Hao Tieng

A novel virtual metrology scheme for predicting machining precision of machine tools

Because virtual metrology (VM) can achieve real-time and on-line total inspection, it is a promising way for measuring machining precision of machine tools. However, the machining processes possess the characteristics of severe vibrations. Thus, how to effectively handle signals with low signal/noise ratios and extract key features from them is a challenging issue for successfully applying VM to the machine tools. In this paper, a novel VM scheme for predicting machining precision of machine tools is proposed based on several previously developed methods for data quality evaluation, model reliance evaluation, and machining precision prediction. Besides, for data preprocess, we propose a Wavelet-based de-noising method to improve the S/N ratio of sensor data. In addition, we base on the stepwise technique to develop an automatic feature selection method that can extract key features related to machining operations in time, frequency, and time-frequency domains, and can reduce the dimension of essential features. Testing results of a 3-axis CNC machine center machining standard workpieces show that the VMS can achieve the performance that the maximum average error of machining-precision conjecture is less than 2 um and the conjecture of 20 machining-precision items can be completed within 3.8 sec.

Industry 4.1 for Wheel Machining Automation

Industry 4.0 is set to be one of the new manufacturing objectives. The technologies involved to achieve Industry 4.0 are Internet of Things (IoT), cyber physical systems (CPS), and cloud manufacturing (CM). However, the current objectives defined by Industry 4.0 do not include zero defects; it only keeps the faith of achieving nearly zero-defects state. The purpose of this paper is to propose a platform denoted advanced manufacturing cloud of things (AMCoT) to not only achieve the objectives of Industry 4.0 but also accomplish the goal of zero defects by applying the technology of automatic virtual metrology (AVM). As such, by applying Industry 4.0 together with AVM to achieve the goal of zero defects, the era of Industry 4.1 is taking place. The application of wheel machining automation is adopted in this letter to illustrate how AMCoT and Industry 4.1 work.

Total precision inspection of machine tools with virtual metrology

The technology of virtual metrology (VM) has been applied in the semiconductor industry to convert sampling inspection with metrology delay into real time and online total inspection. The purpose of this study is trying to apply VM for inspecting machining precision of machine tools. However, machining processes will cause severe vibrations that make process data collection, data cleaning, and feature extraction difficult to handle. Thus, the tasks of how to accurately segment essential parts of the raw process data from the original numerical-control file, how to effectively handle raw process/sensor data with low signal-to-noise ratios, and how to properly extract significant features from the segmented and clean raw process data are challenging issues for successfully applying VM to machine tools. These issues are judiciously addressed and successfully resolved in this paper. Testing results of machining standard workpieces and cellphone shells of two three-axis CNC machines show that the proposed approach of applying VM to accomplish total precision inspection of machine tools is promising.

Automatic virtual metrology for wheel machining automation

Total inspection after wheel machining becomes essential for safety consideration and continuous improvement. However, conducting wheel-by-wheel actual metrology is very expensive and time-consuming. A novel idea is to use virtual metrology (VM) that predicts wheel quality based on process data collected from machine tool with a slight supplement of actual metrology data. The technology of automatic virtual metrology (AVM) has been proposed by the authors and successfully deployed in hi-tech industries, such as semiconductor, display and solar cell. The purpose of this study was to propose an approach to apply the AVM system factory-wide to wheel machining automation (WMA) for achieving total inspection of all the precision items of WMA under mass production environment.

A multi-objective optimization approach for selecting key features of machining processes

This paper presents a multi-objective optimization approach to select key machining features for improving the predictive accuracy of virtual metrology. Along increasing of complicated machining features, supervised optimization methods can be applied to select the significant features; however, these methods are inapplicable when the number of selected features are far greater than the number of training samples for modeling. Based on a novel unsupervised two-stage clustering procedure, this paper proposes a clustering non-dominated sorting genetic algorithm (CNSGA) to minimize objectives of selecting key features, e.g., the feature number and the clustering ratios. According to the selected features, a virtual metrology system was adopted to predict the machining quality of a machining process in a CNC lathe. The results show that precision and robustness of using the features selected by the proposed CNSGA for predicting machining accuracies of wheel rims are better than that of using the other selection approach.

Automatic Virtual Metrology and Target Value Adjustment for Mass Customization

One of the core values of Industry 4.0 targets to integrate peoples demand into manufacturing for enhanced products, systems, and services for a wider variety of increasingly personalized customization of products. Thus, Industry 4.0 advances the traditional manufacturing techniques from mass production toward mass customization (MC). Take wheel machining automation (WMA) as an example. To meet MC expectations, manufacturers should offer customized wheels at a large scale with low cost, short lead-time, and high quality. Thus, WMA cells with MC capability are requested to be designed to have a high degree of quick responsiveness to accurately react on machining-condition changes for manufacturing different wheel types. To meet the requirements of MC production, this work proposes to apply the automatic virtual metrology (AVM) system together with the so-called target-value-adjustment (TVA) scheme. The AVM system was developed by the authors to convert sampling inspections with metrology delay into real-time and on-line total inspection, while the TVA scheme is designed to enhance AVMs adaptive customization capability for automatically and rapidly accomplishing the goals of MC production. To demonstrate the versatility of the AVM-plus-TVA approach, the other example in semiconductor etching process is also illustrated.

Extracting relevant features for diagnosing machine tool faults in cloud architecture

This paper presents a cloud diagnosis architecture to support diagnosis of different machine tool faults with similar abnormal events. Lacking the corresponding features of failure historical data, similar abnormal events are insufficient to be used for identifying the root causes of faults. On the basis of a novel event-oriented process monitoring and backtracking (EOPMB) method and the clustering non-dominated sorting genetic algorithm (CNSGA), this paper proposes a cloud diagnosis architecture for identifying failure causes by extracting relevant features of various faults from different machine tools. Results show that the proposed architecture can assist users in improving diagnosis performance.

A virtual-metrology-based machining state conjecture system

This study proposes a virtual-metrology-based control system for conjecturing machining states and suggesting controller operating modes. The two machining state conjecture models were integrated to validate the virtual metrology results using a dual-stage featuring scheme and a two-phase modeling procedure. The featuring scheme is to extract significant features from collecting data for modeling and to minimize the modeling features by using a genetic algorithm. The modeling procedure adopts two quality indicators to evaluate model effectiveness. Two case studies indicate that the system achieves a mean MAPE of precision estimation less than 8.5% and suggests the controller with operating modes in 1 s.