3D Printer Part Replacement via Predictive Model Data
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Solution Overview
Problem
3D printers face delays in replacing failed components due to time-consuming part ordering processes, leading to potential downtime and inefficient stock management.
Innovation Solution
An information processing apparatus with an identifying unit, acquiring unit, and instructing unit that identifies and acquires model data for a failed part, allowing the 3D printer to form a replacement part using the acquired data, even if no other printer is available nearby.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of repair
If parts are ordered from a supplier when a failure occurs, then the part can be replaced, but transporting the ordered part takes time causing delays
Solution Approach 1:
The system performs preliminary actions by identifying potential failure parts before they actually fail, using predictive analytics and sensor data. When a part is identified as potentially failing, the system proactively orders replacement parts in advance, so that when the actual failure occurs, the parts are already available for immediate replacement, eliminating transport delays.
Solution Approach 2:
The system enables self-service by automatically monitoring device health, identifying failing parts, ordering replacements, and coordinating delivery without human intervention. The device essentially services itself by triggering the entire replacement workflow, from detection to part arrival, reducing dependency on external support and minimizing downtime.
2Reliability
If all types of parts are procured in advance and managed in stock, then failure can be prepared against, but space and costs for custody are wasted
Solution Approach 1:
Instead of holding all parts in stock beforehand, the system takes preliminary action by using predictive analytics to identify which specific parts are likely to fail soon. Only these identified parts are ordered in advance, maintaining reliability by having necessary parts ready while avoiding the waste of storing unnecessary parts that would consume space and resources.
Solution Approach 2:
The system changes the parameter of inventory management from static (holding all parts constantly in stock) to dynamic (ordering parts based on real-time predictive needs). This allows the system to maintain high reliability by having parts ready when needed, while minimizing stock space by only holding parts that are actually predicted to be required, rather than maintaining a comprehensive inventory of all possible parts.
Data Source
AI summary
An information processing apparatus comprising an identifying unit configured to identify a part being a forming target in the forming apparatus on the basis of information acquired from a forming apparatus for forming a three-dimensional object, an acquiring unit configured to acquire model data describing a shape of the identified part to form the part in the forming apparatus, and an instructing unit configured to instruct the forming apparatus to form the identified part by using the acquired model data.