Today, high levels of precision and accuracy are needed in manufacturing to meet the increased complexities in product designs. Most products consist of multiple assembled parts, and fitting these parts together can present a major challenge, especially for complex products. Thus manufacturing with high precision is particularly required, and CNC machining is typically used as a machining process to reduce the risk of parts not fitting together in the assembly process, especially for automatic assembly. Thereby improving quality control and reducing scrap in high-value and low volume production.Over the last 60 years, NC and CNC machines have been used to improve product quality due to their increased accuracy. However, even with today’s more sophisticated machine tools, errors still occur during machining. The literature shows that there are numerous sources of error in machining processes. Additionally, different methods are being used to define and subsequently correct these errors. The methods used to compensate these errors typically depend on offline error compensation. A gap in the existing research methods has been identified as a lack of online error compensation methods to enable parts to be manufactured to specification and corrected during the machining process. The major contribution of this research is the design and implementation of a method for production of right-first-time parts based on an online error compensation. The proposed framework, CLosEd loop MAchining and inspecTIon System (CLeMatiS), is considered to be an important approach for industry to improve the machining and measuring accuracy for high-cost parts. A computational model has been developed, where an algorithm within this model can handle different types of feature relationships and is able to update feature positions based on on-machine measurements.This research shows that the proposed method for compensating the machining errors in order to machine parts right-first-time provides advantages over traditional methods. The method thus improves the positional accuracy of machined features while maintain the relationships between them, compared to the traditional machining.
|Date of Award||13 Jul 2018|
|Supervisor||Stephen Newman (Supervisor) & Joseph Flynn (Supervisor)|