Product lifecycle informatics is the product data and information management process by
which a product is evolved from conceptualization, detail design, manufacture,
distribution, maintenance, and recycling. The lifecycle of a product is
from cradle to grave, from atoms to systems. Product data can come from different sources, including original digital models during design, scanned point clouds from reverse engineering, environmental and operational data from sensors onboard after deployment, and maintenance records during its usage. Modern product design and manufacturing paradigm enables multidisciplinary stakeholders to participate in decision making and share product information across enterprise boundaries in a cyber-enabled distributed environment.
Many research issues need to be resolved, such as:
Distributed design information model and interoperability: Current data models for heterogeneous design and manufacturing systems are tool-specific. Although some neutral file formats such as IGES, STEP, OpenJT, etc. have developed, important design intent such as features and modeling histories are lost during translation. Similarly, for different machines in additive manufacturing, STL and AMF as neutral file formats do not capture manufacturing engineers' intent and process information is lost, which causes inconsistency and verifiability issues of AM products.
Data analytics: With the latest sensing and embedded computing techniques, information about products through their lifecycles becomes increasely available to stakeholders. For instance, how products are fabricated, deployed, and used can provide valuable input to product designers for informed decision making. Manufacturing equipment's health status can guide suppliers to provide just-in-time shipment of part replacement for preventive maintenance.
Data compression: The amount of data collected by sensors is exponentionally growing, which makes communication and data processing the bottleneck. Compressive or compressed sensing is a new approach to obtain information from reduced data sets. Domain specific compression can further improve compression ratios.
Security and intellectual property protection: Collaborative design requires design data to
be shared by different parties. Data security is essential to build trustworthy distributed