An Intelligent Manufacturing System for Injection Molding

In recent years, the great trends of industry 4.0, internet of things (IoT), big data analytics, and cloud computing, the design and development of plastic injection molding (PIM) products has been more requested to achieve the requirements of light, thin, short, small, multi-function, high-precision, energy-saving, and obliged to fulfill a large number of customized production. To tackle this arduous challenge, effectively developing a novel PIM intelligent manufacturing system will play a crucial role. The aim of the proposed study is to carry on building an intelligent manufacturing system (IMS) for PIM industry, which is composed of three subsystems: a multiple response optimization systems of PIM, a database management system of process parameters, and a PIM real-time monitoring and control system. Firstly, the multiple response optimization systems present an intelligent optimization system to find optimal process parameters of multiple quality characteristics in the PIM process. Secondly, the database management system allows for saving the experimental data, PIM process parameter settings and quality goals. The third is a PIM real-time monitoring and control system, which establishes a graphic monitoring and control interface to real-time monitor the parameters of PIM machine and the optimal process parameter settings. The proposed PIM intelligent manufacturing systems enable the functions of real-time monitoring, process parameter optimization and database management, which can assure better PIM product quality and yield rate, effectively reduce the manufacturing cost, and promote the competition of the PIM industry in the future

AN ON-LINE INTELLIGENT ADAPTIVE CONTROLLER FOR MANUFACTURING OPERATIONS BASED ON AN OPEN ARCHITECTURE

In the development of the unattended and self-adjust machining system, where the human operator must be replaced by a Computer Numerical Control, the control system should properly process information with its varying environment in an intelligent way. The intelligent CNC must be able to recognize, in real time, major problems of operation connected to the machining process like chipping problems or tool breakage. To do that, the CNC intelligent system must be able to receive, process, and analyze inputs from multiple types of external sensor attached to it. This approach can only be possible on the base of an Open Architecture. The author of this paper proposes the use of fuzzy logic to develop an intelligent adaptive controller for manufacturing operations based on an open architecture that must be able to face problems of control, monitoring and fault diagnosis in real time

Harnessing IoT Data and Knowledge in Smart Manufacturing

In the modern digitalized era, the use of electronic devices is a necessity in daily life, with most end users requiring high product quality of these devices. During the electronics manufacturing process, environmental control, for monitoring ambient temperature and relative humidity, is one of the critical elements affecting product quality. However, the manufacturing process is complicated and involves numerous sections, such as processing workshops and storage facilities. Each section has its own specific requirements for environmental conditions, which are checked regularly and manually, such that the whole environmental control process becomes time-consuming and inefficient. In addition, the reporting mechanism when conditions are out of specification is done manually at regular intervals, resulting in a certain likelihood of serious quality deviation. There is a substantial need for improving knowledge management under smart manufacturing for full integration of Internet of Things (IoT) data and manufacturing knowledge. In this chapter, an Internet-of-Things Quality Prediction System (IQPS), which is a mission critical system in electronics manufacturing, is proposed in adopting the advanced IoT technologies to develop a real-time environmental monitoring scheme in electronics manufacturing. By deploying IQPS, the total intelligent environmental monitoring is achieved, while product quality is predicted in a systematic manner

Developing sensor signal-based digital twins for intelligent machine tools

Abstract Digital twins can assist machine tools in performing their monitoring and troubleshooting tasks autonomously from the context of smart manufacturing. For this, a special type of twin denoted as sensor signal-based twin must be constructed and adapted into the cyber-physical systems. The twin must (1) machine-learn the required knowledge from the historical sensor signal datasets, (2) seamlessly interact with the real-time sensor signals, (3) handle the semantically annotated datasets stored in clouds, and (4) accommodate the data transmission delay. The development of such twins has not yet been studied in detail. This study fills this gap by addressing sensor signal-based digital twin development for intelligent machine tools. Two computerized systems denoted as Digital Twin Construction System (DTCS) and Digital Twin Adaptation System (DTAS) are proposed to construct and adapt the twin, respectively. The modular architectures of the proposed DTCS and DTAS are presented in detail. The real-time responses and delay-related computational arrangements are also elucidated for both systems. The systems are also developed using a Java™-based platform. Milling torque signals are used as an example to demonstrate the efficacy of DTCS and DTAS. This study thus contributes toward the advancement of intelligent machine tools from the context of smart manufacturing

An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data.publishedVersio

Managing quality control systems in intelligence production and manufacturing in contemporary time

