Latest News & Events
Santa Fe, NM – January 14, 2012 - Sigma Labs, Inc. (OTCBB: SGLB) announced today that is has been awarded two U.S. Patents. The first award is for a new class of reactive material known as ARMS - Advanced Reactive Materials and Structures - that could be very useful for the future design of more energetic munitions able to deliver equivalent explosive power in a device of roughly half of the weight.
SANTA FE, N.M., Sept. 25, 2012 -- Sigma Labs, Inc. (SGLB) announced today that it was awarded a contract from one of the world's leading suppliers of power generation systems to develop and demonstrate advanced manufacturing repair technology for land-based gas turbines.
SANTA FE, N.M., Sept. 13, 2012 -- Sigma Labs, Inc. (SGLB) announced today that it has signed a non-binding memorandum of understanding ("MOU") with Morris Technologies, Inc. (www.morristech.com) ("Morris Technologies"), a world leader in the field of Additive Manufacturing and 3D printing, which sets forth the parties intent to explore the formation of a joint venture for the purpose of commercializing Sigma Labs' PrintRite3Dâ„¢ technology for the Additive Manufacturing industry.
Methodology
IPQA® is forward-looking, on-machine, real-time feedback on product and process quality.
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The work that B6 Sigma has done advances the state of the art [for GMAW welding] in several specific ways...
FAQs
Q. How is In-Process Quality Assurance different?
A. In Process quality assurance, or IPQA, is based on preventing errors, not inspecting for errors or potential flaws after the fact. IPQA seeks to measure process dynamics in real-time and on the machine tool to immediately flag conditions which are off-normal. Traditional post-process inspection by comparison can only identify potential flaws after they have formed, often long after the manufacturing process that originally created the part.
Q. How Does IPQA Work in Practice?
A. IPQA is a multi-step process that allows to user to differentiate normal from off-normal process behaviours. The various process steps can be summarized as shown below.
I – IDENTIFY. The first step is to identify the key in-process behaviours which are of interest, and the associated measurements which are possible. Also, the definition of a good part or a normal process condition must be established.
C – COLLECT. Then the process must be instrumented so that the real-time data corresponding to normal or off-normal behaviours can be collected.
A – ANALYZE. Next the real-time data must be analyzed to determine the baseline signature of a normal or acceptable process condition, i.e. a process condition capable of making a good part. This will normally take more than one instance of a good process, and typically 10-12 are required. After that, the analysis will establish a fingerprint for each occurrence of the manufacturing process that will be compared to the baseline to determine if the specific manufacturing run is normal or off-normal.
R – REACT. If an off-normal condition is in fact detected, then some action must be undertaken to ensure that defective parts are not passed further downstream. This action could either be a "stop-work" to allow resolution of the issue, or in the cases of an automated process, the process can initiate corrections on its own to get back to normal process conditions.
E – EVOLVE. The software will be able to take advantage of process learning as additional fault conditions are identified. Therefore IPQA is an evolving quality assurance technique that becomes better at identifying AND classifying faults as time goes on.
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