A stochastic model for the analysis of a two-machine flexible manufacturing cell

This paper presents a stochastic model to determine the performance of a flexible manufacturing cell (FMC) under variable operational conditions, including random machining times, random loading and unloading times, and random pallet transfer times. The FMC under study consists of two machines, pallet handling system, and a loading/unloading robot. After delivering the blanks by the pallet to the cell, the robot loads the first machine followed by the second. Unloading of a part starts with the machine that finishes its part first, followed by the next machine. When the machining of all parts on the pallet is completed, the handling system moves the pallet with finished parts out and brings in a new pallet with blanks. A model with these characteristics turns out to be a Markov chain with a transition matrix of size 5n+3, where n is the number of parts on the pallet. In this paper, we present exact numerical solutions and economic analysis to evaluate FMC systems, to determine optimal pallet capacity and robot speed that minimize total FMC cost per unit of production.     

A rule-based robot scheduling system for flexible manufacturing cells

In this research we examine a class of flexible manufacturing cells (FMC) containing a robot. The role of the robot is to load parts onto machines, to unload parts from machines, and to transport parts between machines. Since the productivity of an FMC is directly proportional to the level of productive work performed by the robot, the manner in which robots move between machines affects productivity. The problem of finding efficient robot schedules/tours is therefore one of substantial economic significance in the operation of a FMC. Unfortunately, in many practical situations it is difficult to develop efficient robot schedules given the dynamic environments in which they exist. We devise a rule-based system to assist the cell supervisor in making good decisions by dynamically coordinating the available information during the production process. The rule-based system combines an algorithmic procedure to deal with a well-structured environment and a flexible heuristic approach employed to deal with less well-structured environments. Both the algorithmic and heuristic procedures are applied separately, then together, to control the robot's movement in a simulation experiment. We show that there is a predictable tradeoff between the quality of the resulting schedule and the information contents of heuristic used.     

The UniSet approach for the programming of flexible manufacturing cells

Flexible manufacturing cells (FMC) may be considered the most significant development in small-batch manufacturing. Setting-up and operating costs of FMC prove to be the most major hindrance to their large-scale implementation and use, particularly by small and medium size industries. Incompatibilities between the different components constituting the cells and the lack of a unified language/approach to programming and coordinating them are cited as the cause of the complexity of setting up and subsequently operating the cells. In order to eliminate these difficulties, a new philosophy for setting-up, programming and control of FMC has been developed. This paper reports the effort to develop this new unified manufacturing instruction set and its environment, called here “UniSet”, its philosophy and some of the components of the UniSet environment. UniSet has been developed as a non-exclusive unified manufacturing instruction set, based on comparisons of the prevailing machine tool and programming primitives. UniSet allows programmers to deal with only one instruction set, if they so desire, in a single coherent environment, rather than numerous machine programming languages. The software system is coded in an object-oriented programming (OOP) language, Smalltalk, and derives its paradigm from the OO philosophy. Test results are also included to demonstrate the applicability of the approach employed.     

Hierarchical stochastic production planning for the highest business benefit

This paper addresses the hierarchical stochastic production planning (HSPP) problem of flexible automated workshops (FAWs), each with a number of flexible manufacturing systems (FMSs) the part-transfer between which is a delay of a time period. The problem not only includes uncertainties in the demand, capacities, material supply, processing times, necessity for rework, and scrap, but also considers multiple products and multiple time periods. The objective is to develop a production plan which tells each FMS how many parts to produce and when to produce them so as to obtain the highest business benefit. Herein, the HSPP problem is formulated by a stochastic nonlinear programming model whose constraints are linear but whose objective function is piecewise linear. For the convenience of solving the stochastic nonlinear programming model above, it is approximately transformed into a deterministic nonlinear programming model and further into a linear programming model. Because the scale of the model for a general workshop is too large to be solved by the simplex method on a personal computer within acceptable time, Karmarkar's algorithm and an interaction/prediction algorithm, respectively, are used to solve the model, the former for the medium or small scale problems and the latter for the large scale problems. By the implementation of the above-mentioned algorithms and through many HSPP examples, Karmarkar's algorithm, the interaction/prediction algorithm and the linear programming method in Matlab 5.0 are compared, the result of which shows that the proposed approaches are very effective and suitable for not only “push” production but also “pull” production.     

A modular flexible scalable and reconfigurable system for manufacturing of Microsystems based on additive manufacturing and e-printing

Digital manufacturing technologies [1] are gaining more and more importance as key enabling technologies in future manufacturing, especially when a flexible scalable manufacturing of small medium series of customized parts is required. The paper describes a new approach for design manufacturing of complex three dimensional components building on a combination of digital manufacturing technologies such as laminated objects manufacturing, laser and e-printing technologies. The micro component is made up of stacks of functionalized layers of polymer films. The concept is currently developed further in the project SMARTLAM [2], [3], funded by the European Commission. The manufacturing system is based on a flexible, scalable and modular equipment and application features approach which enables the manufacturing of different small size batches without tool or mask making in short time. Different modules can be combined by defined hardware and software interfaces. Avoiding time consumable and difficult programming caused by manufacturing a new conceptual approach a Function-Block Runtime (FORTE) executes generated control application platform-independently and coordinates component module functionalities. The control system is designed to integrate all processes as well as the base platform with features far beyond ordinary PLC systems. One aspect is the use of process data out of the data acquisition system to simulate and optimize the processes. These results are incorporated into the main machine control system. Another aspect is the vision system for flexible quality control and closed-loop positioning control with visual servoing.The paper shows the overall concept of SMARTLAM and exemplarily demonstrates the control system as well as the modular equipment approach by the example of the control system for alignment of different stacks and inspection system.     

