COMPOSITES 4.0: ENABLING THE MODERNIZATION OF LEGACY MANUFACTURING ASSETS IN SOUTH CAROLINA

Composites 4.0 is the implementation of Industry 4.0 concepts to plastics and composites manufacturing with the goal to overcome the complexities associated with these materials. Due to very complex process-structure-property relationships associated with plastics and composites, a wide range of process parameters need to be tracked and monitored. Furthermore, these parameters are often affected by the tool and machinery, human intervention and variability and should thus, be monitored by integrating intelligence and connectivity in manufacturing systems. Retrofitting legacy manufacturing systems with modern sensing and control systems is emerging as one of the more cost-effective approaches as it circumvents the substantial investments needed to replace legacy equipment with modern systems to enhance productivity. The goal of the following study is to contribute to these retrofitting efforts by identifying the current state-of-the-art and implementation level of Composites 4.0 capabilities in the plastics and composites manufacturing industry. The study was conducted in two phases, first, a detailed review of the current state-of-the-art for Industry 4.0 in the manufacturing domain was conducted to understand the level of integration possible. It also helped gain insights into formulating the right questions for the composites manufacturing industry in South Carolina. Second, a survey of the plastics and composites manufacturing industries was performed based on these questions, which helps identify the needs of the industry and the gap in the implementation of Composites 4.0. The study focuses on the three leading composite manufacturing industries: injection molding, extrusion, and 3D printing of thermoset and thermoplastic materials. Through the survey, it was possible to identify focus areas and desired functionalities being targeted by the industries surveyed and concentrate research efforts to develop targeted solutions. After analyzing the survey responses, it was found that updating old protocols using manufacturer support and customized integration of cost-effective solutions like retrofit kits, edge gateways, and smart sensors were identified as best-suited solutions to modernize the equipment. Composites 4.0 is already being implemented for Preventive Maintenance (PM), Manufacturing Execution System (MES), and Enterprise Resource Planning (ERP) to some extent, and the focus is on process optimization and equipment downtime reduction. The inferences drawn from this study are being used to develop highly targeted, supplier-agnostic solutions to modernize legacy manufacturing assets

In-process pokayoke development in multiple automatic manufacturing processes

In this dissertation, three in-process pokayoke systems were developed to prevent defects from occurring, so as to ensure product quality for three automated manufacturing processes.;The first pokayoke development resulted in an in-process, gap-caused flash monitoring (IGFM) system for injection-molding machines. An accelerometer sensor was integrated in the proposed system to detect the difference of the vibration signals between flash and non-flash products. By sub-grouping every two consecutive molded parts with the vibration signal, the online statistical process control (OLSPC) was able to monitor 100% of the molded products. The threshold of this system established by the SPC approach can determine if flash occurred when the machine was in process. The testing results indicated that the accuracy of this IGFM system was 94.7% when flash is caused by a mold-closing gap.;The second pokayoke development led to an in-process surface roughness adaptive control (ISRAC) system for CNC end milling operations. A multiple linear regression algorithm was successfully employed to generate the models for predicting surface roughness and adaptive feed rate change in real time. Not only were the machining parameters included in the ISRAC pokayoke system, but also the cutting force signals collected by a dynamometer sensor. The testing results showed this proposed ISRAC system was able to predict surface roughness in real time with an accuracy of 91.5%, and could successfully implement adaptive control 100% of the time during milling operations.;The third pokayoke development brought an in-process surface roughness adaptive control (ISRAC) system in CNC turning operations. This system employed a back-propagation (BP) neural network algorithm to train the models for in-process surface roughness prediction and adaptive parameter control. In addition to the machining parameters, vibration signals in the Z direction used as an input variable to the neural network system were included for training. The test runs showed this pokayoke system was able to predict surface roughness in real time with an accuracy of 92.5%. The 100% success rate for adaptive control proved that this proposed system could be implemented to adaptively control surface roughness during turning operations

IIoT Framework for SME level Injection Molding Industry in the Context of Industry 4.0

