Showing papers in "Robotics and Computer-integrated Manufacturing in 2006"

Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making

Abstract: Rapid prototyping (RP) is an emerging technology for creating physical prototypes from three-dimensional computer aided design models using the additive process with layers. In order to use the process efficiently, several important factors must be considered. The ability to select the optimal orientation of build-up is one of the critical factors since it affects the part quality, build time, and part cost. This study aims to determine the optimal build-up direction of a part for different RP systems. The revised average weighted surface roughness, which considers stair stepping effect, build time calculated by laser/knife/nozzle travel, and part cost calculated by build cost rate, labor cost rate, material cost, etc., are considered. Among the orientation candidates chosen from the convex hull of a model, the best orientation is selected using the simple additive weighting method, a widely used multi-criterion method for decision making. The best orientation is also identified for each criterion. The validity of the algorithm is demonstrated by several examples. The algorithm can help RP users select the best build-up direction of the part and create optimal process planning.

An agent-based framework for collaborative negotiation in the global manufacturing supply chain network

Abstract: This paper applies the multi-agent system paradigm to collaborative negotiation in a global manufacturing supply chain network. Multi-agent computational environments are suitable for dealing with a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving agents. An agent-based multi-contract negotiation system is proposed for global manufacturing supply chain coordination. Also reported is a case study of mobile phone global manufacturing supply chain management.

Selection, identification and comparison of industrial robots using digraph and matrix methods

Abstract: In the present work, a methodology based on digraph and matrix methods is developed for evaluation of alternative industrial robots. A robot selection index is proposed that evaluates and ranks robots for a given industrial application. The index is obtained from a robot selection attributes function, obtained from the robot selection attributes digraph. The digraph is developed considering robot selection attributes and their relative importance for the application considered. A step by step procedure for evaluation of robot selection index is suggested. Coefficients of similarity and dissimilarity and the identification sets are also proposed. These are obtained from the robot selection attributes function and are useful for easy storage and retrieval of the data. Two examples are included to illustrate the approach.

Realization of STEP-NC enabled machining

Abstract: A STEP-compliant CNC machine tool that demonstrated a G-code free machining scenario is presented. The aim of this research is to showcase the advantages of, and evaluate, STEP-NC—a new NC data model—by implementing it in a legacy CNC system. The work consists of two parts: retrofitting an existing CNC machine and the development of a STEP-compliant NC Converter called STEPcNC. The CompuCam's motion control system is used for retrofitting the machine, which is programmable using its own motion control language—6K Motion Control language and capable of interfacing with other CAPP/CAM programs through languages such as Visual Basic, Visual C++ and Delphi. STEPcNC can understand and process STEP-NC codes, and interface with the CNC controller through a Human Machine Interface. It makes use of STEP-NC information such as “Workplan”, “Workingstep”, machining strategy, machining features and cutting tools that is present in a STEP-NC file. Hence, the system is truly feature-based. The Application Interpreted Model of STEP-NC has been used.

A knowledge-based system for the prediction of surface roughness in turning process

Abstract: In the present work, a knowledge-based system is developed for the prediction of surface roughness in turning process. Neural networks and fuzzy set theory are used for this purpose. Knowledge acquired from the shop floor is used to train the neural network. The trained network provides a number of data sets, which are fed to a fuzzy-set-based rule generation module. A large number of IF–THEN rules are generated, which can be reduced to a smaller set of rules by using Boolean operations. The developed rule base may be used for predicting surface roughness for given process variables as well as for the prediction of process variables for a given surface roughness. The concise set of rules helps the user in understanding the behavior of the cutting process and to assess the effectiveness of the model. The performance of the developed knowledge-based system is studied with the experimental data of dry and wet turning of mild steel with HSS and carbide tools.

Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach

Abstract: In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.

A multi-objective approach to facility layout problem by genetic search algorithm and Electre method

Abstract: Classical approaches to layout design problem tend to maximise the efficiency of layout, measured by the handling cost related to the interdepartmental flow and to the distance among the departments. However, the actual problem involves several conflicting objectives hence requiring a multi-objective formulation. Multi-objective approaches, recently proposed, in most cases lead to the maximisation of a weighted sum of score functions. The poor practicability of such an approach is due to the difficulty of normalising these functions and of quantifying the weights. In this paper, this difficulty is overcome by approaching the problem in two subsequent steps: in the first step, the Pareto-optimal solutions are determined by employing a multi-objective constrained genetic algorithm and the subsequent selection of the optimal solution is carried out by means of the multi-criteria decision-making procedure Electre. This procedure allows the decision maker to express his preferences on the basis of the knowledge of candidate solution set. Quantitative (handling cost) and qualitative (adjacency and distance requests between departments) objectives are considered referring to a bay structure-based layout model, that allows to take into account also practical constraints such as the aspect ratio of departments. Results obtained confirm the effectiveness of the proposed procedure as a practicable support tool for layout designers.

