Design of a reconfigurable prognostics platform for machine tools

For decades, researchers and practitioners have been trying to develop and deploy prognostics technologies with ad hoc and trial-and-error approaches. These efforts have resulted in limited success, due to the fact that it lacks a systematic approach and platform in deploying the right prognostics tools for the right applications. This paper introduces a methodology for designing a reconfigurable prognostics platform (RPP) which can be easily and effectively used to assess and predict the performance of machine tools. RPP can be installed on the equipment and it has the prognostic capabilities to convert the data into performance-related information. The equipment performance information can then be integrated into the enterprise asset management system for maintenance decision making through the Internet. Two industrial cases are used to validate the effectiveness of applying the RPP for different prognostic applications as well as the reconfigurable capabilities of the proposed RPP.     

A hybrid fuzzy MCDM approach to machine tool selection

The selection of the appropriate machine tools for a manufacturing company is one of the important points to achieving high competitiveness in the market. Besides, an appropriate choice of machine tools is very important as it helps to realize full production quickly. Today’s market offers many more choices for machine tool alternatives. There are also many factors one should consider as part of the appropriate machine tool selection process, including productivity, flexibility, compatibility, safety, cost, etc. Consequently evaluation procedures involve several objectives and it is often necessary to compromise among possibly conflicting tangible and intangible factors. For these reasons, multiple criteria decision making (MCDM) has been found to be a useful approach to solve this kind of problem. Most of the MCDM models are basically mathematical and ignore qualitative and often subjective considerations. The use of fuzzy set theory allows incorporating qualitative and partially known information into the decision model. This paper describes a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based methodology for evaluation and selection of vertical CNC machining centers for a manufacturing company in Istanbul, Turkey. The criteria weights are calculated by using the fuzzy AHP (analytical hierarchy process).     

Error compensation in machine tools: a neural network approach

For a non-idealized machine tool, each point in the workspace is associated with a tool point positioning error vector. If this error map can be determined, then it is possible to substantially improve the positioning performance of the machine by introducing suitable compensation into the control loop. This paper explores the possibility of using an artifical neural network (ANN) to compute this mapping. The training set for the ANN is obtained by mounting a physical artifact whose dimensions are precisely known in the machine's workspace. The machine, equipped with a touch trigger probe, measures the positions of features on the artifact. The difference between the machine reading and the known dimension is the machine error at that point in the workspace. Using standard modeling techniques, the kinematic error model for a CNC turning center was developed. This model was parameterized by measurement of the parametric error functions using a laser interferometer, electronic levels and a precision square. The kinematic model was then used to simulate the artifact-measuring process and develop the ANN training set. The effect of changing artifact geometry was explored and a machining operation was simulated using the ANN output to provide compensation. The results show that the ANN is capable of learning the error map of a real machine, and that ANN-based compensation can significantly reduce part-dimensional errors.     

Modular machine tools: Design and barriers to industrial implementation

The Reconfigurable Manufacturing System (RMS) paradigm has been developed to address challenges in the design of manufacturing systems and equipment that will meet the demands of modern manufacturing. This research involved the development of Modular Reconfigurable Machines (MRMs); as an emerging technology in reconfigurable manufacturing. MRMs are mechanically modular machines. The modularity permits the kinematic architecture and processing functions of the machine to be reconfigured to meet changing production requirements. This paper will focus on aspects of the mechanical design and the development of a control system that supported the modularity and reconfigurability of the mechanical platform. A modular electronic system is presented that is characterized by a plug and play approach to control scalability. This is complemented by a software architecture that has been developed with a focus on hardware abstraction for the management of an augmented mechanical and electronic architecture. The implications of MRMs for RMSs are discussed and key inhibitors to industrial implementation are identified.     

An approach to regulating machine sharing in reconfigurable back-end semiconductor manufacturing

By complying with the operational philosophy of virtual production lines, a back-end semiconductor manufacturing system can be controlled and managed with better reconfigurability. However, due to the absence of a fully-integrated information system and the gaining popularity of distributed computing, machine reconfiguration decisions are made by machine controllers on the shop floor where heterarchical control architecture is typically used. This research investigates how non-cooperative game theory could be applied for facilitating the decision process reconfiguration decision-making at the machine controller level as machines are competed by multiple jobs streams. This paper presents how material flow traffic can be better regulated in a reconfigurable manufacturing environment. A study using an industrial pilot system is discussed to demonstrate the applicability of the proposed approach, in which heuristics are used to determine the game specification.     

