Quality and Defect Prediction in Plastic Injection Molding using Machine Learning Algorithms based Gating Systems and Its Mathematical Models

To achieve high quality products from Plastic Injection Molding (PIM) process it is very essential to identify the defective operations in automatic manner which is most challenging task. This paper proposes a Machine Learning (ML) approach to detect the complex faults occurrence during the PIM process. During initial sampling process of molding to achieve high quality and low time consumption it is essential to concentrate on the suitable determination of parameter values by considering the properties of injection molding process. For that purpose, a novel machine learning algorithms based gating system is introduced in PIM (MLGS-PIM). Technical evaluation can be done using simulation which combines the CATIA and MATLAB. Therefore in MLGS-PIM, a holistic approach is introduced to improve and predict the process quality of the parameters which is based on machine learning approaches. The considered machine learning approaches for this process are Artificial Neural Network (ANN) and Support Vector Machine (SVM). This two learning models are combined to achieve high quality under various conditions. Such novel ML based technique helps to increase the quality characteristics of the injection molding process and it is predicted with various parameter values where the simulation data and measurements are handled in an intelligent manner. The materials which are considered in the PIM process are thermoplastic polystyrene, thermoplastic acrylonitrile butadiene styrene and thermoplastic polyvinyl chloride where three types are gating systems are applied with it and consists of 3, 4 and 5 gates and as well the parameters which are measured for the output analysis are sum rate, bit error rate and convergence plot. The results show that the performance of the proposed MLGS-PIM approach significantly increases the performance when compared with the earlier approaches such as AntLion Optimization and PSO-MSQPA

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA

Plastic injection molding is one of the most used methods for producing plastic products because it can be produced at a high production rate, low cost, and ease in manufacturing. However, one defect that affects product quality is namely warpage. To reduce plastic product warpage, the injection molding process is required optimal process control to increase plastic product quality. The objective of this paper is to optimize injection molding process parameters for minimizing the warpage of plastic glass. The optimization process is divided into two phases. The Finite Element Method (FEM) was employed in the first phase to simulate 32 experiments under various parameters. The parameters of this process consist of melt temperature ranging from 180 to 230°C, mold temperature in the range of 20 – 45°C, filling time from 0.82 to 0.92 s, packing time ranging from 5.88 to 7 s and cooling time of 14 to 18 s. In the second phase, Artificial Neural Network (ANN) combined Genetic Algorithm (GA) was developed to predict the warpage and solve the optimization process to find optimal parameters. Combining the intelligent method shows that ANN and GA effectively find the optimal process parameters that can reduce the warpage of the product by 35.73% from the maximum value

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments

Systemic circular economy solutions for fiber reinforced composites

This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

Systemic Circular Economy Solutions for Fiber Reinforced Composites

This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

Characterization and Modelling of Composites, Volume II

Composites have been increasingly used in various structural components in the aerospace, marine, automotive, and wind energy sectors. Composites’ material characterization is a vital part of the product development and production process. Physical, mechanical, and chemical characterization helps developers to further their understanding of products and materials, thus ensuring quality control. Achieving an in-depth understanding and consequent improvement of the general performance of these materials, however, still requires complex material modeling and simulation tools, which are often multiscale and encompass multiphysics. This Special Issue is aimed at soliciting promising, recent developments in composite modeling, simulation, and characterization, in both design and manufacturing areas, including experimental as well as industrial-scale case studies. All submitted manuscripts will undergo a rigorous review and will only be considered for publication if they meet journal standards

Systemic Circular Economy Solutions for Fiber Reinforced Composites

This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

Discontinuous Fiber Composites, Volume II

Discontinuous fiber-reinforced polymers have gained importance in transportation industries due to their outstanding material properties, lower manufacturing costs and superior lightweight characteristics. One of the most attractive attributes of discontinuous fiber-reinforced composites is the ease with which they can be manufactured in large numbers, using injection and compression molding processes. The main aim of this Special Issue is to collect various investigations focused on the processing of discontinuous fiber-reinforced composites and the effect that processing has on fiber orientation, fiber length and fiber density distributions throughout the final product. Papers presenting investigations on the effect that fiber configurations have on the mechanical properties of the final composite products and materials were welcome in the Special Issue. Researchers who model and simulate processes involving discontinuous fiber composites as well as those performing experimental studies involving these composites were welcomed to submit papers. The authors were encouraged to present new models, constitutive laws, and measuring and monitoring techniques to provide a complete framework on these groundbreaking materials and to facilitate their use in different engineering applications