Predictive Assembly Workflow Orchestration With Service Pods
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Solution Overview
Problem
Conventional predictive assembly processes lack a cohesive workflow or process management system, leading to inefficiencies and challenges in managing and executing custom scripts for each process independently.
Innovation Solution
A workflow engine that autonomously builds and manages workflows for predictive assembly by determining which service pods to invoke, their sequence of execution, and computing resources needed, allowing for dynamic and parallel processing of assembly predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom scripts are used for each predictive assembly process independently, then flexibility and customization are improved, but system complexity and lack of cohesive workflow management worsen
Solution Approach 1:
The system segments the predictive assembly process into independent service pods that can be selectively invoked. Each service pod represents a discrete functional unit that can be customized and configured independently, allowing flexibility while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The workflow engine provides a universal platform that can manage multiple different predictive assembly workflows through a common interface and architecture. This universal framework handles workflow orchestration, resource allocation, and process monitoring across diverse custom scripts, reducing overall system complexity while preserving customization capabilities.
2Reliability
If multiple custom scripts are executed independently, then process specificity is improved, but lack of centralized process management and monitoring worsens
Solution Approach 1:
The workflow engine acts as an intermediary that coordinates between multiple custom scripts and service pods. It manages process flow, handles dependencies, and provides centralized monitoring without requiring direct complex interactions between individual scripts, thereby improving reliability while managing coordination complexity.
Solution Approach 2:
The system dynamically determines the sequence of execution for service pods based on workflow requirements and resource availability. This dynamic orchestration allows the system to adapt to different process needs while maintaining centralized management, improving reliability without imposing rigid complex structures.
3Productivity
If computing resources are allocated statically, then resource stability is improved, but inability to shift resources based on priority worsens productivity
Solution Approach 1:
The system dynamically allocates and shifts computing resources based on workflow priority and current system state. High-priority predictive assembly requests can receive additional computing resources automatically, improving productivity while the automated resource management reduces the complexity of manual allocation decisions.
Solution Approach 2:
The workflow engine implements feedback mechanisms that monitor resource usage and workflow progress, automatically adjusting resource allocation based on observed performance and priority levels. This feedback-driven resource management improves productivity while the automated nature of the adjustments reduces operational complexity.
Data Source
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AI summary
The present disclosure provides a workflow engine for predictive assembly. In some aspects, the workflow engine is executed by one or more processors so that the one or more processors are configured to: receive a predictive assembly request for an assembly prediction to be generated for an assembly of interest; receive measurement data associated with the assembly of interest for which the assembly prediction is desired; autonomously build a workflow specific to the assembly of interest for generating the assembly prediction, wherein building the workflow includes determining i) which service pods of a plurality of service pods to invoke for generating the assembly prediction, and ii) a sequence of execution of the invoked service pods; autonomously generate the assembly prediction by executing the workflow using the invoked service pods and the measurement data; and output the assembly prediction.