Cutting model integrated digital twin–based process monitoring in small-batch machining

Open Architecture Modular Tool Kit for Motion and Machining Process Control

The acceleration of the spindle in the axial direction was measured when preharden steel was slotted with a square end mill under several cutting conditions. The signal of the accelerometer was stored and processed through digital Fourier transformation on a personal computer. The power spectra had peaks in a distinctive frequency range when chatter occurred. There is a threshold value of the ratio of the maximum intensity in the range to the one at the frequency of interrupted cutting by edges for detection of chatter.

The monitoring of machining process seems to be the crucial for effectiveness and accuracy. The precise monitoring needs fast, efficient, easy and open communication standard for data interchange or data acquisition. The use a popular, inexpensive and ordinary network system instead complicated and valuable professional/industrial field-bus is really attractive. The paper presents some conception of the office Ethernet implementation as data transmission structure for monitoring. The basic, laboratory tests with different protocols and configuration were done. As a conclusion several rules were formulated to help potential users.

Milling is one of the most important metal cutting processes. If we measure the cutting force, we can make cutting process optimum, prevent tool breakage, and improve efficiency of process. However in the case of using an end mill with small diameter, it is very difficult to identify whether cutting process is good or not. In this paper, we proposed the cutting force measurement technique by using image processing. In the technique, we captured a image of the end mill during cutting process, determined deflection of the tool and then estimated cutting force. Finally, we can estimate small cutting force with high accuracy.

Research in automating the process level of machining operations has been conducted, in both academia and industry, over the past few decades. This work is motivated by a strong belief that research in this area will provide increased productivity, improved part quality, reduced costs, and relaxed machine design constraints. The basis for this belief is two-fold. First, machining process automation can be applied to both large batch production environments and small batch jobs. Second, process automation can autonomously tune machine parameters (feed, speed, depth of cut, etc.) on-line and off-line to substantially increase the machine tool’s performance in terms of part tolerances and surface finish, operation cycle time, etc. Process automation holds the promise of bridging the gap between product design and process planning, while reaching beyond the capability of a human operator. The success of manufacturing process automation hinges primarily on the effectiveness of the process monitoring and control systems. This paper discusses the evolution of machining process monitoring and control technologies and conducts an in-depth review of the state-of-the-art of these technologies over the past decade. The research in each area is highlighted with experimental and simulation examples. Open architecture software platforms that provide the means to implement process monitoring and control systems are also reviewed. The impact, industrial realization, and future trends of machining process monitoring and control technologies are also discussed.

ABSTRACTMachining process monitoring is essential to guarantee machining quality and reduce production cost, especially for the parts with complex structures and high accuracy requirement. However, effective machining process monitoring is still a challenge in industry. Particularly, a smooth communication between remote control computers and CNC (Computerized Numerical Control) systems is required. To address this requirement, a remote machining process monitoring method based on features and web is proposed. Machining feature model is established to facilitate the communication among the involved modules in the system. A web-based method is adopted to realize remote monitoring and control for multiple machines, as well as to alleviate the computational load of CNC systems. In addition, a feature-based database is constructed as a communication hub, where feature information, monitoring strategies and monitoring results are stored. A prototype system has been developed to validate the feasibility of the ...

Three-axis displacement sensor-integrated spindle system for carbon fiber-reinforced plastic (CFRP) machining process monitoring

To ensure the safety and processing quality of high investment automation processing equipment, machining process monitoring is becoming an urgent problem to be solved in the modern machining system.

In the study of metal cutting, the data related to cutting are generally obtained by way of a cutting test. For example, the cutting force coefficient used in cutting force prediction is usually obtained through a cutting test.

An investigation of tool-wear monitoring in a high-speed machining process

New demands are being placed on monitoring systems in the manufacturing environment because of recent developments in machining technology and machine tool design. In-process sensors are used to generate control signals to improve both the control and productivity of manufacturing systems. Numerous different sensors are available for monitoring and controlling the machining environment including force, power, and acoustic emission (AE) sensors. This paper first discusses the requirements for sensor technology for precision manufacturing process monitoring in general. Details are also given about AE and its application for process characterisation and monitoring in ultraprecision machining.

Health monitoring of CNC machining processes using machine learning and wavelet packet transform

Applying the method based on Petri net can join knowledge expression and diagnosis reasoning together. Firstly, the symptomatic places reflecting failures were established for the original places and the fault places causing failures were established for the objective places. Then, by prior searching in depth and maximal searching in reliability, the optimum routes from the original place sets to the objective place sets were set up. In reasoning based on Petri net, by a series of matrix calculation for the status vector recurrence, the quick diagnosing results could be got. The conclusion inferred by the Petri net reasoning was consistent with the facts. It suits for the status monitoring and fault diagnosing of modern machining processes and systems

We demonstrate in-situ monitoring of an in-bulk fs-laser machining process of a borosilicate substrate using a co-propagating continuous wave probe at 4.46 μm provided by a mid-IR quantum cascade laser.

Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as signal intensity estimator and root mean square was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.