The research in this dissertation proposes Bayesian-based predictive analytics for modeling and prediction of the manufacturing metrics such as cutting force, tool life and reliability in the technological era of Industry 4.0. Bayesian statistics is a probabilistic method, which can quantify and minimize manufacturing process uncertainties. The Bayesian method combines previous knowledge about the manufacturing models with experimental data to predict the manufacturing metrics
Project performance models play an important role in the management of project success. When used fo...
Bayesian predictive methods have a number of advantages over traditional statistical methods. For o...
We present a novel approach to estimating the effect of control parameters on tool wear rates and re...
Manufacturing is usually performed as a sequence of operations such as forming, machining, inspectio...
This work discusses the Bayesian parameter inference method for a mechanistic force model for machin...
Machining process modeling & simulation as well as in-process monitoring and control have been ident...
There is an increasing demand for manufacturing processes to improve product quality and production ...
Probabilistic prediction of cutting and ploughing forces is performed by applying Bayesian inference...
Analysing and modelling efforts on production throughput are getting more complex due to random vari...
Manufacturing is a promising technique for producing complex and custom-made parts with a high degre...
In manufacturing processes various machines are used to produce the same product. Based on the age, ...
We present a novel approach to estimating the effect of control parameters on tool wear rates and re...
Machine learning methods have become increasingly popular with the release of numerous open-source t...
Cutting tool wear reduces the quality of the product in production processes. The optimization of bo...
AbstractA Smart Manufacturing (SM) system should be capable of handling high volume data, processing...
Project performance models play an important role in the management of project success. When used fo...
Bayesian predictive methods have a number of advantages over traditional statistical methods. For o...
We present a novel approach to estimating the effect of control parameters on tool wear rates and re...
Manufacturing is usually performed as a sequence of operations such as forming, machining, inspectio...
This work discusses the Bayesian parameter inference method for a mechanistic force model for machin...
Machining process modeling & simulation as well as in-process monitoring and control have been ident...
There is an increasing demand for manufacturing processes to improve product quality and production ...
Probabilistic prediction of cutting and ploughing forces is performed by applying Bayesian inference...
Analysing and modelling efforts on production throughput are getting more complex due to random vari...
Manufacturing is a promising technique for producing complex and custom-made parts with a high degre...
In manufacturing processes various machines are used to produce the same product. Based on the age, ...
We present a novel approach to estimating the effect of control parameters on tool wear rates and re...
Machine learning methods have become increasingly popular with the release of numerous open-source t...
Cutting tool wear reduces the quality of the product in production processes. The optimization of bo...
AbstractA Smart Manufacturing (SM) system should be capable of handling high volume data, processing...
Project performance models play an important role in the management of project success. When used fo...
Bayesian predictive methods have a number of advantages over traditional statistical methods. For o...
We present a novel approach to estimating the effect of control parameters on tool wear rates and re...