Distributed Analytics Framework for Integrating Brownfield Systems to Establish Intelligent Manufacturing Architecture

Intelligent manufacturing otherwise called as smart manufacturing concentrates upon optimising production and processes by making full use of data available. It is regarded as a new manufacturing model where the entire product life cycle can be simplified using various smart sensors, data-driven decision-making models, visualisation, intelligent devices, and data analytics. In the Industry 4.0 era, Industrial Internet of Things (IIoT) architecture platform is required to streamline and secure data transfer between machines, factories, etc. When certain manufacturing industry is equipped with this platform, an intelligent manufacturing model can be achieved. In today’s factories, most machines are brownfield systems and are not connected to any IoT platforms. Thus they cannot provide data or visibility into their performance, health, and optimal maintenance schedules, which would have improved their operational value. This paper attempts to bridge this gap by demonstrating how brownfield equipment can be IIoT enabled and how data analytics can be performed at the edge as well as cloud using two simple use cases involving industrial robot on the abrasive finishing process. The focus of this paper is on how a scalable data analytics architecture can be built for brownfield machines at the edge as well as the cloud

Robotic Grinding Process of Turboprop Engine Compressor Blades with Active Selection of Contact Force

The work presents a robotic system for grinding the blades of a turboprop engine compressor. The proprietary conceptual solution includes a data acquisition system based on a robotic 3D scanner, a neural decision system and a robot performing a grinding process with force control. The contact force of the tool to the blade was assumed as a variable and controlled process parameter. A neural network was used to generate the contact force on the basis of measured machining allowances on the blade. A virtual grid of several dozen regularly spaced points was placed on the surface of the blade. The neural network was learned the allowance-force dependence for the selected points, making it possible to select the proper contact force on the surface to be machined. The developed algorithm for the process of robotic grinding of the blades takes into account the necessity of ongoing quality control of the processing and the introduction of corrections in the process

Development of an integrated robotic polishing system

This thesis presents research carried out as part of a project undertaken in fulfilment of the requirements of Loughborough University for the award of Philosophical Doctorate. The main focus of this research is to investigate and develop an appropriate level of automation to the existing manual finishing operations of small metallic components to achieve required surface quality and to remove superficial defects. In the manufacturing industries, polishing processes play a vital role in the development of high precision products, to give a desired surface finish, remove defects, break sharp edges, extend the working life cycle, and meet mechanical specification. The polishing operation is generally done at the final stage of the manufacturing process and can represent up to a third of the production time. Despite the growth automated technology in industry, polishing processes are still mainly carried out manually, due to the complexity and constraints of the process. Manual polishing involves a highly qualified worker polishing the workpiece by hand. These processes are very labour intensive, highly skill dependent, costly, error-prone, environmentally hazardous due to abrasive dust, and - in some cases - inefficient with long process times. In addition, the quality of the finishing is dependent on the training, experience, fatigue, physical ability, and expertise of the operator. Therefore, industries are seeking alternative solutions to be implemented within their current processes. These solutions are mainly aimed at replacing the human operator to improve the health and safety of their workforce and improve their competitiveness. Some automated solutions have already been proposed to assist or replace manual polishing processes. These solutions provide limited capabilities for specific processes or components, and a lack of flexibility and dexterity. One of the reasons for their lack of success is identified as neglecting the study and implementing the manual operations. This research initially hypothesised that for an effective development, an automated polishing system should be designed based on the manual polishing operations. Therefore, a successful implementation of an automated polishing system requires a thorough understanding of the polishing process and their operational parameters. This study began by collaborating with an industrial polishing company. The research was focused on polishing complex small components, similar to the parts typically used in the aerospace industry. The high level business processes of the polishing company were capture through several visits to the site. The low level operational parameters and the understanding of the manual operations were also captured through development of a devices that was used by the expert operators. A number of sensors were embedded to the device to facilitate recording the manual operations. For instance, the device captured the force applied by the operator (avg. 10 N) and the cycle time (e.g. 1 pass every 5 sec.). The capture data was then interpreted to manual techniques and polishing approaches that were used in developing a proof-of-concept Integrated Robotic Polishing System (IRPS). The IRPS was tested successfully through several laboratory based experiments by expert operators. The experiment results proved the capability of the proposed system in polishing a variety of part profiles, without pre-existing geometrical information about the parts. One of the main contributions made by this research is to propose a novel approach for automated polishing operations. The development of an integrated robotic polishing system, based on the research findings, uses a set of smart sensors and a force-position-by-increment control algorithm, and transpose the way that skilled workers carry out polishing processes