Hybrid adaptive layer manufacturing: An Intelligent art of direct metal rapid tooling process

Abstract: A direct metal rapid tool making process, hybrid-layered manufacturing (HLM), was developed for building metallic dies and molds. This unique methodology has a numerical controlled system that integrates the TransPulse Synergic Metal Inert Gas (MIG)/Metal Active Gas (MAG) welding process for near-net layer deposition and Computer Numerical Control (CNC) milling process for net shaping. A customized software program was made to calculate the required adaptive slice thickness for the deposition of the filler metal with welding process as successive layers from the lowest to the topmost layer direction and to generate the required NC codes for machining from the top to the bottom layer direction of the deposited metallic layers for attaining the required contour profile shape. To implement this proposed process, a low-cost three-axis manipulator was fabricated with stepper motor divers in open-loop control and integrated with the weld machine. Adequate isolation to protect the motion control electronics from welding spike was incorporated. Synchronization of this two-step processing of each layer, yielding near-net deposition with welding process and near-net shaping with CNC milling operation offers a new accelerator way of building metal tools and dies.

Two-stage approach for machine-part grouping and cell layout problems

Abstract: Cellular manufacturing system (CMS) which is based on the concept of group technology (GT) has been recognized as an efficient and effective way to improve the productivity in a factory. In recent years, there have been continuous research efforts to study different facet of CMS. Most of them concentrated on distinguishing the part families and machine cells either simultaneously or individually with the objective of minimizing intercellular and intracellular part movements. This is known as machine-part grouping problem (MPGP) which is a crucial process while designing CMS. Nevertheless, in reality some components may not be finished within only one cell, they have to travel to another cell(s) for further operation(s). Under this circumstance, intercellular part movement will occur. Different order/sequence of machine cells allocation may result in different total intercellular movement distance unit. It should be noted that if the production volume of each part is very large, then the total number of intercellular movement will be further larger. Therefore, the sequence of machine cells is particularly important in this aspect. With this consideration, the main aim of this work is to propose two-stage approach for solving cell formation problem as well as cell layout problem. The first stage is to identify machine cells and part families, which is the essential part of MPGP. The work in second stage is to carry out a macro-approach to study the cell formation problem with consideration of machining sequence. The impact of the sequencing for allocating the machine cells on minimizing intercellular movement distance unit will be investigated in this stage. The problem scope, which is a MPGP together with the background of cell layout problem (CLP), has been identified. Two mathematical models are formulated for MPGP and CLP respectively. The primary assumption of CLP is that it is a linear layout. The CLP is considered as a quadratic assignment problem (QAP). As MPGP and QAP are NP-hard, genetic algorithm (GA) is employed as solving algorithm. GA is a popular heuristic search technique and has proved superior performance on complex optimization problem. In addition, an industrial case study of a steel member production company has been employed to evaluate the proposed MPGP and CLP models, and the computational results are presented.

An extended model of design process of lean production systems by means of process variables

Abstract: In this paper, we present an axiomatic modeling of lean production system design, using process variables (PVs). So far, we had developed a model for conceptual design of lean production systems by means of FR–DP relationships, the key characteristics of axiomatic design (AD) methodology, appeared in the proceedings of Second International Conference of Axiomatic Design. Albeit the model in question was thorough enough to be applied in various cases, its embedded abstract principles hamper straightforward applications and the required resources, tools, and techniques are not clarified. In AD terms, it lacks PVs created by mapping the design parameters (DPs) to the process domain to clarify the means that produce the specified DPs. Owing to the difficulties involved in the definition of PVs for manufacturing systems, there is few works in this area. This paper is an attempt to introduce PVs in production system design. When we are developing a product (i.e. a part), we can simply interpret the set of PVs as the process design. In the case of a production system we interpret PVs as the tools, methods, and resources, required for implementing a lean production system. In this paper, according to AD methodology, we have developed a hierarchical structure to model the design process of a lean production system, composed of FRs, DPs, and PVs. Serving as an efficient guideline for the design process and clarifying the required tools, methods, and resources, this structure is general enough to be applied for different cases.

