Customizable Data Parsers for Material Testing Workflow Automation
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Solution Overview
Problem
Conventional material testing systems require manual entry of multiple pieces of information for setting up, executing, and analyzing test methods, leading to data entry errors and inefficiencies, especially when dealing with numerous data points.
Innovation Solution
The implementation of customizable data parsers and workflow field mappings in material testing systems, which allow for automatic data importation and separation into smaller portions, enabling efficient population of input fields and parameters, and allowing different parsers for various workflows and fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual entry of multiple pieces of information is used for setting up test methods, then data entry flexibility is maintained, but data entry errors increase and efficiency decreases
Solution Approach 1:
The system enables self-service data population by automatically parsing imported data and mapping it to workflow fields. The data parser autonomously processes raw data, identifies relevant parameters, and populates input fields without requiring manual intervention, thereby improving both efficiency and accuracy.
Solution Approach 2:
A data parser is introduced as an intermediary component between data importation and workflow execution. This mediator automatically processes raw data, extracts relevant parameters, and maps them to appropriate workflow fields, eliminating the need for manual data entry while ensuring data accuracy through systematic parsing rules.
2Productivity
If automatic data importation is implemented, then data handling efficiency improves, but data processing complexity increases
Solution Approach 1:
The data processing system is segmented into distinct functional modules: data importation, data parsing, field mapping, and workflow execution. Each module performs a specific function, making the overall complex process manageable and maintainable while achieving high automation efficiency.
Solution Approach 2:
The data parser is designed with dynamic configuration capabilities, allowing parsing rules and field mappings to be adjusted based on different data formats and workflow requirements. This dynamic adaptability enables the system to handle diverse data types without requiring complete system redesign.
3Adaptability or versatility
If a single data parser is used for all workflows, then system simplicity is maintained, but adaptability to different workflow types decreases
Solution Approach 1:
The data parser is designed as a universal component capable of handling multiple workflow types and data formats. Through configurable parsing rules and flexible field mapping, a single parser instance can adapt to different test methods, material types, and data structures without requiring separate parsers for each workflow.
Solution Approach 2:
The parser's behavior is controlled through configurable parameters and rules that can be modified to match different workflow requirements. By changing parsing parameters, field mappings, and data format specifications, the same parser infrastructure can efficiently handle diverse testing scenarios across different material types and test methods.
Data Source
AI summary
Described herein are examples of material testing systems that allow users to select one or more customizable data parsers (from amongst several customizable data parsers) when configuring a workflow for setup, execution, and/or analysis of a test method on a material testing machine. Thereafter, when a piece of data is imported by an importation device during operation of the workflow, the selected data parser(s) can separate (or parse) out several smaller data portions from the imported data. The several smaller data portions can be mapped to different input fields and/or used to set several different (e.g., input field associated) parameters of the workflow at the same time. In this way, a material testing workflow can be made far more efficient than in conventional systems where imported data can only be used to set a single workflow parameter (and/or fill a single input field).