Geometrical Error Analysis and Correction in Robotic Grinding

The use of robots in industrial applications has been widespread in the manufacturing tasks such as welding, finishing, polishing and grinding. Most robotic grinding focus on the surface finish rather than accuracy and precision. Therefore, it is important to advance the technology of robotic machining so that more practical and competitive systems can be developed for components that have accuracy and precision requirement. This thesis focuses on improving the level of accuracy in robotic grinding which is a significant challenge in robotic applications because of the kinematic accuracy of the robot movement which is much more complex than normal CNC machine tools. Therefore, aiming to improve the robot accuracy, this work provides a novel method to define the geometrical error by using the cutting tool as a probe whilst using Acoustic Emission monitoring to modify robot commands and to detect surfaces of the workpiece. The work also includes an applicable mathematical model for compensating machining errors in relation to its geometrical position as well as applying an optimum grinding method to motivate the need of eliminating the residual error when performing abrasive grinding using the robot. The work has demonstrated an improved machining precision level from 50µm to 30µm which is controlled by considering the process influential variables, such as depth of cut, wheel speed, feed speed, dressing condition and system time constant. The recorded data and associated error reduction provide a significant evidence to support the viability of implementing a robotic system for various grinding applications, combining more quality and critical surface finishing practices, and an increased focus on the size and form of generated components. This method could provide more flexibility to help designers and manufacturers to control the final accuracy for machining a product using a robot system

Advanced Techniques and Efficiency Assessment of Mechanical Processing

Mechanical processing is just one step in the value chain of metal production, but to some exten,t it determines an effectiveness of separation through suitable preparation of the raw material for beneficiation processes through production of required particle sze composition and useful mineral liberation. The issue is mostly related to techniques of comminution and size classification, but it also concerns methods of gravity separation, as well as modeling and optimization. Technological and economic assessment supplements the issue

Tuning of Parameters for Robotic Contouring Based on the Evaluation of Force Deviation

The application of industrial robots with advanced sensor systems in unstructured environments is continuously becoming wider. A widely used type of advanced sensor systems is the force-torque sensor. Force-torque sensors are typically used for applications such as robot grinding, sanding, polishing, and deburring, where a constant force is exerted upon a workpiece. In this research, control parameters for exerting a constant force along a predefined path are evaluated in laboratory conditions. The experimental setup with the contouring force feedback is composed of a Fanuc LRMate six-degree-of-freedom industrial robot with an integrated force-torque sensor. Control parameters of the Contouring function within the Fanuc robot controller are tuned in four contouring experiments. The experiments conducted in this research are: i) flat beam, ii) flat beam with a rigid support, iii) wave shaped compliant plate, and iv) compliant flat plate. During the experiments, contouring parameters were altered in order to collect the feedback on the values of the force to be used for the evaluation of the force deviation. A fitness function for the evaluation of the force deviation and the tuning of the control parameters is presented. The fitness function enables a selection of initial control parameters which minimize the force deviation during the robot contouring process

Modeling of Surface Roughness in Honing Processes by Using Fuzzy Artificial Neural Networks

Honing processes are abrasive machining processes which are commonly employed to improve the surface of manufactured parts such as hydraulic or combustion engine cylinders. These processes can be employed to obtain a cross-hatched pattern on the internal surfaces of cylinders. In this present study, fuzzy artificial neural networks are employed for modeling surface roughness parameters obtained in finishing honing operations. As a general trend, main factors influencing roughness parameters are grain size and pressure. Mean spacing between profile peaks at the mean line parameter, on the contrary, depends mainly on tangential and linear velocity. Grain Size of 30 and pressure of 600 N/cm2 lead to the highest values of core roughness (Rk) and reduced valley depth (Rvk), which were 1.741 µm and 0.884 µm, respectively. On the other hand, the maximum peak-to-valley roughness parameter (Rz) so obtained was 4.44 µm, which is close to the maximum value of 4.47 µm. On the other hand, values of the grain size equal to 14 and density equal to 20, along with pressure 600 N/cm2 and both tangential and linear speed of 20 m/min and 40 m/min, respectively, lead to the minimum values of core roughness, reduced peak height (Rpk), reduced valley depth and maximum peak-to-valley height of the profile within a sampling length, which were, respectively, 0.141 µm, 0.065 µm, 0.142 µm, and 0.584 µm.Peer ReviewedPostprint (published version