A Parametric-Controlled Cavity Layout Design System for a Plastic Injection Mould

Abstract

Today, the time-to-market for plastic products is becoming shorter, thus the lead time available for making the injection mould is decreasing. There is potential for timesaving in the mould design stage because a design process that is repeatable for every mould design can be standardised. This paper presents a methodology for designing the cavity layout for plastic injection moulds by controlling the geometrical parameters using a standardisation template. The standardisation template for the cavity layout design consists of the configurations for the possible layouts. Each configuration of the layout design has its own layout design table of all the geometrical parameters. This standardisation template is pre-defined at the layout design level of the mould assembly design. This ensures that the required configuration can be loaded into the mould assembly design very quickly, without the need to redesign the layout. This makes it useful in technical discussions between the product designers and mould designers prior to the manufacture of the mould. Changes can be made to the 3D cavity layout design immediately during the discussions, thus saving time and avoiding miscommunication. This standardisation template for the cavity layout design can be customised easily for each mould making company to their own standards.

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

“…Thus, studies have largely focused on a variety of perspectives to enhance the efficiency and sustainability of the injection-molding manufacturing processes. Regarding the literature on cavities, which constitute a major part of injection molding [14], literature focused on ways to save manufacturing time. One approach was to exploit the intelligent cavity layout design system to help injection molding designers in cavity design steps [15].…”

“…Focusing on its implications for solving issues in manufacturing businesses, research has focused on predicting failures [6]. Cinar et al (2020) [14] and Binding, Dykeman, and Pang (2019) [19] forecasted the downtime of manufacturing machines using real-time prediction models. They utilized unstructured historical machine data to train the machine learning classification algorithms, including random forest, XGBoost, and logistic regression, to predict machine failures [6].…”

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

“…Equation 3ensures the precedence relations between the consecutive operations of a part for the job. Equations (4) and (5) guarantee that the overlapping of the operations on the machine is restrained. Equation 6means that the job cannot be started before the release time itself.…”

Laser powder bed fusion of the steels used in the plastic injection mould industry: a review of the influence of processing parameters on the final properties

“…The plastic injection moulding industry is one of the fastest-growing industries in the world since a lot of products that are used in daily life involve the use of plastics [27][28][29]. Despite having numerous advantages, namely high dimensional and geometric precision, repeatability, and adaptability to a wide range of raw materials [30,31], the costs associated with the mould and the injection machine are high [32,33].…”