Digitization is the core feature of additive manufacturing. A key aspect of digital manufacturing lies in data management, because data forms the basis of almost every workflow in additive manufacturing. The latest developments in digital data collection, analysis, machine learning and artificial intelligence, and modeling can quickly accelerate the maturity of additive manufacturing technologies for more controllable production processes.
The ASTM International Standards Committee collaborated with Americas Makes to launch an additive manufacturing data strategy guide, which points out the gaps, challenges, solutions and action plans of additive manufacturing (AM) data.
Source: ASTM
Opportunities and challenges of huge amounts of data
The key to processing additive manufacturing data is data traceability and capture, additive manufacturing data analysis and integration with CAD systems. The challenges of data management in the additive manufacturing value chain mainly lie in design/manufacturing engineering, additive manufacturing production and testing, and certification.
Similarly, the challenges faced by additive manufacturing data include difficulty in defining product characteristics, data capture from non-standard sensors, machine logs, data encryption, granularity of material definitions in design and simulation, and so on. It is very important to promote the compatibility of test suites with additive manufacturing equipment, the integration of machines with design and simulation systems, and the standards of additive manufacturing equipment process data.
According to ASTM InternationalStandards CommitteeConference Director Dr. Mohsen Seifi, a key and huge gap in the development of the additive manufacturing (AM) data ecosystem is how to easily and securely generate, store, analyze, and share critical and important data. This strategic guide attempts to resolve a series of gaps, provides solutions and potential action plans. It is available to industry stakeholders and AM experts who wish to participate in the development of the AM data ecosystem and contribute to standardization.
Another latest achievement of ASTM is additive manufacturing in ASTMTechnical CommitteeA new data subcommittee (F42.08) was established within (F42). The subcommittee will develop and implement value stream mapping to identify the requirements, processes, tools, and opportunities needed to obtain a complete digital thread understanding.
According to the market research of 3D Science Valley, ASTM proposes to create a sound and powerful additive manufacturing data ecosystem in the additive manufacturing data strategy guide to promote3D printingEntered into an exponential growth trend.
The current consensus is that work on the following five themes needs to be promoted as soon as possible:
- Development of a public additive manufacturing data guide
- Develop the smallest feasible data package
- Promote the feasibility of automated data acquisition
- Establish a common data exchange format
- Establish cross-industry data benchmarks and value stream maps
In terms of data acquisition, the current challenge is the data acquisition methods of different test tool providers, and the data quality is different. The same data will show different results after testing according to different test methods. In addition, there will be errors caused by manually entered data.In terms of data management, currently for different3D printingThere is no systematic data standard for the parts manufactured by the equipment to compare the results across equipment. This makes standardization work also full of resistance. Due to the huge amount of data in additive manufacturing, how to manage back-end support data and how to provide an efficient infrastructure to effectively manage and analyze these data is very important.
In terms of data security, how to protect and trace data to understand the interaction of various processing factors that occur during each manufacturing process is necessary. In addition, it is also necessary to consider how to share analysis data while taking into account the protection of data. Sharing data can improve cooperation. However, data protection is also important. Among them, a clear method is needed to promote bilateral development.
In terms of data analysis, it is necessary to establish the correlation between the data and the performance of the parts. If the data cannot predict the real-world processing results, the data will be invalid. Of course, the amount of data generated by additive manufacturing is huge. If the falsification is removed and the authenticity is preserved, how to select data streams with analytical value for the processing results requires a public guide to guide the industry in data optimization. In terms of data application practice, ASTM’s additive manufacturing data working group will determine MVD (Minimum Viable Data Package) to define those data that are necessary to determine the results of additive manufacturing.Generally speaking, it is necessary to establish a digital correlation between data and product results, how to combine powder characteristics, machine conditions and processing parameters, and to understand how these factors affect the mechanical properties of the final part. It is also necessary and empowering.3D printingTechnological development of new materials, new product paths, 3D Science Valley believes that all these need to be completed on the basis of algorithms and artificial intelligence, combined with a large amount of data.
In this regard, Aachen, Germany has taken the lead.European 5G Industrial ParkThe 5G network was launched on May 12, 2020. With an area of nearly 1 square kilometer, 19 5G antennas and a bandwidth of 10 Gbits per second, Aachen began to operate the largest 5G research network in Europe. It can be said that the European 5G industrial park is creating a unique global ecosystem to research and develop industry 4.0 technologies that adapt to 5G.
Near future,3D printingThe PK of data and artificial intelligence will evolve into the PK of data and artificial intelligence. At that time, in a sense, companies with manufacturing data are happy, and companies with artificial intelligence software are happy.
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