SLM Solutions Presents New Software Partner Ecosystem
The software partner ecosystem is designed to complement its in-house software portfolio to deliver a comprehensive End-to-End additive manufacturing software solution set.
November 22, 2022
Metal additive manufacturing company SLM Solutions presents its new software partner ecosystem. Comprised of the key software players in the additive manufacturing market, it is designed to complement its in-house software portfolio to deliver a comprehensive End-to-End additive manufacturing software solution set. It is tailored to the customer’s individual requirements in the key application spaces: build preparation, AM digitalization, connectivity and quality assurance.
“This new software partner ecosystem—based on SLM Solutions open architecture—offers our customers the broadest and most compelling joint software portfolio to industrialize and take their AM operations to the next level, while overcoming key business challenges along the entire process chain,” Nicolas Lemaire, software product manager of SLM Solutions explains.
“Thanks to our new partner ecosystem, customers can now choose the most appropriate build preparation software on the market that is best suited to the specifics of their application, and use it to design build job on their SLM machine,” Lemaire adds.
Digitalization of Additive Manufacturing
SLM Solutions used its open architecture to partner with key Manufacturing Execution System (MES) and Internet of Things (IoT) solution providers in additive manufacturing space. SLM Solutions customers will then be able to implement such solutions into their SLM process to streamline and enhance production processes through automation, integration and full visibility on the additive manufacturing process.
Not only are SLM Solutions and Materialise partners in build preparation, but the two companies are now also collaborating on connectivity and secure digitalization as part of SLM Solutions' new Software Partner Ecosystem.
“Materialise is proud to take the next step in the partnership with SLM. Welcoming SLM to the Materialise CO-AM ecosystem will empower innovators to take their AM operations to the next level, protecting build files, enforcing production rules, and enabling end-to-end data traceability,” shares Vishal Singh, senior director of CO-AM. “Together, Materialise and SLM will deliver scalable and secure workflows for serial production and distributed manufacturing to our current and future customer base.”
Another new partner in this field is Oqton, a software provider helping manufacturers automate production. SLM Solutions and Oqton enter into a strategic partnership to increase productivity and give complete traceability to Industrial Additive Manufacturing customers, the companies say.
“The partnership between Oqton and SLM Solutions will allow our mutual customers to access and optimize AM workflows in one place, bringing an end to fragmented workflows,” Roy Sterenthal, VP Industrial Additive, Oqton explains. “Customers will be well-equipped to meet the growing need for highly complex parts and serial production in Industrial Additive Manufacturing. Oqton’s excellent solutions for SLM machines, including our Manufacturing OS for order management and planning, optimized AI/ML and simulation-based build preparation, will enable customers to streamline successful and certified workflows and ensure traceability.”
SLM Solutions offers its in-house, ready-to-use quality assurance software aimed to optimize process qualification and part validation activities: SLM.Quality. The software enables easy evaluation of the build job process data through standard dashboards and full documentation of the build process thanks to a reporting tool.
SLM Solutions also partners with quality assurance software companies to enable customers to perform extended quality control through more
powerful dashboards, combining different evaluation technologies like industrial X-ray Computed Tomography with 3D visualization and automatic part and process defect detection through machine learning and artificial intelligence technologies.
Sources: Press materials received from the company and additional information gleaned from the company’s website.
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