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Fast forward more than a century later, and manufacturing is once again on the cusp of radical change to what some people call the fourth industrial revolution: new, innovative methods for manufacturing products and their processes. Cutting-edge technologies such as additive manufacturing<\/a> (AM)<\/em>, or 3D printing<\/a>, make it possible to print complex objects via a CAD simulation model, creating layer-by-layer using various types of materials with high precision and repeatable quality. Simulation <\/a>and 3D modeling allow for advanced complexity of design and quality, ultimately resulting in fewer distortions and errors.<\/p>\n\n\n\n While there are numerous examples of additive manufacturing in its prototype stage, the goal is to propel it into the mainstream. Though it may not replace all conventional manufacturing methods, it will serve as an invaluable add-on for adopting innovative technology because it allows users to re-imagine products, reinvent manufacturing and even rethink business models. In order to get there, the industry still has to overcome some challenges \u2013 after all, 3D printing is an extremely complicated process. <\/p>\n\n\n\n Just think about\nhow complex this concept is: Driving a laser beam no bigger than the tip of a\npen, at a speed of six feet per second, melting metal powder at more than 3000\ndegrees Fahrenheit over a course of about 25 miles, to create a product about\n18 inches high with a 10 inch circumference, and never once deviating from the\nplanned path. This is the additive manufacturing\nchallenge, and its complexities continue throughout several stages.<\/p>\n\n\n\n First, find the\nideal materials. Secondly, create a perfect design. Lastly, qualify the process\nto be able to produce that design. Then production needs to execute flawlessly,\nand when the part comes out of the factory, it needs to be certified. The full\nAM challenge covers the entire value chain: from product design to the\nproduction process all the way to its performance, including:<\/p>\n\n\n\n All of these assist in achieving the production of quality parts, on time and at scale.<\/p>\n\n\n\n Let us focus on some of the most\nsignificant challenges.<\/p>\n\n\n\n 1) How can we tap into the huge\nbut costly potential of materials?<\/strong><\/p>\n\n\n\n Audi recently published a paper about\na metal additive manufacturing part\nthat unexpectedly failed in an\nunpredicted location on the part. So, how can additive manufacturing help to\ntap into the complexities of the part, process and materials to avoid these\nfailures? <\/p>\n\n\n\n It can take up to five iterations\nto get a print correct. How can this high failure rate be avoided? <\/p>\n\n\n\n Siemens\nis working on two solutions to assist designers and engineers:<\/p>\n\n\n\n Imagine using a smartphone app to scan your\nfeet to create a pair of running shoes, creating a lattice structure tailored\nto your size and running style. Additive technology does this and much more,\nautomating the discovery of the optimal lattice per the target performance of\nthe part. <\/p>\n\n\n\n Siemens\nperformed design experiments\non 39 lattice structures to characterize the properties of each \n So, as the Audi\nstory conveyed, part failures can happen in unpredictable places. AM durability solver software rectifies this problem\nutilizing:<\/p>\n\n\n\n 2)\nHow can we avoid the high failure rate?<\/strong><\/p>\n\n\n\n Compared to conventional\nmanufacturing, it can take three\ntimes more iterations to validate an\nadditive part design that will print repeatedly with quality. This\nmeans process inefficiencies are magnified\nthree times. So, how can the process deliver quality parts efficiently when data is thrown over the wall? <\/p>\n\n\n\n Digitally defining a process for AM parts requires two technologies to solve this problem. Both rely on the power\nof simulation; one addresses macro-level defects in AM builds, the other\naddresses meso-level defects. \n<\/p>\n\n\n\n The macro-level<\/em>\n3D printing problems are overheating, residual\nstress, distortion, warping and re- coater collisions.\nThese issues are addressed\nusing an AM build simulation CAE-based solution, which predicts the\nthermal-mechanical distortion of the\nparts in the 3D print process, altering the original CAD to compensate for the problems it uncovers. This solution provides customers with significant\nimprovement in their success rates to achieve first-time-right <\/em>3D printing. <\/p>\n\n\n\n However, this solution\nonly analyzes the 3D print process from a Macro-level<\/em>. <\/p>\n\n\n\n Path analysis\nis a physics-based technology that\npredicts meso-level<\/em> defects due to overheating along the actual tool\npath. Then it re-sequences the\nvectors in the toolpath job file to correct the predicted 3) How do we\ndeliver quality parts on time and at scale?