Robotic grinding is considered as an alternative towards the efficient and intelligent machining of complex components by virtue of its flexibility, intelligence and cost efficiency, particularly in comparison with the current mainstream manufacturing modes. The advances in robotic grinding during the past one to two decades present two extremes: one aims to solve the problem of precision machining of small-scale complex surfaces, the other emphasizes on the efficient machining of large-scale complex structures. To achieve efficient and intelligent grinding of these two different types of complex components, researchers have attempted to conquer key technologies and develop relevant machining system. The aim of this paper is to present a systematic, critical, and comprehensively review of all aspects of robotic grinding of complex components, especially focusing on three research objectives. For the first research objective, the problems and challenges arising out of robotic grinding of complex components are identified from three aspects of accuracy control, compliance control and cooperative control, and their impact on the machined workpiece geometrical accuracy, surface integrity and machining efficiency are also identified. For the second aim of this review, the relevant research work in the field of robotic grinding till the date are organized, and the various strategies and alternative solutions to overcome the challenges are provided. The research perspectives are concentrated primarily on the high-precision online measurement, grinding allowance control, constant contact force control, and surface integrity from robotic grinding, thereby potentially constructing the integration of “measurement – manipulation – machining” for the robotic grinding system. For the third objective, typical applications of this research work to implement successful robotic grinding of turbine blades and large-scale complex structures are discussed. Some research interests for future work to promote robotic grinding of complex components towards more intelligent and efficient in practical applications are also suggested.

Robotic grinding of complex components: A step towards efficient and intelligent machining – challenges, solutions, and applications

https://dr.ntu.edu.sg/bitstream/10356/105905/2/In-Process%20Tool%20Condition%20Monitoring%20in%20Compliant%20Abrasive%20Belt%20Grinding%20Process%20Using%20Support%20Vector%20Machines%20and%20Genetic%20Algorithm.pdf

In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm

A shear-thickening polishing (STP) method utilizing the shear thickening mechanism of non-Newtonian power-law fluid based slurry is proposed for curved surface polishing. The STP principle and micro-material removal action are analyzed. The high-performance STP slurry with the shear-thickening rheological behaviors has been prepared. To achieve the material removal mechanism of STP process, based on the Preston formula, fluid dynamics and shear thickening mechanism, the material removal rate (MRR) model is established and the difference of MRR between theoretical and experimental results is 6.12%. The experimental and theoretical tests of STP process are conducted to investigate the influences of polishing velocity, abrasive concentration and grain size on MRR and surface roughness. Compared with Newtonian fluid slurry, STP slurry can achieve much higher MRR and better surface quality due to shear-thickening effect. MRR of Cr12Mo1V1 (die steel) is up to 13.69 μm/h, and surface roughness is reduced from Ra 105.95 nm to Ra 5.1 nm within 0.5 h of processing. This indicates that STP is a promising processing method for precision finishing or polishing.

Shear-thickening polishing method

A local process model to estimate the material removal rate in robotic belt grinding is presented and applied to the process simulation system. It calculate the acting force by incorporating the local geometry information of the workpiece instead of the cutting depth parameter with only one certain value as in a global grinding model. The simulation accuracy can be improved to below 5% even for a non-uniform contact under stable cutting conditions.

A local process model for simulation of robotic belt grinding

Because of the special characteristics of aero-engine precision-forged blade edges such as heat-resistant and high-strength materials, the unequal distribution and small removal allowance, and easy deformation in processing, edge profile shape errors are easily caused by using conventional processing methods. These errors include chamfered, sharp, flat, and obtuse edges and cervical part shrinkage, so the real-R shape of the edge is difficult to guarantee, and the aero-engine air dynamic performance is seriously affected. The purpose of this research is to improve the dimensional precision, profile shape errors, and surface quality of the real-R edge of the precision-forged blade. With these aims, the method of equivalent self-adaptive belt grinding (ESBG) using a cubic boron nitride (CBN) belt is developed in this study. First, the grinding characteristics and process planning to the blade real-R edge are analyzed. The methodologies of equivalent belt grinding (EBG) and ESBG using a CBN belt are then illustrated, the control equation for EBG is established, and the method of force adaptive control is introduced. Finally, the ESBG method using the CBN belt is verified by experimental investigation of the aero-engine precision-forged blade and comparison with manual and EBG methods. The experimental results showed that the ESBG method using the CBN belt achieved the requirements of the aero-engine precision-forged blade real-R edge. It was verified that the method of ESBG using the CBN belt has the advantages of real-R shape dimensional precision and surface quality for the blade edge compared with the conventional method.

Equivalent self-adaptive belt grinding for the real-R edge of an aero-engine precision-forged blade

Industrial robots are recently introduced to the belt grinding of free-form surfaces to obtain high productive efficiency and constant surface quality. The simulation of belt grinding process can facilitate planning grinding paths and writing robotic programs before manufacturing. In simulation, it is crucial to get the force distribution in the contact area between the workpiece and the elastic contact wheel because the uneven distributed local forces are the main reason to the unequal local removals on the grated surface. The traditional way is to simplify this contact problem as a Signorini contact problem and use the finite element method (FEM) to calculate the force distribution. However, the FEM model is too computationally expensive to meet the real-time requirement. A new model based on support vector regression (SVR) technique is developed in this paper to calculate the force distribution instead of the FEM model. The new model approximates the FEM model with an error smaller than 5%, but executes much faster (1 s vs 15 min by FEM). With this new model, the real-time simulation and even the on-line robot control of grinding processes can be further conducted.

An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding

A bottleneck in the real-time simulation of belt grinding processes is the calculation of the force distribution between the workpiece and the grinding wheel, which can be simplified by the Signorini contact problem. The Finite Element Method (FEM) is the conventional way of solving such a contact problem, but too computationally expensive to meet the real-time requirement. This paper demonstrates a new approach to model the Signorini contact problem based on learning. This new model approximates the FEM model so that it is not necessary to execute optimization for each contact in run time; hence the calculation time is dramatically reduced.

/pdf/an-intelligent-model-for-the-signorini-contact-problem-in-2j2dtgdw1i.pdf

An intelligent model for the signorini contact problem in belt grinding processes

An intelligent model for the Signorini contact problem in belt grinding processes

It is crucial to calculate the Signorini contact problem at high speed in real-time simulation of the belt grinding process. Finite Element Method (FEM) is the traditional way to solve such a contact problem. However, FEM is too time-consuming to fulfill the real-time simulation requirement. This paper demonstrates a new approach to model the Signorini contact problem based on learning. This new model averts doing the expensive optimization problem for each contact by FEM; hence dramatically reduces the calculating time. Multi-Layers Neurons (MLN) and Support Vector Regression (SVR) are adopted as a learning machine. The testing errors of these two learning machines are also compared. SVR showed superior performance than MLN in this application.

Bernd Kuhlenkötter

Papers

An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding

An intelligent model for the signorini contact problem in belt grinding processes

An intelligent model for the Signorini contact problem in belt grinding processes

A Learning-Based Contact Model in Belt-Grinding Processes