Object recognition and pose estimation for industrial applications: A cascade system

The research work presented in this paper focuses on the development of a 3D object localization and recognition system to be used in robotics conveyor coating lines. These requirements were specified together with enterprises with small production series seeking a full robotic automation of their production line that is characterized by a wide range of products in simultaneous manufacturing. Their production process (for example heat or coating/painting treatments) limits the use of conventional identification systems attached to the object in hand. Furthermore, the mechanical structure of the conveyor introduces geometric inaccuracy in the object positioning. With the correct classification and localization of the object, the robot will be able to autonomously select the right program to execute and to perform coordinate system corrections. A cascade system performed with Support Vector Machine and the Perfect Match (point cloud geometric template matching) algorithms was developed for this purpose achieving 99.5% of accuracy. The entire recognition and pose estimation procedure is performed in a maximum time range of 3 s with standard off the shelf hardware. It is expected that this work contributes to the integration of industrial robots in highly dynamic and specialized production lines.     

A methodology for implementation of mobile robot in adaptive manufacturing environments

With the rapid development of technologies, many production systems and modes has been advanced with respect to manufacturing, management and information fields. The paper deals with the problem of the implementation of an autonomous industrial mobile robot in real-world industrial applications in which all these fields are considered, namely mobile robot technology, planning and scheduling and communication. A methodology for implementation consisting of: a mobile robot system design (Little Helper prototype), an appropriate industrial application (multiple-part feeding), an implementation concept for the industrial application (the Bartender Concept), a mathematical model and a genetic algorithm-based heuristic is proposed. Furthermore, in order for the mobile robot to work properly in a flexible (cloud-based) manufacturing environment, the communications and exchange of data between the mobile robot with other manufacturing systems and shop-floor operators are addressed in the methodology. The proposed methodology provides insight into how mobile robot technology and abilities contribute to cloud manufacturing systems. A real-world demonstration at an impeller production line in a factory and computational experiments are conducted to demonstrate the effectiveness of the proposed methodology.     

An architecture for universal construction via modular robotic components

A set of modular components is presented for use in reconfigurable robotic construction systems. The set includes passive and active components. The passive components can be formed into static structures and adaptable grids carrying electrical power and signals. Passive and active components can be combined into general purpose mobile manipulators which are able to augment and reconfigure the grid, construct new manipulators, and potentially perform general purpose fabrication tasks such as additive manufacturing. The components themselves are designed for low-cost, simple fabrication methods and could potentially be fabricated by constructors made of the same components. This work represents a step toward a Cyclic Fabrication System, a network of materials, tools, and manufacturing processes that can produce all of its constituent components. These and similar systems have been proposed for a wide range of far-term applications, including space-based manufacturing, construction of large-scale industrial facilities, and also for driving development of low-cost 3D printing machines.     

柔性机械臂运动轨迹的鲁棒自适应控制

本文针对多连杆柔性机械臂的运动轨迹问题，讨论了动力学建模，控制系统结构设计以及鲁棒自适应控制法，运用假设模记方法得到了柔性机械臂动力学所似方程，通过对柔性机械臂动力学特性分析，建立了等价动力学模型，依此提出了一种鲁棒自适应控制算法，并给出仿真研究结果。     

Transformative CAD based industrial robot program generation

Industrial robots are widely used in various processes of surface manufacturing, such as spray painting, spray forming, rapid tooling, spray coating, and polishing. Robot programming for these applications is still time consuming and costly. Typical teaching methods are not cost effective and efficient. There are many off-line programming methods developed to reduce the robot programming effort. However, these methods suffer many practical issues, such as cable/hose tangling, robot configuration, collision, and reachability. To solve these problems, this paper discusses a new method to generate robot programs. Since industrial robots have been used in production for decades, there are many robot programs for different parts generated by the robot programmers. These robot programs, which contain not only the robot paths, but also the programmers' knowledge and process parameters, can be transformed to generate new robot programs for similar parts. In this paper, a transformative robot program generation method is developed based on the existing ones in the database. Experiments were performed to validate the developed methodology. The results are very promising in reducing the programming efforts in surface manufacturing.     