In contemporary time, the production arena has become an interesting scene with introduction of innovations that is changing the climate of production technology. The innovation comes in the form of smart technique of production for an enhanced productivity. Which is termed intelligence manufacturing or production. Intelligence manufacturing has led to enhance productivity in manufacturing sector in recent time on account of 4th industrial revolution. Intelligent manufacturing (IM) involves the use of sophisticated and advanced analytics, sensor application in robotics and internet based connections to things popularly known as IoT. This study was centered on application of intelligent manufacturing in industrial productivity and cost/time wastage. Purposive sampling method was adopted in this study, random sampling survey methods was used to pick samples for collation of responses from production managers of manufacturing companies at the study area (Lagos state, Nigeria). Population frame of 100 product manufacturing companies was adopted, out of which 73 respondents that constitute production managers and supervisors were selected using random sampling technique. The study censored the opinion and view of professionals such as managers (production), production supervisors on calibrated. The study highlighted emerging areas of application of quality monitoring system in intelligent manufacturing to include advanced analytical tools and censored based applications such as robotics applications used in design and product calibration, virtual and augmented reality application that simulates real situation using virtual approach, machine learning, expert systems (AI), block chain technology, drones for real time supervision of production process

Algorithms on determining the correlation laws between ultrasonic images and quality of spot welds.

Conventional quality control devices for spot welding cannot perform on-line inspection and provide feedback to the welding control system. In this way, the traditional quality control systems are similar to statistical welding parameters monitoring systems. It is imperative to combine the idea of on-line quality inspection with closed-loop feedback control in a robust control system. However, there is no single acoustic method to date capable of manipulating real-time control and on-line quality inspection, concurrently, since specific procedures (e.g. scanning time and adjustment time) need to be adopted by traditional acoustic microscopes to retrieve proper information, and these procedures tend to disable the real-time and on-line capability of acoustic microscopy. With recent hardware improvements, the novel portable acoustic device is able to reduce the scanning time to real-time fashion without losing any significant data. On the other hand, the adjustment time of the portable acoustic device can be reduced noticeably by employing intelligent control software instead of human operators. This new hardware-software configuration will be an ideal approach to the on-line, real-time nondestructive inspection of spot welds. The primary goal of this research is to develop an intelligent system to accomplish the on-line, real-time nondestructive inspection for spot welds. The following objectives were fulfilled to reach the final goal. (1) Classification of the acoustic images of spot welds. (2) Quantification of acoustic information as parameters. (3) The study of the influence of each parameter on the strength of spot welds. (4) Identification of important and significant parameters. (5) Integration of these parameters into the knowledge base of the software. The system developed can be an on-line advisor that is capable of providing critical information about the quality of spot welds during the process. Furthermore, this system is able to render warning signals to the process control unit to prevent further mistakes.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .L33. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1132. Advisers: Roman Maev; Michael Wang. Thesis (Ph.D.)--University of Windsor (Canada), 1999

RFID-Enabled Dynamic Value Stream Mapping for Smart Real-Time Lean-Based Manufacturing System

