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

Advanced Monitoring of Manufacturing Process through Video Analytics

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

Angular error measurement of workpiece repositioning using a full-scale rotation detection method

Workpiece repositioning error has always been a key factor affecting manufacturing accuracy. The issues become more sensitive when machining microstructures with special morphologies, where the declination error caused by the repositioning may lead to microstructural defects. To solve this practical problem, in this paper, we report the design of a fixture that can detect the plane angular displacement error between the workpiece and the tool, namely the Rotation Correction Fixture (RCF). The fiducial marker referred to as polar microstructure is proposed and placed on the RCF edge. Angular displacement measurement is realized by observing the microstructural changes. Simultaneously, a Full-scale Rotation Detection (FRD) method is proposed to obtain the full-scale and high-precision angular displacement, including coarse extraction based on Fourier transform and fine extraction based on the Fast and Robust Feature-based Positioning method. Template matching is employed to eliminate the phase ambiguity in the Fourier transform. The results show that the proposed method can realize the calibration of the workpiece declination with a standard deviation error of 250.24 seconds, which meets the needs of workpiece precision positioning well.

Open Source IIoT Solution for Gas Waste Monitoring in Smart Factory

Rapid development of smart manufacturing techniques in recent years is influencing production facilities. Factories must both keep up with smart technologies as well as upskill their workforce to remain competitive. One of the recent concerns is how businesses can contribute to environmental sustainability and how to reduce operating costs. In this article authors present a method of measuring gas waste using Industrial Internet of Things (IIoT) sensors and open-source solutions utilised on a brownfield production asset. The article provides a result of an applied research initiative in a live manufacturing facility. The design followed the Reference Architectural Model for Industry 4.0 (RAMI 4.0) model to provide a coherent smart factory system. The presented solution’s goal is to provide factory supervisors with information about gas waste which is generated during the production process. To achieve this an operational technology (OT) network was installed and Key Performance Indicators (KPIs) dashboards were designed. Based on the information provided by the system, the business can be more aware of the production environment and can improve its efficiency.

Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

This work presents a non-invasive and low-cost alternative to traditional methods for measuring the performance of machining processes directly on existing machine tools. A prototype measuring system has been developed based on non-contact microphones, a custom designed signal conditioning board and signal processing techniques that take advantage of the underlying physics of the machining process. Experiments have been conducted to estimate the depth of cut during end-milling process by means of the measurement of the acoustic emission energy generated during operation. Moreover, the predicted values have been compared with well established methods based on cutting forces measured by dynamometers.

Computer Vision Tool-Setting System of Numerical Control Machine Tool

An automatic tool-setting and workpiece online detecting system was proposed to study the key technologies of next-generation intelligent vision computerized numerical control (CNC) machines. A computer vision automatic tool-setting system for a CNC machine was set up on the basis of the vision tool-setting principle. A rapid vision calibration method based on the position feedback from the CNC machine was proposed on the basis of the theory of traditional vision system calibration. The coordinate mapping relationship of the image and the CNC machine, the tool-setting mark point on the workpiece, and the tool tip were calibrated. The vision system performance testing and system calibration experiments were performed. Experimental results indicated that the time consumption was 128 ms in image processing. The precision of tool setting and measuring was less than 1 μm. The workpiece positioning and processing online detection function of the system can completely meet the requirements of visual CNC machine application, and the system has wide application prospects.

The Analysis of Key Technologies for Sustainable Machine Tools Design

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.