Zhang J, Alexander SM (2008) Fault diagnosis in injection moulding via cavity pressure signals. Speranza V, Vietri U, Pantani R (2011) Monitoring of injection molding of thermoplastics: average solidification pressure as a key parameter for quality control. Statistical variables are proved to be more effective and reliable for quality monitoring and fault detection compared with the whole process control variables, which are used in the classical MPCA, owning to a better compliance with the Gaussian distribution. Experimental results show that the rate of successful fault detection of the present method reaches 91.48 % at the confidence level 99 %, compared to 3.93 % using the multi-way principal component analysis (MPCA) method. They are subsequently monitored by applying the principal component analysis method. Statistical variables, which are representatives of the product quality in one batch, are first automatically extracted from the original data acquired. In this paper, a statistical quality monitoring method is proposed, using only hydraulic pressure and screw position data obtained from built-in machine sensors. A challenge in quality monitoring using machine built-in sensors is identifying couplings among different control trajectories and complicated effects on the product quality. Majority of the current quality monitoring for injection molding relies on extra sensors assembled within the mold, which not only is restricted by the mold structure but also requires additional costly investment.
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