Learning to monitor a machine tool

This paper deals with the issue of automatic learning and recognition of various conditions of a machine tool. The ultimate goal of the research discussed in this paper is to develop a comparehensive monitor and control (M&C) system that can substitute for the expert machinist and perform certain critical in-process tasks to assure quality production. The M&C system must reliably recognize and respond to qualitatively different behaviours of the machine tool, learn new behaviors, respond faster than its human counterpart to quality threatening circumstances, and interface with an existing controller. The research considers a series of face-milling anomalies that were subsequently simulated and used as a first step towards establishing the feasibility of employing machine learning as an integral component of the intelligent controller. We address the question of feasibility in two steps. First, it is important to know if the process models (dull tool, broken tool, etc.) can be learned (model learning). And second, if the models are learned, can an algorithm reliably select an appropriate model (distinguish between dull and broken tools) based on input from the model learner and from the sensors (model selection). The results of the simulation-based tests demonstrate that the milling-process anomalies can be learned, and the appropriate model can be reliably selected. Such a model can be subsequently utilized to make compensating in-process machine-tool adjustments. In addition, we observed that the learning curve need not approach the 100% level to be functional.     

The status of machine ethics: a report from the AAAI Symposium

This paper is a summary and evaluation of work presented at the AAAI 2005 Fall Symposium on Machine Ethics that brought together participants from the fields of Computer Science and Philosophy to the end of clarifying the nature of this newly emerging field and discussing different approaches one could take towards realizing the ultimate goal of creating an ethical machine.

A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes

Global economic competition has spurred the manufacturing sector to improve and invest in modern equipment to satisfy the needs of the market. In particular, machine tool selection is the most important problem; it plays a primary role in the improvement of productivity and flexibility in the manufacturing environment and involves the imprecise, vague and uncertain information. This paper presents the hybrid approach of the fuzzy ANP (Analytic Network Process) and COPRAS-G (COmplex PRoportional ASsessment of alternatives with Grey relations) for fuzzy multi-attribute decision-making in evaluating machine tools with consideration of the interactions of the attributes. The fuzzy ANP is used to handle the imprecise, vague and uncertain information from expert judgments and model the interaction, feedback relationships and interdependence among the attributes to determine the weights of the attributes. COPRAS-G is employed to present the preference ratio of the alternatives in interval values with respect to each attribute and calculate the weighted priorities of the machine alternatives. Alternatives are ranked in ascending order by priority. As a demonstration of the proposed model, a numerical example is implemented based on the collected data and the literature. The result is then compared with the rankings provided by other methods such as TOPSIS-G, SAW-G and GRA. Moreover, a sensitivity analysis is conducted to verify the robustness of the ranking. The result highlights that the hybrid approach of the fuzzy ANP and COPRAS-G is a highly flexible tool and reaches an effective decision in machine tool selection.     

From biological consciousness to machine consciousness: An approach to make smarter machines

Consciousness research has been of great concern to philosophers, psychologists and neuroscientists in recent years. At the same time, consciousness has also attracted more and more interest of artificial intelligence （AI） researchers. In order to make more intelligent machines, many computing models of machine consciousness have been presented. Furthermore, self-consciousness has relevance to the level of intelligent functions. Hence, it is necessary to study self-consciousness in AI. This thesis, starting from biological consciousness, discusses some viewpoints of machine consciousness. Based on the discussions, we present a way to emulate self-consciousness and test this method via simulation experiments. Our results indicate that self-consciousness, which belongs to organisms, can he imitated by machines.     

Learning control and knowledge representation

Practising to operate an unknown system and observing the input and output of the system, in a sense, helps to optimally control that system. The acquired knowledge, is, in turn, used to solve future analogous control problems. This means that it is very important to know how to memorize the acquired knowledge and to utilize it for learning. In this paper, we propose a new knowledge representation and reasoning method and develop a learning machine (KBLC: Knowledge-Based Learning Controller) by using them. A simple implementation has been constructed that demonstrates the feasibility of building such a machine.     

A synergistic approach to manufacturing systems control using machine learning and simulation

This paper describes a synergistic approach that is applicable to a wide variety of system control problems. The approach utilizes a machine learning technique, goal-directed conceptual aggregation (GDCA), to facilitate dynamic decision-making. The application domain employed is Flexible Manufacturing System (FMS) scheduling and control. Simulation is used for the dual purpose of providing a realistic depiction of FMSs, and serves as an engine for demonstrating the viability of a synergistic system involving incremental learning. The paper briefly describes prior approaches to FMS scheduling and control, and machine learning. It outlines the GDCA approach, provides a generalized architecture for dynamic control problems, and describes the implementation of the system as applied to FMS scheduling and control. The paper concludes with a discussion of the general applicability of this approach.     