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

Abstract: 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.

Prediction of workpiece elastic deflections under cutting forces in turning

Abstract: One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The whole approach is implemented through a Microsoft Excel © workbook. The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. The results support the proposed cutting mechanism. The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions.

Managing product/process knowledge in the concurrent/simultaneous enterprise environment

Abstract: Innovation is a critical factor in the success of industrial companies, and just as important is the need to get innovative products to the market quickly Therefore, it is important to talk about ‘management of product development time’ because, under this new paradigm, companies able of ‘mastering’ the development time will launch the product into the market just spending the planned time and resources and at the real moment This will give back to the company higher market share and faster market penetration The main objective of the project Acceleration of Innovative Ideas into the Market (AIM) is to provide the means of stimulating the creation of innovative ideas in general, and specifically on potential product/process improvements and on problem solving These ideas are collected throughout the extended enterprise from people involved with the products and processes; this knowledge will be further developed into innovations in a project-basis process

Optimization of process route by Genetic Algorithms

Abstract: Process route sequencing is considered as the key technology for computer aided process planning (CAPP) and is very complex and difficult. In this paper, based on the analyzing of various constraints in process route sequencing and the astringency of Genetic Algorithms (GAs), the GA is reconstructed, including the establishing of the coding strategy, the evaluation operator and the fitness function. The new GAs can meet the requirement of sequencing work and can meet the requirement of astringency. The natural number is adopted in coding strategy, the “elitist model” and the “tournament selection” are adopted as selection operators, the nonconforming sequential searching crossover operator is used and the inconsistent mutation operator is adopted, the fitness function is defined as a formula of the sum of compulsive constraints with each weighing, and these constraints are used as the control strategy for GAs in the searching process. By using GAs in the optimization, the optimal or near-optimal process route is obtained finally.

Intelligent environmental management for SMEs in manufacturing

Abstract: Currently environmental management is at the forefront of small and medium-sized enterprises (SMEs), especially in certain sectors where its presence is mandatory in order to operate in business. Although SMEs in certain sectors are catching up on their larger counterparts in reducing their environmental impacts, which are mainly due to supply chain demands, statistics clearly show that the majority of manufacturing SMEs are not incorporating formal environmental management system (EMS) into their businesses. The treatment of SMEs as one homogenous group by research has led to a lack of providing real strategies and tools that SMEs can implement and use. Current strategies are fragmented and seen as piecemeal, requiring adaptation and application to a particular sector by SMEs. SMEs do not have the resources required to research all environmental aspects of their businesses, which is a fundamental part of an EMS. Irish EMS support initiatives have but provided grant assistance in contributing towards the fees of external environmental consultants. As a result the majority of SMEs must rely on the help and assistance of external environmental consultants to assist them in taking account of their environmental impacts. Recent research obtained data on the current practices and experiences of Irish SMEs in areas of manufacturing and environmental management. Results of which, compares with previous research carried out in other European countries, however their significance and weightings differ considerably. Responses obtained regarding waste minimisation, recycling and reuse were positive, however, Irish manufacturing SMEs are falling short of initiating formal environmental management systems and front of pipe technologies. The use of information technology in assisting SMEs manage their environmental impacts holds much potential in providing a complete holistic environmental information management system (EIMS), which is already implemented in larger companies. Initial research has identified and developed proven strategies, solutions and tools from ISO 14001 certified Engineering SMEs, which will be used to form an initial base for an EIMS.

A particle swarm optimization algorithm for part–machine grouping

Abstract: Although in the last years different metaheuristic methods have been used to solve the cell formation problem in group technology, this paper presents the first particle swarm optimization (PSO) algorithm designed to address this problem. PSO is a population-based evolutionary computation technique based on a social behavior metaphor. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. A maximum cell size is imposed. Some published exact results have been used as benchmarks to assess the proposed algorithm. The computational results show that the PSO algorithm is able to find the optimal solutions on almost all instances.

