Auxiliary Advising for Context-Aware Manufacturing Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturing control systems lack outside-in information and context-awareness, making it difficult to assess and adapt manufacturing processes effectively, especially in detecting variables beyond sensor detection ranges, which impacts energy use and yield.
Innovation Solution
An auxiliary advising system that uses sensors to detect states of machine operations and applies causality models with labeled datasets to provide context adaptation, interaction context detection, and anomaly prevention outputs, enabling adaptive learning and self-labeling of sensor data without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manufacturing control systems use traditional sensors and monitoring methods, then system simplicity is maintained, but context-awareness and detection of outside-in information are insufficient
Solution Approach 1:
An auxiliary advisor system is introduced as an intermediary component that bridges traditional manufacturing control systems and outside-in information sources. The advisor receives data from multiple sensors including cameras, microphones, and environmental sensors, processes this information through machine learning models, and provides contextual insights to the control system without requiring direct integration of complex detection capabilities into the core control architecture.
Solution Approach 2:
The auxiliary advisor system performs self-labeling of sensor data through automated machine learning models that independently identify and categorize patterns in sensor data without requiring manual annotation. The system adapts to new information sources and contexts autonomously, reducing the need for human intervention in system configuration and maintenance.
2Measurement precision
If manufacturing systems implement comprehensive sensor monitoring, then detection accuracy improves, but energy consumption and system complexity increase
Solution Approach 1:
The system implements selective monitoring where the auxiliary advisor activates specific sensor groups based on operational context and detected anomalies rather than continuously operating all sensors at full capacity. Machine learning models prioritize processing of critical data streams while reducing computational overhead for routine monitoring, achieving high detection accuracy for critical events while minimizing overall energy consumption.
3Measurement precision
If manual data labeling is used for training detection models, then model accuracy improves, but productivity and time efficiency deteriorate
Solution Approach 1:
The auxiliary advisor system implements self-labeling capabilities where machine learning models automatically annotate and categorize sensor data without human intervention. The system uses unsupervised and semi-supervised learning techniques to identify patterns, classify events, and generate training labels autonomously. This automated approach maintains model accuracy while eliminating the time-consuming manual data preparation process, significantly improving productivity in model training and deployment.
4Manufacturing precision
If manufacturing processes are rigidly controlled according to standard procedures, then manufacturing precision is maintained, but adaptability to abnormal situations decreases
Solution Approach 1:
The auxiliary advisor system continuously monitors manufacturing processes and provides real-time feedback when deviations from standard procedures are detected. Machine learning models analyze sensor data to identify abnormal patterns and generate adaptive recommendations that guide operators or automated systems in adjusting processes while maintaining quality standards. This feedback loop enables the system to maintain manufacturing precision during normal operations while adapting to abnormal situations through data-driven insights.
Solution Approach 2:
The control system transitions from rigid static procedures to dynamic adaptive control where process parameters can be adjusted in real-time based on environmental conditions and operational context. The auxiliary advisor enables flexible modification of standard operating procedures while maintaining quality constraints, allowing the manufacturing process to adapt dynamically to changing conditions without sacrificing precision.
Data Source
AI summary
Process and device configurations are provided operating and designing an auxiliary advisory system for manufacturing control systems and machine operations. Systems and processes are configured to utilize adaptive learning and context awareness to provide auxiliary advising and to assess a manufacturing environment and infrastructure. Processes are provided to receive and process data for a machine operation and assess interactions relative to one or more of a worker, machine, machine component and material. The processes and systems may output advice to assist with existing manufacturing systems and to adapt to changes in manufacturing conditions and environmental constraints. Advisory system functions may also include anomaly detection and recognition of worker gestures to control operation of a machine and a manufacturing process. Processes are provided to integrate causal relationships of objects by self-labeling data into causality models for assessing machine operation.