Addithive

Tag: topology optimization

  • Digital Thread and Traceability in Additive Manufacturing

    Digital Thread and Traceability in Additive Manufacturing

    The digital thread in additive manufacturing is the controlled connection between product definition, manufacturing configuration, process data, inspection evidence and final part identity. Its purpose is not to collect every available signal. It is to preserve enough trusted information to reproduce, investigate and release the part.

    A useful digital thread answers four questions: What was authorized? What was actually used? What happened during production? What evidence supports acceptance?

    Why additive manufacturing needs a strong digital thread

    AM relies on a dense chain of digital and physical transformations. A CAD model is converted into manufacturing geometry, oriented, supported, nested, sliced and translated into machine instructions. Feedstock, machine condition, software, parameters, heat treatment, machining and inspection then determine the final hardware.

    If these relationships are not controlled, two parts with the same nominal drawing can be produced through materially different routes. That undermines repeatability, root-cause analysis, supplier transfer and qualification.

    Digital thread, digital twin and data package are not the same

    ConceptPractical meaning in AMPrimary purpose
    Digital threadTraceable information flow across design, manufacturing, inspection and serviceConfiguration continuity and provenance
    Digital twinA digital representation connected to a physical asset or process through data exchangeMonitoring, prediction, simulation or decision support
    AM data packageA defined set of information associated with a specific part or manufacturing workflowCommunication, acceptance, retention and auditability
    Machine logEquipment-generated operational and event recordsProcess traceability and troubleshooting
    Build fileManufacturing representation used to execute a specific buildMachine execution; not by itself a complete production record

    A company can have a digital thread without a sophisticated real-time digital twin. It can also have large volumes of machine data without having a trustworthy digital thread.

    The core objects that must be connected

    1. Product definition

    • Authoritative CAD model and drawing
    • Part number, revision and effectivity
    • Material and process specifications
    • Critical characteristics and acceptance criteria
    • Approved deviations, concessions and repairs

    2. Manufacturing definition

    • Orientation, support and nesting configuration
    • Manufacturing geometry and file format
    • Machine model and machine identity
    • Software, slicer and parameter-set versions
    • Build plate, substrate or fixture definition
    • Coupon and witness-specimen layout

    3. Material genealogy

    • Powder, wire, resin or bound-feedstock lot
    • Supplier certificate and incoming inspection
    • Storage, handling and environmental history
    • Powder reuse, blending, sieving and refresh history
    • Contamination or disposition records

    4. Process execution

    • Build start and completion information
    • Machine state, alarms and operator interventions
    • Atmosphere, temperature and other controlled conditions
    • Layer images, recoater events or melt-pool data where required
    • Maintenance and calibration status
    • Nonconformance and interruption records

    5. Post-processing history

    • Stress relief, heat treatment and HIP cycles
    • Debinding and sintering records
    • Build-plate and support-removal route
    • Machining programs, fixtures and completed operations
    • Surface finishing, coating and cleaning
    • Subcontractor certificates and process records

    6. Inspection and acceptance

    • Dimensional results and inspection-program revision
    • NDT equipment, technique and operator identity
    • Material test results and specimen location
    • Cleanliness, flow or functional-test results
    • Disposition and final release authorization
    • Link between the physical serial number and digital record

    The authoritative-source problem

    AM workflows often create multiple copies of geometry and parameters across PLM, CAD, build-preparation software, local workstations and machine controllers. A valid digital thread must identify which object is authoritative and which copies are derived.

    Useful controls include:

    • Unique identifiers and revision status
    • Checksum or cryptographic hash for critical files
    • Role-based approval and electronic signature
    • Controlled transfer from engineering to manufacturing
    • Read-only released records
    • Documented regeneration rules when a derived file changes

    Saving a file with “final” in its name is not configuration control.

    Machine data: collect what supports a decision

    Modern AM machines can generate large amounts of data. Storage volume is not the same as traceability value. Before collecting a signal, define:

    1. Which failure mode or quality characteristic it represents
    2. How the sensor is calibrated and time-synchronized
    3. How the data is linked to machine, build, layer and part location
    4. Which limits, alerts or analysis methods are approved
    5. How long the data must be retained
    6. Who can modify, interpret and disposition the record

    ISO/ASTM 52953:2025 establishes minimum requirements for registering multimodal monitoring and quality-control data. The emerging direction is toward structured, referenceable data rather than isolated screenshots and proprietary reports.