<\/strong><\/p>\n\n\n\n The\nmost significant challenge that companies face pertaining to design is opportunity<\/em>. Using a variety of\nmaterials in an infinite number of ways develops new dimensions to optimizing\nproduct performance. So, how do you work with this dynamic of choice? <\/p>\n\n\n\n Sintavia, one of the leading suppliers of AM\nparts to the aerospace industry, has\ndealt with the issue of how a disconnected digital thread impacts the ability\nto deliver AM parts on time and at scale. Siemens is working\nwith them on several projects to achieve an integrated end-to-end\nprocess for industrializing additive\nmanufacturing. These projects include streamlining the print process through simulation-driven design<\/em> and optimizing shop floor operations<\/em>.<\/p>\n\n\n\n Simulation-driven design can have a\nsignificant impact on the quality and efficiency of the AM process.\nSiemens\u2019solutions are tightly embedded into the simulation process, driving\ndesign without over-the-wall iterations.<\/p>\n\n\n\n This design method allows for the manufacturing engineer to be\nconcurrently working on setting up the build job in parallel to the designer\nmodifying the design, instantaneously updating the simulation model and rerunning\nthe analysis. Then the manufacturing engineer can update the build job with the\nnew part design all in one system.<\/p>\n\n\n\n While simulation-driven design for additive manufacturing (dfAM)\nstreamlines the front of the process, factory communications connects the\ndigital thread to the shopfloor. We\u2019re working on a solution for automakers to\nsecurely connect all the software in the AM process to the physical world, orchestrating,\nexecuting and monitoring the entire 3D print operation. <\/p>\n\n\n\n Besides the challenges mentioned, above, solutions\nare being developed to address all areas of the AM challenge as part of the technical\nvision to achieve industrialized\nadditive manufacturing. Industrializing AM is only a matter of\ntime. Just like the automobile, this\ntechnology will eventually take off and become a mainstream addition to all\nmanufacturing. However, it will take collaboration, an ecosystem of partners and\nthe right technogies to make this change happen.<\/p>\n\n\n\n \u201dBe ready\nto revise any system, scrap any method, abandon any theory, if the success of\nthe job requires it.\u201d <\/em>\u2013 Henry Ford<\/p>\n\n\n\n This concludes the first\narticle in a blog series highlighting the challenges and steps to\nindustrializing additive manufacturing.<\/em><\/p>\n\n\n\n Related Links:<\/strong> About the author: <\/strong> The goal of industrializing additive manufacturing is moving it past the prototype stage into the mainstream.<\/p>\n","protected":false},"author":61920,"featured_media":2587,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[63,1],"tags":[5,107],"industry":[],"product":[],"coauthors":[],"class_list":["post-2585","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-featured","category-news","tag-additive-manufacturing","tag-industrializing-additive-manufacturing"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2019\/11\/additive-manufacturing.png","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/2585","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/users\/61920"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/comments?post=2585"}],"version-history":[{"count":5,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/2585\/revisions"}],"predecessor-version":[{"id":3510,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/2585\/revisions\/3510"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media\/2587"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media?parent=2585"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/categories?post=2585"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/tags?post=2585"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/industry?post=2585"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/product?post=2585"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/coauthors?post=2585"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Additive manufacturing goal and challenges<\/h3>\n\n\n\n
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<\/figure>\n\n\n\n
\none, followed by predicting the performance for various\ncell sizes and strut diameters. Based on their findings, the software gives power to the designer to suggest the best-automated lattice design to\nachieve a given part\u2019s design performance specifications. Also,\nartificial intelligence (AI) technology assists in automating the discovery of the optimal lattice structure for a design\u2019s requirements. <\/p>\n\n\n\n
\ndefects. <\/p>\n\n\n\nSimulation-driven design<\/h3>\n\n\n\n
Where challenges of today\nmeet solutions for tomorrow<\/h3>\n\n\n\n
Press Release – Siemens expands additive manufacturing portfolio through acquisition of Atlas 3D<\/a>
Industrializing Additive Manufacturing 3D Printing through an Integrated, End-to-end Process<\/a><\/p>\n\n\n\n
Aaron Frankel is vice president of Siemens additive manufacturing software program. He has over 20 years of experience in the PLM software industry and has held various positions in engineering services, product management, and marketing \u2013 all focused on helping manufacturers take advantage of the latest digital design and manufacturing technology. <\/em><\/p>\n","protected":false},"excerpt":{"rendered":"