Lean Manufacturing has become the most popular and dominant management strategy in the pursuit of perfection and in strengthening the competitive edges of manufacturers to face the challenges in the global markets. However, today’s global markets drive manufacturers to create highly customer-oriented job-shop manufacturing systems characterized by high dynamic behavior, uncertainty and high variability, in contradiction to lean being originally designed for high repetitive-production systems with a high-volume low-mix work environment with stable demand and a low degree of customization. Moreover, since the product is the changing agent, another challenging aspect that faces the effectiveness of lean is that the product life cycle is rapidly decreasing; and thus some of the lean initiatives often die after the product life cycle ends. In this regard, in order to constantly cope with the resulting rapid changes and adapt new process designs while reviving lean initiatives and keeping them alive; an effective real-time lean-based IT system should be developed, since lean without a real-time IT system has become impracticable and unthinkable in today’s high-customized manufacturing environments. In this context, due to the special characteristics and superior capabilities of Radio Frequency Identification technology (RFID), it could be the major enabler to support such a real-time IT system with real-time production data. However, RFID remains questionable and doubtable and manufacturers are still quite hesitant to adopt it in their manufacturing systems. This thesis introduces a solid basis for a standard framework of a digitalized smart real-time lean-based system. This framework describes the best practice of RFID technology through the integration of real-time production data captured via RFID with lean manufacturing initiatives in manufacturing systems, in order to overcome today’s lean manufacturing challenges. The introduced framework represents a new kind of smart real-time monitoring and controlling lean-based IT mechanism for the next-generation of manufacturing systems with dynamic and intelligent aspects concerning lean targets. The idea of this mechanism has been derived from the main concepts of traditional value stream mapping (VSM), where the time-based flow is greatly emphasized and considered as the most critical success factor of lean. The proposed mechanism is known as Dynamic Value Stream Mapping (DVSM), a computerized event-driven lean-based IT system that runs in real-time according to lean principles that cover all manufacturing aspects through a diversity of powerful practices and tools that are mutually supportive and synergize well together to effectively reduce wastes and maximize value. Therefore, DVSM represents an intelligent, comprehensive, integrated, and holistic real-time lean- based manufacturing system. The DVSM is proposed to contain different types of engines of which the most important engine is the “Lean Practices and Tools Engine” (LPTE) due to its involvement with several lean modules that guarantees the comprehensiveness of the real-time lean system. Each of these modules is specified to control a specific lean tool that is equipped with suitable real-time monitoring and controlling rules called “Real-Time Lean Control Rules” (RT-LCRs), which are expressed using “Complex Event Processing” (CEP) method. The RT-LCRs enable DVSM to smartly detect any production interruptions or incidents and accordingly trigger real-time re/actions to reduce wastes and achieve a smart real-time lean environment. Practically, the basis of this introduced framework in this dissertation is derived based on a highly customized job-shop manufacturing environment of an international switchgear manufacturer in Germany. The contributions of this dissertation are represented as follows: building the main framework of the DVSM starting with a systematic RFID deployment scheme on the production shop floor; introducing the main components of the DVSM (i.e. Event Extractor-engine, AVSM-engine, VVSM-engine, Real-time Rules-engine, and LPTE); demonstrating the feasibility of the DVSM concerning lean targets through developing a number of Lean Practices and Tools Modules that are supplied with RT-LCRs (e.g. Real-time Manufacturing Lead-time Analysis, Smart Real-time Waste Analysis, Real-time Dispatching Priority Generator (RT-DPG), Real-time Smart Production Control (RT-SPC), Smart-5S, Smart Standardized Work, Smart Poka-Yoke, Real-time Manufacturing Cost Tracking (RT-MCT), etc.); verifying the effectiveness of RT-LCRs in RT-DPG and RT-MCT modules through building simulation models using ProModel simulation software and finally proposing a framework of the tools “Smart-5S, Smart Standardized Work, Smart Poka-Yoke” to be implemented in the switchgear manufacturing environment

Process Optimization Towards The Development Of An Automated Cnc Monitoring System For A Simultaneous Turning And Boring Operation

Manufacturing operations generate revenue by adding value to material through machine work and the cost associated with part production hinders the maximum profit available. In order to remain competitive, companies invest in research to maximize profit and reduce waste of manufacturing operations. This results in cheaper products for the customer without sacrificing quality. The purpose of this research was to identify machine settings of an Okuma LC 40 Turning Center and optimize the cost of machining in terms of tool cost and energy consumption while maintaining part quality at a productive cycle time. Studying the relationship between energy consumption, tool life, and cycle time with the speed and feed settings through statistical Analysis of Variance (ANOVA) method will allow the production plant to make profitable financial decisions concerning simultaneous turning operation of forged chrome-alloy steel. The project was divided into three phases; the first phase began with a literature survey of sensors used in current manufacturing research and the adaptation of our sensors to the Okuma LC 40 turning center. Then, phase II used design of experiments to identify spindle speed and feedrate settings that optimize multiple responses related to the turning process. The result was a saving in energy consumption (kWh) by 11.8%, a saving in cutting time by 13.2% for a total cost reduction from $1.15 per tool pass to$1.075 per tool pass. Furthermore, this work provides the foundation for phase III to develop an intelligent monitoring system to provide real-time information about the state of the machine and tool. For a monitoring system to be implemented in production, it should utilize cost effective sensors and be nonintrusive to the cutting operatio

Reliable Wireless Data Transfer in a High EMI Environment

DissertationIndustrial wireless communication has had many recent developments in improving transmission to overcome Electromagnetic Interference (EMI) in the industrial environment. Many industrial monitoring systems available on the market work on real-time communication, with nodes and data logging, which has limitations as to number of nodes that can be supported depending on the system’s processor capabilities. This research focuses on a different approach to design and implements a wireless industrial monitoring system using LabVIEWTM, a system which is not limited by a certain number of nodes with real-time data logging at nodes and data transmission taking the EMI environment into consideration. In addition, the study looks at creating a wireless monitoring system where delays in system updates are acceptable until real-time log data can be received. Wireless monitoring devices have many advantages over cable or wired industrial monitoring; including fast deployment, intelligent processing capability and flexibility. In this dissertation, design principles are described in terms of hardware and software development. The aim is to use the existing wireless communication network “IEEE 802.11b/g/n 2.4 GHz” at an industrial plant for communication using techniques to avoid or lower the amount of data which is lost. Moreover, this research looks at processing and logging data at nodes to allow the receiver/server to communicate with unlimited nodes in a timely manner. The research was performed, developed and tested at Route Management-South African Truck Bodies manufacturing plant