    The 2026 data-package standard

    ISO/ASTM 52951:2026 provides a framework for developing and using AM part data packages from design through acceptance. It is based primarily on PBF-LB/M, but its workflow principles can inform other AM processes.

    The practical significance is that organizations can define data-package depth by part class, customer need and regulatory risk. A prototype bracket should not require the same evidence as a flight-critical component, but both should have a clear minimum record.

    Cybersecurity and intellectual property

    The digital thread can expose design IP, process know-how and evidence used for certification. Security should be designed into the workflow rather than added after deployment.

    • Apply least-privilege access and strong identity management.
    • Separate development, production and supplier environments where appropriate.
    • Encrypt sensitive data in transit and at rest.
    • Record file transfers, approvals and administrative actions.
    • Control removable media and machine-network interfaces.
    • Validate backup, recovery and long-term readability.
    • Define which supplier data must be shared and which can remain protected.

    Blockchain is not a prerequisite for trustworthy AM traceability. In most factories, disciplined configuration management, access control, audit logs and signed records create more immediate value.

    Interoperability remains a bottleneck

    AM data can pass through CAD, PLM, MES, QMS, build-preparation tools, machine software, laboratory systems and supplier portals. Proprietary formats and inconsistent naming make it difficult to connect the record.

    Organizations should define stable identifiers, metadata and interfaces even when full automation is not possible. A controlled manual link is better than an invisible automated transformation that cannot be audited.

    A practical minimum viable digital thread

    1. Give every part, build and material lot a unique identifier.
    2. Control the released product definition and manufacturing revision.
    3. Link orientation, support, nesting, machine and parameter configuration.
    4. Record feedstock genealogy and equipment readiness.
    5. Capture alarms, deviations and operator interventions.
    6. Link post-processing records to the serial number or production batch.
    7. Store inspection results with technique and acceptance criteria.
    8. Protect approved records from uncontrolled modification.
    9. Define retention, backup and migration rules.
    10. Test whether the organization can reconstruct the complete history of a released part.

    Common failure modes

    • Machine files stored only on a local workstation
    • Parameters renamed without version or approval history
    • Powder lots blended without genealogy
    • Inspection reports disconnected from the physical part identity
    • Large monitoring-data archives with no validated interpretation
    • Supplier records delivered as unsearchable PDFs with ambiguous references
    • Software updates applied without configuration review
    • Digital records that cannot be opened years later

    Conclusion

    The AM digital thread is a quality and configuration system, not a marketing layer. Its value comes from connecting authorized design, actual manufacturing conditions and acceptance evidence to a specific physical part. Start with traceability and decision needs, then add automation, analytics and digital-twin capability where they solve a defined problem.

    Related Addithive resources: Industrial AM Workflow · Aerospace AM Qualification Guide

    References and further reading

  • Unlocking the Potential of Metal 3D Printing: Challenges and Opportunities in the Mobility Industry

    Unlocking the Potential of Metal 3D Printing: Challenges and Opportunities in the Mobility Industry

    Metal additive manufacturing (MAM), also known as “metal 3D printing,” has been around for over 30 years. In the past decade, however, there has been a surge of interest in the technology as it moves from prototype to low-rate and high-rate production for increasingly critical applications for more industries. With this shift comes the challenge of determining design properties for the first time in many years. Not only is it necessary to determine basic material properties, but it is also necessary to accommodate new geometries and design concepts as well. While some of the methods and approaches are common to other product forms, others are unique to MAM.

    MAM is a process that uses a laser or electron beam to melt metal powder and create complex, three-dimensional parts directly from a computer-aided design (CAD) model. The process offers several advantages over traditional manufacturing methods, including the ability to create complex geometries with less waste, shorter lead times, and lower tooling costs. However, as the technology has matured and gained wider acceptance, the need to determine design properties has become increasingly important.

    “Additive manufacturing” by oakridgelabnews is licensed under CC BY 2.0.

