Hybrid Manufacturing Process Planning With AI State-Space Search
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Solution Overview
Problem
Generating process plans for hybrid manufacturing systems that combine additive and subtractive processes is challenging due to the complexity of arbitrary multimodal sequences, which requires exploring a vast state transition space and is computationally expensive, and ensures manufacturability without prior process planning.
Innovation Solution
A systematic approach using advanced AI planning techniques to explore viable process plans for hybrid manufacturing, decoupling geometric and spatial reasoning from logical and combinatorial search, allowing for flexible adjustment of geometric decisions and separation of manufacturability analysis from manufacturing planning, and utilizing canonical intersection terms to identify atomic building blocks for efficient process planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If arbitrary multimodal sequences of AM and SM processes are used in hybrid manufacturing, then manufacturing efficiency and flexibility are improved, but computational complexity and planning time increase significantly
Solution Approach 1:
The patent segments the manufacturing process into distinct modalities (AM and SM) and represents them as separate states in a state transition space. This segmentation allows the complex hybrid manufacturing problem to be broken down into manageable discrete states and transitions, making the planning computationally tractable while still allowing arbitrary sequences of operations.
Solution Approach 2:
The patent implements a dynamic state transition space where the manufacturing process can flexibly transition between AM and SM states based on optimal planning. The system dynamically evaluates different sequences of operations and selects the most efficient path through the state space, enabling arbitrary multimodal sequences while managing computational complexity through systematic exploration.
2Adaptability or versatility
If arbitrary multimodal sequences of AM and SM processes are explored, then manufacturing flexibility is improved, but planning time and computational cost increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining the state transition space and manufacturing capabilities before actual process planning. By establishing the complete state space and transition rules in advance, the system enables rapid evaluation of different sequences during planning without recomputing fundamental constraints, thus reducing planning time while maintaining flexibility.
Solution Approach 2:
The patent uses digital representations and models (digital twins) of the manufacturing process and state transitions. By working with computational models rather than physical trial-and-error, the system can evaluate multiple hypothetical sequences rapidly, maintaining manufacturing flexibility while minimizing actual planning and execution time.
3Reliability
If manufacturability is ensured through comprehensive process planning, then production reliability is improved, but computational expense increases
Solution Approach 1:
The patent implements feedback mechanisms by evaluating whether each state transition maintains manufacturability constraints. The system continuously checks if proposed sequences satisfy production requirements and adjusts planning accordingly. This feedback-driven approach ensures manufacturability reliability while avoiding exhaustive exploration of infeasible sequences, reducing computational expense.
Solution Approach 2:
The patent applies partial action by exploring only the necessary portion of the state transition space required to ensure manufacturability. Rather than exhaustively evaluating all possible sequences, the system identifies and evaluates sufficient conditions for manufacturability, ensuring reliability without the full computational burden of complete enumeration.
Data Source
AI summary
A systematic approach to constructing process plans for hybrid manufacturing is provided. The process plans include arbitrary combinations of AM and SM processes. Unlike the suboptimal conventional practice, the sequence of AM and SM modalities is not fixed beforehand. Rather, all potentially viable process plans to fabricate a desired target part from arbitrary alternating sequences of pre-defined AM and SM modalities are explored in a systematic fashion. Once the state space of all process plans has been enumerated in terms of a partially ordered set of states, advanced artificial intelligence (AI) planning techniques are utilized to rapidly explore the state space, eliminate invalid process plans, for instance, process plans that make no physical sense, and optimize among the valid process plans using a cost function, for instance, manufacturing time and material or process costs.