A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)

Int. J. Precis. Eng. Manuf.

Lee

et al. 2020

Self Cite

In the manufacturing industry, the smart factory is considered the final stage of the Fourth Industrial Revolution. Manufacturing companies are pursuing breakthroughs by introducing various advanced technologies to ensure their competitiveness. However, it is difficult for small and medium-sized enterprises (SMEs) to adopt smart-factory technologies, owing to financial and technical burdens. This paper proposes a smart factory that can be applied technically and strategically to the introduction of a smart factory for SMEs. The concept of an ‘appropriate smart factory’ involves applying appropriate measures in terms of cost and scale with consideration of the situations faced by SMEs. The goal is to build a smart factory that has necessary functions (Essential) but can be easily operated (Simple) at a low cost (Affordable) and has compatibility (Interoperable). This paper presents technical application measures such as appropriate smart sensors, appropriate IoT (Internet of Things), and small data processing, along with the definition of an appropriate smart factory. In addition, a case study was examined where the quality inspection equipment for garment manufacturing SMEs was developed by applying the appropriate smart factory concept.

Section: Appropriate Vision-based Monitoringmentioning

confidence: 99%

Appropriate Smart Factory for SMEs: Concept, Application and Perspective

Jung

Int. J. Precis. Eng. Manuf.

Lee

et al. 2020

Self Cite

“…Thus, to increase the utilization of ME process printers, users must monitor their printers at the same location constantly during the printing process. In this manual and traditional approach, however, watching the printing status requires much labor and time [17]. Therefore, a real-time monitoring system must be considered to reduce failed printing jobs and to reduce the unproductive time required for manual monitoring of the printing process.…”

Section: Fault Detection In the Me Processmentioning

confidence: 99%

Image-based failure detection for material extrusion process using a convolutional neural network

Lee

Int J Adv Manuf Technol

et al. 2020

Self Cite

The material extrusion (ME) process is one of the most widely used 3D printing processes, especially considering its use of inexpensive materials. However, the error known as the “spaghetti-shape error,” related to filament tangling, is a common problem associated with the ME process. Once occurring, this issue, which consumes both time and materials, requires a restart of the entire process. In order to prevent this, the user must constantly monitor the process. In this research, a failure detection method which uses a webcam and deep learning is developed for the ME process. The webcam captures images and then analyzes them by machine learning based on a convolutional neural network (CNN), showing outstanding performance in both image classification and the recognition of objects. Sample images were trained based on a modified Visual Geometry Group Network (VGGNet) model and the trained model was evaluated, resulting in 97% accuracy. The pre-trained model was tested on a 3D printer monitoring system for its ability to recognize the “spaghetti-shape-error” and was able to detect 96% of abnormal deposition processes. The proposed method can analyze the ME process in real time and informs the user or halts the process when abnormal printing is detected.

“…Nevertheless, the intelligence goal should be considered in the design process of the structure, control system, and sensor installation to enhance the function of machine tools. Smart sensor design for structural components of machine tools as a crucial step must be taken into account under the condition of keeping the reliability of data [132] and enhancing signal quality like capacitive displacement measurements [133]. Additionally, the process of integrating intelligence into machine tools design contributes to making decisions in the sustainable machining planning [134].…”

Section: Other Technologiesmentioning

confidence: 99%

The Analysis of Key Technologies for Sustainable Machine Tools Design

Feng

Shi

2020

Applied Sciences

Machine tools as indispensable tools for manufacturing products are typical high-energy, high-carbon manufacturing systems due to their larger mass, longer life cycles and huge resources and energy consumption. The current research trend of sustainable machine tools aims to reduce cost, energy consumption and increase sustainability without compromising their functionality, usability, productivity, accuracy, etc. However, there is a lack of systematic reviews about what are the key technologies for sustainable machine tools design. Thus, a comprehensive literature review of machine tool design for sustainability is needed in order to make clear how to design and evaluate sustainable machine tools from the viewpoint of life cycle sustainability analysis. The aim of this paper is to review the sustainable design and assessment aspects of machine tool design from partial goals to integrated aims according to whole sustainability dimensions such as the environment, economy and society, as well as involving key techniques in different life cycle stages. Recent research and study on improving directly or indirectly sustainability performance of machine tools according to focus points could be summarized as: design optimization of components such as lightweight using topology and bionic methods; structure design with modular design and layout; reducing cutting fluids and lubricant oil by employing minimum quantity lubrication, dry and cryogenic machining; reducing energy consumption; waste reduction by reusing, remanufacturing and recycling; sustainability assessment i.e., energy model, life cycle cost and life cycle social benefit. This paper assists designers and manufacturers to improve sustainability of machine tools by specific optimization measures in their activities.