Study on Robot Sequencing Model in Flexible Manufacturing System Using Timed Petri Net

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Stochastic models for performance analysis of multistate flexible manufacturing cells

Performance analysis of flexible manufacturing cells (FMCs) can help companies find the pros and cons of production processes. However, the emphasis has been on issues like cell formation, layout design and scheduling optimization. Little seems to have been done to assess the reliability of an FMC. In this paper, we develop the stochastic models for the performance analysis mainly on the reliability of two different FMCs configured from a set of teaching intelligent flexible manufacturing system (TIFMS). The closed form solutions of probabilities of system states are obtained. Then, utilization rate of equipment in the cell and productivities of the two FMCs as the performance indexes are calculated and optimized. Compared to simulation methods, the closed form solutions make calculations of the performance indexes faster and more accurate. When random variables in the stochastic models are assumed to follow non-exponential distributions, the effects of them on the performance indexes are discussed. The objective of this paper is to fill up the gap that the closed form solutions are difficult to obtain as the number of machine tool increases. Another objective is to optimize the performance indexes to help engineers better evaluate the performance of FMC. Numerical analysis cases are used to illustrate the proposed stochastic models.     

Augmented reality-assisted robot programming system for industrial applications

Robots are important in high-mix low-volume manufacturing because of their versatility and repeatability in performing manufacturing tasks. However, robots have not been widely used due to cumbersome programming effort and lack of operator skill. One significant factor prohibiting the widespread application of robots by small and medium enterprises (SMEs) is the high cost and necessary skill of programming and re-programming robots to perform diverse tasks. This paper discusses an Augmented Reality (AR) assisted robot programming system (ARRPS) that provides faster and more intuitive robot programming than conventional techniques. ARRPS is designed to allow users with little robot programming knowledge to program tasks for a serial robot. The system transforms the work cell of a serial industrial robot into an AR environment. With an AR user interface and a handheld pointer for interaction, users are free to move around the work cell to define 3D points and paths for the real robot to follow. Sensor data and algorithms are used for robot motion planning, collision detection and plan validation. The proposed approach enables fast and intuitive robotic path and task programming, and allows users to focus only on the definition of tasks. The implementation of this AR-assisted robot system is presented, and specific methods to enhance the performance of the users in carrying out robot programming using this system are highlighted.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems

This paper presents a constraint programming (CP) methodology to deal with the scheduling of flexible manufacturing systems (FMSs). The proposed approach, which consists of both a model and a search strategy, handles several features found in industrial environments, such as limitations on number of tools in the system, lifetime of tools, as well as tool magazine capacity of machines. In addition, it tackles the problem in a integrated way by considering tool planning and allocation, machine assignment, part routing, and task timing decisions altogether in the approach. The formulation, which is able to take into account a variety of objective functions, has been successfully applied to the solution of test problems of various sizes and degrees of difficulty.     

An Attempt to Raise the Level of Software Abstraction in Assembly Robotics through an Apposite Choice of Underlying Mechatronics

Robot manipulators were meant to be the production engineer"s flexible friend. Assembly robots, however, have failed to fulfill their promise. The problem that has continuously plagued robotic assembly is that of spatial uncertainty. It is our thesis that the ubiquitous problem of spatial uncertainty is an artefact of the fact that current industrial manipulators are designed for an operational paradigm that assumes position control is of primary importance. In this paper we propound an alternative approach based on sliding as the primary motion primitive. We first present a model that uses sliding to allow us to raise the level of abstraction of robot programming tasks. We then describe an inherently accommodating, (planar) three degree of freedom, direct-drive robot arm that was constructed to test our approach. Finally, we present data collected from representative (planar) manipulation tasks that substantiate our claims.     

Proactive human–robot collaboration: Mutual-cognitive,predictable, and self-organising perspectives

Human–Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human-centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human–robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human–robot symbiotic relation is evolving with a 5C intelligence — from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self-organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others’ operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human–robot flexible production.     

Design and construction of a variable-aperture gripper for flexible automated assembly

The growing market demand for a wide variety of product models and small batch production makes flexible robotized production systems an emerging need in industry. Today, in manufacturing applications, general purpose grippers are not very considered, and robot end effectors are properly designed for the specific task with a strongly limited versatility. Flexibility is thus usually obtained by using a different tool for each family of parts: a tool changing system allows the robot to rapidly replace the tool on the end effector; tools are stored in a tool magazine allocated in the workcell. However, such systems are expensive and their use can affect the working cycle-time. This paper presents the design and testing of a variable-aperture, cost-effective gripper, capable of adapting its aperture (grasp width) to different handling demands, without affecting the working-cycle time of the production system. The solution proposed consists of (1) an electrically-actuated mechanism, which allows it to satisfy flexibility requirements, by regulating the aperture in hidden time; (2) a pneumatically-actuated mechanism to achieve high performance in open/close operations. Simulations and preliminary tests showed that this type of design can be a suitable solution to increase flexibility in robotized workcells without increasing the cycle time.     

Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS

Due to the global competition in manufacturing environment, firms are forced to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems (FMSs) offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. Some of the important planning problems that need realistic modelling and quicker solution especially in automated manufacturing systems have assumed greater significance in the recent past. The language used by the industrial workers is fuzzy in nature, which results in failure of the models considering deterministic situations. The situation in the real life shop floor demands to adopt fuzzy-based multi-objective goals to express the target set by the management. This paper presents a fuzzy goal programming approach to model the machine tool selection and operation allocation problem of FMS. An ant colony optimization (ACO)-based approach is applied to optimize the model and the results of the computational experiments are reported.