Christopher O'HAra

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) “stop-and-go” approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks.

Injection moulding is an industrial process for the mass production of plastic components, with many parameters affecting the quality of this process. Hotspot regions in the component occur due to non-optimised process variables or limitations in the cooling system and can lead to warpage or shrinkage. Hotspots should be minimised to avoid part defects and achieve the required dimensional tolerances for precision components. This work outlines a machine-learning-based approach for predicting the maximum hotspot temperature in an injection moulded component using process simulation and in-mould sensor data. The hotspots were identified through software simulation, and then their locations and temperatures were confirmed through an actual experiment using in-mould thermocouples. Two different machine learning approaches, artificial neural network (ANN) and support vector regression (SVR), were developed using the extracted data from the sensors and a design of experiment (DOE) method. The performance of linear and Gaussian kernels was compared for the SVR method. The Gaussian SVR resulted in superior performance compared to the linear kernel. The Gaussian SVR was then compared to the ANN prediction method, where ANN showed a slightly better prediction performance. This study has two primary outcomes. First, we show the simulation results can be used to identify critical areas of the part for real-time monitoring. Secondly, embedding sensors in these locations and applying the collected data for a machine learning analysis, provides a good indication of potential quality issues such as warpage and shrinkage post-production. The use of ANN indicates an accurate prediction performance, facilitating rapid optimisation of the process for the minimisation of hotspots.

Minimising cycle time without the risk of inducing quality defects is a challenge in injection moulding (IM). Design of experiment methods (DoE) have been widely studied for optimisation of the IM process, but current methods have some limitations. Classical DoE methods require a pre-determined search space and a trade off between a high number of experiments or a risk of poorly capturing the optimum process region. Bayesian adaptive design of experiment (ADoE) is an iterative optimisation process where the results of the previous experiments are used to make an informed selection for the next design. Due to a lack of real-time information on part quality, ADoE has only previously been investigated in simulation of the IM process. This study introduces a new approach to the real-time characterisation of post-production part shrinkage using the measured temperature differential between two mould locations. The post-production metrology results of the components verified the correlation between the temperature differential measurement and part quality defined by physical dimensional measurements. A multi-objective optimisation of the cycle time and temperature differential ([[EQUATION]]) in an injection moulded component is proposed by comparing a classic DoE method, central composite design (CCD), against an ADoE approach using experimental data. ADoE achieved almost 50% reduction in the number of experiments required for the single optimisation of [[EQUATION]] and, nearly 30% decrease in the number of experiments for the optimisation of [[EQUATION]] and cycle time together. Further the optimum settings found by ADoE yielded better part dimensional stability than that determined by CCD. The adaptive experimental approach is faster than simulation-based adaptive optimisation with the further advantage that uncertainty due to unknown model parameters is avoided.

Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.