    One of the main challenges in determining design properties for MAM is the lack of standardized testing methods. While traditional manufacturing methods such as casting, forging, and machining have established testing methods, MAM is still in the process of developing these methods. The lack of standards can make it difficult to compare results between different MAM processes and materials.

    Another challenge is the need to understand the microstructure of MAM parts. The microstructure refers to the arrangement of the atoms in the metal and can have a significant impact on the properties of the part. The microstructure of MAM parts is often different from that of parts made using traditional methods, which can make it difficult to predict the properties of the part.

    To overcome these challenges, product teams must take a methodical approach to determining design properties for MAM parts. This involves understanding the process parameters, material properties, and part geometry, and using this information to develop testing methods that can accurately predict the performance of the part.

    One approach to understanding the process parameters is to use a design of experiments (DOE) approach. DOE involves systematically varying the process parameters and measuring the resulting properties of the part. This can help identify the optimal process parameters for a given material and part geometry.

    Another approach is to develop a process map for the MAM process. A process map is a graphical representation of the process parameters and their impact on the part properties. This can help identify the key process parameters that have the most significant impact on the part properties.

    Understanding the material properties is also critical in determining design properties for MAM parts. This involves characterizing the mechanical, thermal, and chemical properties of the material. Traditional testing methods such as tensile testing, hardness testing, and impact testing can be used to determine these properties.

    In addition to the traditional testing methods, there are also some unique testing methods that are specific to MAM. One such method is the use of computed tomography (CT) scanning to analyze the internal structure of the part. This can help identify defects such as voids, cracks, and inclusions that can affect the part properties.

    Another unique testing method is the use of digital image correlation (DIC) to analyze the deformation of the part under load. DIC involves analyzing images of the part before and after loading to determine the displacement and strain of the part. This can help identify areas of the part that are experiencing high stress and may be prone to failure.

    Once the process parameters and material properties have been characterized, the next step is to determine the part geometry. This involves analyzing the CAD model and identifying areas of the part that may be prone to failure. Finite element analysis (FEA) is a common tool used to simulate the behavior of the part under different loads and boundary conditions. This can help identify areas of the part that are experiencing high stress and may be prone to failure.

    FEA can also be used to optimize the part geometry for the MAM process. This involves modifying the CAD model to minimize distortion, reduce residual stress, and improve the part properties. One approach to this is topology optimization, which involves using algorithms to generate an optimal shape for the part based on a set of design constraints.

    Once the testing methods have been developed and the part geometry has been optimized, the next step is to validate the design properties. This involves testing the part under real-world conditions to confirm that it meets the design requirements. This can include testing the part under different loads, temperatures, and environmental conditions.

    One example of MAM in the mobility industry is the use of the technology to produce lightweight, complex parts for aerospace applications. MAM has been used to produce parts such as brackets, hinges, and latches that are up to 60% lighter than their traditionally manufactured counterparts. These parts offer significant weight savings, which can lead to improved fuel efficiency and reduced emissions.

    To ensure that these parts meet the stringent safety requirements of the aerospace industry, product teams have had to develop new testing methods and standards. For example, the Federal Aviation Administration (FAA) has developed a set of guidelines for qualifying MAM parts for use in aircraft. These guidelines include requirements for material properties, process parameters, and testing methods.

    Looking to the future, there are several areas where further research is needed to fully realize the potential of MAM in the mobility industry. One area is the development of new materials that are specifically designed for the MAM process. These materials could offer improved properties over traditional materials and enable the production of parts with even greater complexity.

    Another area is the development of in-process monitoring and control systems for the MAM process. These systems could help identify defects and deviations in real-time, allowing for immediate corrective action. This could help improve the quality and consistency of MAM parts and reduce the need for post-processing.

    In conclusion, determining design properties for metal additive manufacturing in the mobility industry is a complex and challenging task. However, with the right approach and testing methods, it is possible to develop parts that meet the stringent requirements of the industry. As MAM continues to mature and gain wider acceptance, it will become increasingly important for product teams to understand the unique challenges and opportunities presented by this technology. By doing so, they can unlock the full potential of MAM to produce lightweight, complex parts that offer significant benefits in terms of cost, lead time, and performance.