The Internet of Things (IoT) is a hype topic for nearly a decade now. Broadly growing, millions of devices get direct access to the Internet provides plenty of applications such as smart homes or mobile health management. This trend can also be found in the industry where IoT components hardened for these environments are introduced, called Industrial IoT (IIoT) devices which can be either sensors or actors, as well as mobile equipment such as smartphones, tablets, and smart glasses. Consequently, mobile communication becomes universal in smart factories. IIoT devices provide massive data on temperature, pressure, machine states, etc. But still, most of the SME level industries in the Asian region are new to these technological advancements. They still operate their facilities ith conventional setups without absorbing the new opportunities which are presented by IoT. In the plastic injection molding industry, process parameters perform a significant role in the quality of the output product. During the manufacturing process, these process parameters have to deal with various factors such as quality and type of materials, requirement tolerance levels of the output product, Environmental conditions like temperature and humidity, etc. Injection molding has been a challenging process for many SME level manufacturers to produce products while meeting the quality requirements at the lowest cost. Most of them are unable to reach the global market in the injection molding industry due to the non-availability of the proper methods to determine the process parameters for injection molding. During production, quality characteristics may differ due to drifting or shifting of processing conditions caused by machine wear, environmental change, or operator fatigue. By determining the optimal process parameter settings productivity and quality will increase while reducing the cost of production. In this paper, we suggest an Industrial IoT framework that can develop for small- and medium-sized enterprises (SMEs) level industries to optimize their production facility. With the presented framework SME level industries can start to inherit IoT devices into their production floor to manage and monitor production parameters in real-time while improving the quality of the production

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

Automatic online algorithm selection for optimization in cyber-physical production systems

Shrinking product lifecycles, progressing market penetration of innovative product technologies, and increasing demand for product individualization lead to frequent adjustments of production processes and thus to an increasing demand for frequent optimization of production processes. Offline solutions are not always available, and even the optimization problem class itself may have changed in terms of the value landscape of the objective function: Parameters may have been added, the locations of optimal values and the values themselves may have changed. This thesis develops an automatic solution to the algorithm selection problem for continuous optimization. Furthermore, based on the evaluation of three different real-world use cases and a review of well-known architectures from the field of automation and cognitive science, a system architecture suitable for use in large data scenarios was developed. The developed architecture has been implemented and evaluated on two real-world problems: A Versatile Production System (VPS) and Injection Molding Optimization (IM). The developed solution for the VPS was able to automatically tune the feasible algorithms and select the most promising candidate, which significantly outperformed the competitors. This was evaluated by applying statistical tests based on the generated test instances using the process data and by performing benchmark experiments. This solution was extended to the area of multi-objective optimization for the IM use case by specifying an appropriate algorithm portfolio and selecting a suitable performance metric to automatically compare the algorithms. This allows the automatic optimization of three largely uncorrelated objectives: cycle time, average volume shrinkage, and maximum warpage of the parts to be produced. The extension to multi-objective handling for IM optimization showed a huge benefit in terms of manual implementation effort, as most of the work could be done by configuration. The implementation effort was reduced to selecting optimizers and hypervolume computation

Intelligent system to support micro injection process through artificial intelligent techniques and cae model integration

Trabajo de investigaciónIn this project a propose of integration of CAE Modeling and artificial intelligence systems to support the process in the production of micro plastic parts is presented. Based on analysis provided by CAE systems, studies will be carried out for diverse parts, to be analyses and throw to artificial intelligent techniques give recommendations of optimal values of plastic micro injection process.1. INTRODUCTION 2. PROBLEM STATEMENT 3. OBJECTIVES 4. CONCEPTUAL FRAMEWORK 5. THEORETICAL FRAMEWORK 6. STATE OF THE ART 7. METHODOLOGY 8. DESCRIPCION OF PROJECT 9. RESULTS 10. VALIDATION OF PROJECT 11. CONCLUSIONS AND FUTURE WORKS 12. REFERENCES 13. ANNEXESMaestríaMagister en Ingeniería y Gestión de la Innovació

Remanufacturing and Advanced Machining Processes for New Materials and Components

"Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems.

Design of hybrid deep learning using TSA with ANN for cost evaluation in the plastic injection industry

In the plastic injection industry, plastic injection molding is one of the most extensively used mass production technologies and has been continuously increasing in recent years. Cost evaluation is essential in corporate operations to increase the market share and lead in plastic part pricing. The complexity of the plastic parts and manufacturing data resulted in a long data waiting time and inaccurate cost evaluation. Therefore, the aim of this research is to apply a cost evaluation approach that combines hybrid deep learning of a tunicate swarm algorithm (TSA) with an artificial neural network (ANN) for the cost evaluation of complicated surface products in the plastic injection industry to achieve a faster convergence rate for optimal solutions and higher accuracy. The methodology entails the ANN, which applies feature-based extraction of 3D-model complicated surface products to develop a cost evaluation model. The TSA is used to construct the initial weight into the learning model of the ANN, which can generate faster-to-convergent optimal solutions and higher accuracy. The result shows that the new hybrid deep learning TSA combined with the ANN provides more accurate cost evaluation than the ANN. The prediction accuracy of cost evaluation is approximately 96.66% for part cost and 93.75% for mold cost. The contribution of this research is the development of a new hybrid deep learning model combining the TSA with the ANN that includes the calculation of the number of hidden layers specifically for complicated surface products, which are unavailable in the literature. The cost evaluation approach can be practically applied and is accurate for complicated surface products in the plastic injection industry