An efficient approach to determine cell formation,cell layout and intracellular machine sequence in cellular manufacturing systems

Cellular manufacturing systems (CMS) are used to improve production flexibility and efficiency. They involve the identification of part families and machine cells so that intercellular movement is minimized and the utilization of the machines within a cell is maximized. Previous research has focused mainly on cell formation problems and their variants; however, only few articles have focused on more practical and complicated problems that simultaneously consider the three critical issues in the CMS-design process, i.e., cell formation, cell layout, and intracellular machine sequence. In this study, a two-stage mathematical programming model is formulated to integrate the three critical issues with the consideration of alternative process routings, operation sequences, and production volume. Next, because of the combinatorial nature of the above model, an efficient tabu search algorithm based on a generalized similarity coefficient is proposed. Computational results from test problems show that our proposed model and solution approach are both effective and efficient. When compared to the mathematical programming approach, which takes more than 112 h (LINGO) and 1139 s (CPLEX) to solve a set of ten test instances, the proposed algorithm can produce optimal solutions for the same set of test instances in less than 12 s.     

A genetic algorithm approach to the multiple machine tool selection problem

A number of earlier researches have emphasized the on-the-job scheduling problems that occur with a single flexible machine. Two solutions to the problem have generally been considered; namely minimization of tool switches and minimization of tool switching instances. Methods used to solve the problems have included KTNS heuristic, dual-based relaxation heuristic, and non-LP-based branch-and-bound methods. However, scant literature has considered the case of job scheduling on multiple parallel machines which invokes another problem involving machine assignment. This paper addresses the problem of job scheduling and machine assignment on a flexible machining workstation (FMW) equipped with multiple parallel machines in a tool-sharing environment. Under these circumstances, the authors have attempted to model the problem with the objective of simultaneously minimizing both the number of tool switches and the number of tool switching instances. Furthermore, a set of realistic constraints has been included in the investigation. A novel genetic algorithm (GA) heuristic has been developed to solve the problem, and performance results show that GA is an appropriate solution.     

The application of manufacturing culture to the design of Asian region oriented machine tools

During the last years the demand for regionally and culturally harmonised machine design is increasingly on the agenda. The problem of localising products like machine tools instantly poses the question for new procedures that allow including the regional and cultural adaptations into the design processes of machine tool companies. How to transform the general insight into the necessity of culture- and region-adapted technologies and how to embed it into a design procedure comprising applicable design attributes is the crucial problem addressed. The paper shows in an exemplary way how ambiguous design attributes can eventually be embodied in a prototype design.     

Virtual machine tools and virtual machining—A technological review

Virtual manufacturing systems provide a useful means for products to be manufactured ‘right the first time’ without the need of physical testing on the shop floor. Earlier research was mostly on developing a virtual manufacturing environment. Over the years, simple graphical prediction and simulation gave way to complex multi-science predictions. Virtual systems such as Virtual Machine Tool, Virtual Machining, Virtual Assembly, Virtual Tooling and Virtual Prototype have been developed to support virtual manufacturing. Different systems and approaches have different targeted applications. This paper aims to provide a comprehensive review of existing virtual systems. Their focuses and approaches (i.e. virtual reality, Web-based techniques, mathematical modelling, hardware interactions and STEP-NC-based methodologies) are discussed in detail. To better understand the systems, we have categorized them into different groups according to their application domains. Discussions and concluding remarks are given based on the review.     

A modern approach to multiagent development

Multiagent systems (MAS) development frameworks aim at facilitating the development and administration of agent-based applications. Currently relevant tools, such as JADE, offer huge possibilities but they are generally linked to a specific technology (commonly Java). This fact may limit some application domains when deploying MAS, such as low efficiency or programming language restrictions. To contribute to the evolution of multiagent development tools and to overcome these constraints, we introduce a multiagent platform based on the FIPA standards and built on top of a modern object-oriented middleware. Experimental results prove the scalability and the short response-time of the proposal and justify the design and development of modern tools to contribute the multiagent technology.     

Holistic methodology using computer simulation for optimisation of machine tools

Virtual machine concepts supporting optimisation of machine tools have been developed in earlier work. The virtual machine concept is a tool that can describe the behaviour of a machine tool while considering the interaction between mechanics of the machines and the control system. Considerable amount of work has been done proving the concept and showing the potential of such a design tool in different contexts. Several studies have shown the potential of using the virtual machine concept, although, no work has been found that is exploring the potential of a full optimisation study.     

An approach to ergonomics evaluation of hand tools

Based on current knowledge of cumulative trauma disorders in the hand and forearm, related to the use of hand tools, an analysis was undertaken of variables to be considered in ergonomics evaluation of hand tools. Measurement methods were developed and an evaluation station was implemented. Measurement methods are physical, physiological or psychophysical. They focus on the tool, and on the effect of typical use of the tool on the operator. The evaluation station serves as a resource in the development, selection, and testing of tools for a given purpose.     

Prognostics of machine condition using soft computing

This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable ‘monitoring index’ extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.