Intelligent Machining Systems

Machining is one of the most widespread manufacturing processes and plays a critical role in industries. As a matter of fact, machine tools are often called mother machines as they are used to produce other machines and production plants. The continuous development of innovative materials and the increasing competitiveness are two of the challenges that nowadays manufacturing industries have to cope with. The increasing attention to environmental issues and the rising costs of raw materials drive the development of machining systems able to continuously monitor the ongoing process, identify eventual arising problems and adopt appropriate countermeasures to resolve or prevent these issues, leading to an overall optimization of the process. This work presents the development of intelligent machining systems based on in-process monitoring which can be implemented on production machines in order to enhance their performances. Therefore, some cases of monitoring systems developed in different fields, and for different applications, are presented in order to demonstrate the functions which can be enabled by the adoption of these systems. Design and realization of an advanced experimental machining testbed is presented in order to give an example of a machine tool retrofit aimed to enable advanced monitoring and control solutions. Finally, the implementation of a data-driven simulation of the machining process is presented. The modelling and simulation phases are presented and discussed. So, the model is applied to data collected during an experimental campaign in order to tune it. The opportunities enabled by integrating monitoring systems with simulation are presented with preliminary studies on the development of two virtual sensors for the material conformance and cutting parameter estimation during machining processes

Reliability Analysis of On-Demand High-Speed Machining

Current trends in high-speed machining aim to increase manufacturing efficiency by maximizing material removal rates and minimizing part cycle times. This project explores three related technologies and presents a system design for rapid production of custom machined parts. First a reliability analysis in high-speed machining of thin wall features is put forth with experimental results. Second an implementation of on-demand manufacturing is presented with emphasis on flexibility and automation. Finally innovative manufacturing cell design is used to drive costs down by optimizing material and information flow. The resulting high-speed on-demand machining cell design employs effective techniques to reduce production time, meet changing customer needs, and drive down costs

Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data

CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the de-noised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments

Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution

The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds

Manufacturing Technology Today

Manufacturing Technology Today, Manufacturing Technology Abstracts, Vol. 14, No. 4, September 2015, Bangalore, India

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the de-noised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments

Predicting chattering alarms: A machine Learning approach

Abstract Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models – Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models

An Early Warning Monitoring System for CNC Spindle Bearing Failure

Equipment employed in a manufacturing environment must be able to operate as long as possible having as little downtime as possible. Therefore, maintenance is crucial in order to allow for the equipment to perform its designated tasks without failure, especially on critical systems. In a CNC machine, if the spindle fails, the machine is useless. Having the ability to detect spindle degradation to the point where a replacement spindle installation can be planned, via condition monitoring, is invaluable to a manufacturer who utilizes these types of machines. An early warning monitoring system for CNC spindle bearing failure has been developed to be utilized directly on a CNC machine\u27s controller employing an open architecture structure. The main system uses an ultrasonic sensor as its primary sensing component and provides a singular value as to the spindle condition. The system allows for both real time data recording as well as provides a trending history for the machine. Additionally, the system allows for the data to be seen remotely via the internet. Accessory devices can be added to perform an in-depth bearing failure analysis. The total system (including accessories) costs just under $2,400, allowing for a very effective system at a very low price. A few thousand dollars towards a predictive and preventive maintenance monitoring solution can prevent tens-of-thousands of dollars in lost production and unnecessary maintenance costs if the system is utilized as intended. System performance was tested to investigate sensor measurement applicability. Spindle speed was found to have an effect on the sensor\u27s output, however excessive vibration did not. Therefore, the same spindle speed must be used each time a measurement is taken. Measurements while the machine is cutting can be performed, however, a test mode is recommended for the most accurate results. The amount of variation for an in-process reading was found to be lower for a harder material (ie: steel vs. aluminum), for the same spindle speed and depth of cut. The system was tested to see if it could detect the various stages of bearing failure. It was unable to detect a plastic/resin bearing cage degradation failure until it was too late as the failure was too quiet to detect