Bojan Babić

Survey paper: A review of automated feature recognition with rule-based pattern recognition

Automated feature recognition (AFR) has provided the greatest contribution to fully automated CAPP system development. The objective of this paper is to review various approaches for solving three major AFR problems: (i) extraction of geometric primitives from a CAD model; (ii) defining a suitable part representation for form feature identification; and (iii) feature pattern matching/recognition. A novel, detailed classification of developed AFR systems has been introduced. This paper also provides a thorough investigation of methods for geometric feature extraction, emphasizing STEP standard application and, finally, a review of recent research reports in the field of AFR with rule-based feature pattern recognition. We discuss potentials and limitations of these approaches and emphasize directions for further research work.

Axiomatic design of flexible manufacturing systems

One of the major requirements of agile manufacturing strategies for the 21st century is to introduce intelligent information technology into manufacturing. The main contribution of this paper is an innovative new FMS design methodology. The operation-driven FMS design methodology is introduced, as the concept of concurrent product and process development is being widely used to enhance the productivity and quality of manufacturing. The new FMS design theory is developed from axiomatic design theory established by Suh (1990). The general design theory based on two design axioms and a set of theorems and corollaries is concretized for the FMS design. The proposed methodology works well as an effective decision support system for FMS designers in determining the appropriate FMS configuration at the design stage. This is illustrated via a successful implementation exercise resulting in configuring FMS in real manufacturing settings.

Neural network Reinforcement Learning for visual control of robot manipulators

It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.

Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems

Abstract Feature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.

PHYSICAL PROPERTIES OF CONTACT LENSES CHARACTERIZED BY SCANNING PROBE MICROSCOPY AND OPTOMAGNETIC FINGERPRINT

In this paper we present applied physics research results of gas-permeable contact lenses (CL) that are manufactured from fluorosilicone acrylate based material (Boston™ type). During contact lenses production the conformation states of polymers belonging to near surface layers of CL surface are changed. Since CL quality crucially depends on surface roughness and optical properties, the properties of surface molecules conformation state and their orientation come into perspective as important factors acting on the molecular level. Therefore, we investigated CL surface by phase contrast atomic force microscopy (PC-AFM), magnetic force microscopy (MFM), and optomagnetic fingerprint (OMF) technique and found out that surface quality and magnetic properties of contact lenses have influence on physical properties of light transmission and that these changes can be detected on the nanolevel of magnetism, as well as optomagnetism. These results carry important biophysically based implications for CL industry, biomedical application industry and applied optical science.

The Ant Lion Optimization Algorithm for Integrated Process Planning and Scheduling

Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper presents implementation of novel nature-inspired Ant Lion Optimization (ALO) algorithm for solving this combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behavior of antlions in hunting ants, the main steps of hunting prey, its mathematical modeling, and optimization procedure for integration of process planning and scheduling is proposed. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem.

Integrated Process Planning and Scheduling Using Multi-Agent Methodology

Market growth and mass customization cause a need for a change in traditional manufacturing. Decentralized decision making and integration of process planning is necessary in order to become concurrent in the market. The paper presents decentralized decision making methodology using multi-agent systems. The model is used for integrated process planning and scheduling based on the minimum processing time under dynamic change of the environment. Two types of disturbance are used to represent the change: part arrival and machine breakdown. The proposed model comprises part agent, job agent, machine agent and optimization agent. Comparative analysis is conducted using simulation in AnyLogic software in order to verify the proposed approach.

Organizational culture, quality improvement tools and methodologies, and business performance of a supply chain

Unlike previous studies that have revealed a link between quality improvement programs and organizational culture typologies in individual companies, this study describes organizational culture dimensions that affect the use of quality improvement tools and methodologies and how both affect supply chain company performance. Structural equation modeling methods are applied to a sample of 200 organizations in the supply chain of a Canadian multinational company. The results show that employee promotion and investment constitutes the most influential cultural dimension. Organizational objectives and an employee reward system individually affect Kaizen. When the level of formalization in an organization is high, Kaizen and total quality management tools are used more intensively. When the level of formalization is low, lean manufacturing and internal audits are used more intensively. Superior communication in an organization causes plan–do–check–act approaches, lean manufacturing methods, corrective actions and internal audits to be used less intensively. Generally speaking, most quality improvement tools and methodologies positively influence business performance. These results suggest that organizations can improve business performance levels by selecting appropriate quality improvement programs depending on existing organizational culture dimensions and may thereby develop an organizational culture that enables successful quality improvements in a supply chain context.

A hierarchical model of distributed simulation of manufacturing systems

Concept of hierarchical distributed simulation system is established and realized as a prototype software tool. HDSS is developed as an open architecture. This structure makes possible to introduce different external simulation resources, and some of them may be physical models controlled by computer. As the simulation controller has the structure of real manufacturing system controller it can be easily converted in a real controller.

Towards an intelligent system for FMS design

The paper presents the concept of an intelligent system performing creative FMS design. Development of the intelligent system for FMS design is based on the application of two theories: design theory and theory of reasoning by analogy. Described general design process is based on design axioms. In accordance with axiomatic design concept four levels of intelligent system for FMS design are defined: functional requirements (FRs) definition level, design parameters (DPs) creation level, the level for analysis of design solution, and the level for checking of the final solution. For each level a particular knowledge processing module is defined. Computer software dealing with each one of four levels is also discussed.