural gas customers and a wholly owned subsidiary of Berkshire Hathaway Energy, turned to the Uptake predictive analytics suite to increase availability and optimize performance for turbines in one of its wind parks. By ingesting data from the turbines and doing analysis work, Uptake was immediately able to identify that something was off with one of the main bearings of a particular turbine because it bore a signature similar to prior failures that had previously led to a catastrophic gearbox failure, Donohue explains. Uptake let the MidAmerican Energy engineers know, and the team was able to issue a quick fix for less than $5,000. Now, months into the Uptake initiative, the company has saved $250,000 by finding a handful of similar anomalies on the more than 10 percent of turbines now under management.
“The ability to plan ahead and see anomalies and failures ahead of time, be able to plan those, get technicians into the tower one time to make repairs—that’s the leading edge of maintenance programs,” says Mark Jeratowski, maintenance manager for MidAmerican Energy.
Just as important as the data is creating closed-loop processes and integration into other enterprise systems so you can automatically initiate a maintenance action when required. To that end, GE is tightly coupling its ServiceMax cloud-based field service management with its asset performance management (APM) software to close the gap, facilitating work orders so problems get fixed proactively and providing contextual information in areas like service history, including previous failure information and frequency of break-fix issues.
“We’ve seen people implement predictive systems, manage their data, uncover anomalies they don’t like—but they don’t have a systematic way to close the loop on alerts coming out of that,” Nichols explains.