Ultra-Precision Machining Error Modeling Across Semi-Finishing
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Solution Overview
Problem
In ultra-precision machining, the accumulation of errors from semi-finishing and finishing processes negatively impacts the final machining precision, as existing methods neglect the surface roughness and shape errors left by semi-finishing, affecting the machining quality of the finished product.
Innovation Solution
An ultra-precision machining method that determines the total material removal amount, performs rough machining, establishes and re-establishes machining error prediction models considering semi-finishing errors, and optimizes process parameters for semi-finishing and finishing to improve precision without reducing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semi-finishing is performed to remove large material amount, then productivity is improved, but manufacturing precision deteriorates due to surface roughness and shape errors left by semi-finishing
Solution Approach 1:
The machining process is divided into three distinct stages: rough machining, semi-finishing, and finishing. Each stage has specific process parameters and objectives. The semi-finishing stage is specifically designed to bridge the gap between rough machining and finishing by controlling both material removal efficiency and surface quality, preventing error accumulation that would otherwise affect the final machining precision.
2Device complexity
If existing machining error prediction model is used, then device complexity is reduced, but manufacturing precision deteriorates because it neglects semi-finishing error influence
Solution Approach 1:
The method performs preliminary prediction of semi-finishing errors before the finishing operation. By establishing an error prediction model that specifically accounts for semi-finishing characteristics and their influence on finishing operations, the system proactively identifies and compensates for potential error sources, enabling more accurate process parameter optimization for the finishing stage.
Solution Approach 2:
The error prediction model creates a feedback mechanism where predicted semi-finishing errors inform the optimization of finishing process parameters. This feedback loop allows the system to adjust finishing parameters based on anticipated errors from semi-finishing, thereby compensating for error accumulation and improving final machining precision without requiring complex real-time measurement systems.
Data Source
AI summary
The present disclosure discloses an ultra-precision machining method, including determining a total material removal amount based on a product to be machined and a blank, and performing rough machining to complete a material removal amount of the rough machining; after the rough machining, establishing an ultra-precision machining error prediction model by using an existing machining error, and predicting a semi-finishing error; re-establishing an ultra-precision machining error prediction model considering influence of the semi-finishing error; and finally performing finishing process planning. In the ultra-precision machining method according to the present disclosure, influence of the finishing error is considered, the ultra-precision machining error prediction model is re-established, and by integrated optimization of process parameters of the semi-finishing and the finishing, machining precision of ultra-precision machining is further improved without reducing machining efficiency.