Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Effect of the relative position of the face milling tooltowards the workpiece on machined surfaceroughness and milling dynamics

In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter's diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given95sem informaçãosem informaçã

A machine-learning based solution for chatter prediction in heavy-dutymilling machines

The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine learning techniques with no need to calculate the two above-mentioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on AISI 1045 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and Random Forest. The results show that Random Forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10 × 10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, Random Forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.Hidrodamp Project (IDI-20110453) of the Centre for Industrial Technological Development (CDTI

Surface roughness evaluation in thin EN AW-6086-T6 alloy plates after face milling process with different strategies

Lightweight alloys made from aluminium are used to manufacture cars, trains and planes. The main parts most often manufactured from thin sheets requiring the use of milling in the manufacturing process are front panels for control systems, housing parts for electrical and electronic components. As a result of the final phase of the manufacturing process, cold rolling, residual stresses remain in the surface layers, which can influence the cutting processes carried out on these materials. The main aim of this study was to verify whether the strategy of removing the outer material layers of aluminium alloy sheets affects the surface roughness after the face milling process. EN AW-6082-T6 aluminium alloy thin plates with three different thicknesses and with two directions relative to the cold rolling process direction (longitudinal and transverse) were analysed. Three different strategies for removing the outer layers of the material by face milling were considered. Noticeable differences in surface roughness 2D and 3D parameters were found among all machining strategies and for both rolling directions, but these differences were not statistically significant. The lowest values of Ra = 0.34 µm were measured for the S#3 strategy, which asymmetrically removed material from both sides of the plate (main and back), for an 8-mm-thick plate in the transverse rolling direction. The highest values of Ra = 0.48 µm were measured for a 6-mm-thick plate milled with the S#2 strategy, which symmetrically removed material from both sides of the plate, in the longitudinal rolling direction. However, the position of the face cutter axis during the machining process was observed to have a significant effect on the surface roughness. A higher surface roughness was measured in the areas of the tool point transition from the up-milling direction to the down-milling direction (tool axis path) for all analysed strategies (Ra = 0.63–0.68 µm). The best values were obtained for the up-milling direction, but in the area of the smooth execution of the process (Ra = 0.26–0.29 µm), not in the area of the blade entry into the material. A similar relationship was obtained for analysed medians of the arithmetic mean height (Sa) and the root-mean-square height (Sq). However, in the case of the S#3 strategy, the spreads of results were the lowest

Application of Audible Signals in Tool Condition Monitoring using Machine Learning Techniques

Machining is always accompanied by many difficulties like tool wear, tool breakage, improper machining conditions, non-uniform workpiece properties and some other irregularities, which are some of major barriers to highly-automated operations. Effective tool condition monitoring (TCM) system provides a best solution to monitor those irregular machining processes and suggest operators to take appropriate actions. Even though a wide variety of monitoring techniques have been developed for the online detection of tool condition, it remains an unsolved problem to look for a reliable, simple and cheap solution. This research work mainly focuses on developing a real-time tool condition monitoring model to detect the tool condition, part quality in machining process by using machine learning techniques through sound monitoring. The present study shows the development of a process model capable of on-line process monitoring utilizing machine learning techniques to analyze the sound signals collected during machining and train the proposed system to predict the cutting phenomenon during machining. A decision-making system based on the machine learning technique involving Support Vector Machine approach is developed. The developed system is trained with pre-processed data and tested, and the system showed a significant prediction accuracy in different applications which proves to be an effective model in applying to machining process as an on-line process monitoring system. In addition, this system also proves to be effective, cheap, compact and sensory position invariant. The successful development of the proposed TCM system can provide a practical tool to reduce downtime for tool changes and minimize the amount of scrap in metal cutting industry

Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via Tool Condition Monitoring System in turning of AISI 5140

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%)

Simulation-based feed rate adaptation considering tool wear condition

The process forces generated in machining are related to a deflection of the milling tool, which results in shape deviations. In addition to process parameters like feed rate, width and depth of cut or cutting speed, the wear condition of the tool has a significant influence on the shape deviation during flank milling. In process planning it is important to take the tool condition and the ideal time for tool change into account when selecting the process parameters. An assistance system is being researched at the Institute of Production Engineering and Machine T ools (IFW) in cooperation with Kennametal Shared Services GmbH to support this task. T he assistance system adjusts automatically the feed rate considering a predefined maximum shape deviation. Additionally, it identifies an optimal moment for tool change. T he advantages of the system are particularly evident in planning of individual milling processes. T he assistance system is based on a combination of a material removal simulation and empirical models of the shape error. For this purpose, spindle currents as well as measured shape errors are stored in a database. T hese data are extended by the actual local cutting conditions calculated by a process-parallel material removal simulation. Afterwards, the data is transferred into process knowledge via a Support Vector Machine (SVM). Within a technological NC simulation before the start of manufacturing, the generated knowledge is applied to predict the shape error of the workpiece and to automatically adjust the feed rate. By adapting the feed rate, it is possible to control the tool life. T he required tool change is defined by specifying a limit for the permitted width of flank wear land. T he presented assistance system enables the prediction of the shape error parallel to the manufacturing process and the automatic determination of the feed rate as well as the ideal time for tool change

Identification and Analysis of Patterns of Machine Learning Systems in the Connected, Adaptive Production

Over the past six decades, many companies have discovered the potential of computer-controlled systems in the manufacturing industry. Overall, digitization can be identified as one of the main drivers of cost reduction in the manufacturing industry. However, recent advances in Artificial Intelligence indicate that there is still untapped potential in the use and analysis of data in industry. Many reports and surveys indicate that machine learning solutions are slowly adapted and that the process of implementation is decelerated by inefficiencies. The goal of this paper is the systematic analysis of successfully implemented machine learning solutions in manufacturing as well as the derivation of a more efficient implementation approach. For this, three use cases have been identified for in-depth analysis and a framework for systematic comparisons between differently implemented solutions is developed. In all three use cases it is possible to derive implementation patterns as well as to identify key variables which determine the success of implementation. The identified patterns show that similar machine learning problems within the same use case can be solved with similar solutions. The results provide a heuristic for future implementation attempts tackling problems of similar nature