A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system

Abstract: Manufacturing industries are rapidly changing from economies of scale to economies of scope, characterized by short product life cycles and increased product varieties. This implies a need to improve the efficiency of job shops while still maintaining their flexibility. These objectives are achieved by Flexible manufacturing systems (FMS). The basic aim of FMS is to bring together the productivity of flow lines and the flexibility of job shops. This duality of objectives makes the management of an FMS complex. In this article, the loading problem in random type FMS, which is viewed as selecting a subset of jobs from the job pool and allocating them among available machines, is considered. A heuristic based on multi-stage programming approach is proposed to solve this problem. The objective considered is to minimize the system unbalance while satisfying the technological constraints such as availability of machining time and tool slots. The performance of the proposed heuristic is tested on 10 sample problems available in FMS literature and compared with existing solution methods. It has been found that the proposed heuristic gives good results.

Similarity assessment of 3D mechanical components for design reuse

Abstract: Duplicate designs consume a significant amount of resources in most new product development. Search of similar parts for a given query part is the key to avoid this problem by facilitating design reuse. Most search algorithms convert the CAD model into a shape signature and compute the similarity between two models according to a measure function of their signatures. However, each algorithm defines the shape signature in a different way, and thus has its own limitations in discriminating 3D parts. This paper proposes a search scheme that successfully complements various shape signatures in similarity assessment of 3D mechanical components. It considers form-feature, topological, and geometric information in component comparison. Such an integrated approach can effectively solve the feature intersection problem, inherited in any feature-based approaches, and capture the user's intent more precisely in the search, which geometry-based methods fail to accomplish. We also develop a set of algorithms that performs the component comparison in a polynomial time. The proposed scheme is implemented in a product design environment consisting of commercial CAD and PDM systems. The result demonstrates the practicality of this work in automatic search of similar mechanical components for design reuse.

Intelligent automation systems for predictive maintenance: A case study

Abstract: A case study is presented, where a predictive maintenance solution for non-critical machinery (such as elevators and machine tools) was sought. Both cases are different. There is no experience in elevator monitoring and diagnosis, and modeling has been performed using Neural Networks. On the other hand, machine tools were monitored through vibration systems where some experience exists. In this case, Bayesian Networks are the paradigm of choice as it was also recommended to include some ‘adaptation’ mechanism for the knowledge modeled in the network. The final system also includes a sensor processing unit and a remote maintenance module system that provides an automated remote condition monitoring system, for both applications. Results indicate the feasibility of partial solutions in monitoring and diagnosis, though future enhancements are needed to compose a complete solution. This paper explains the characteristics of the Bayesian Network solution finally developed for high-speed machine tools, evaluate their strengths and weaknesses, and indicate the future enhancements.

Estimation of NC machining time using NC block distribution for sculptured surface machining

Abstract: The estimation of NC machining time is of importance because it provides manufacturing engineers with information to accurately predict the productivity of an NC machine, as well as its production schedule. NC programs contain various machining information, such as tool positions, feed and speed rates, and other machine instructions. Nominal NC machining time can easily be obtained based on the NC program data. Actual machining time, however, cannot simply be found due to the dynamic characteristics of a NC machine controller, such as acceleration and deceleration effect. Hence, this study presents an NC machine time estimation model for machining sculptured surfaces, considering such dynamic characteristics of the machine. The proposed estimation model uses several factors, such as the distribution of NC blocks, angle between the blocks, federates, acceleration and deceleration constants, classifying tool feed rate patterns into four types based on the acceleration and deceleration profile, NC block length, and minimum feed rate. However, there exists an error for the actual machining time due to the lack of the measurement equipment or tools to gauge an exact minimum feed rate. Thus, this paper proposes a machining time estimation model using NC block distributions, lowering down the error caused by the inaccurate minimum feed rate. The proposed machining time estimator performs at around 10% of mean error.

Economic value added of supply chain demand planning : A system dynamics simulation

Abstract: The development of a successful demand plan is typically a joint effort between different functional units such as Logistics, Marketing, Sales and executive management at one hand and between different business units on the other. Starting a project to structurally improve the demand planning often requires convincing all parties involved in such an effort. The key is to quantify the bottom-line impact of an increased demand planning reliability in the supply chain. This paper proposes a system dynamics simulation modeling framework that allows different managers to examine how improvements in their demand reliability will impact the overall corporate bottom-line. For example, supply chain managers can investigate how proposed changes in the supply chain demand forecasting structure, different suppliers, different logistics routes, or alternative inventory methods, may increase the overall profitability. The simulation model has been tested, validated with a real-life case of LG. Philips Displays Europe.

Genetic algorithms for the optimal common due date assignment and the optimal scheduling policy in parallel machine earliness/tardiness scheduling problems

Abstract: Earliness/tardiness scheduling problems with undetermined common due date which have wide application background in textile industry, mechanical industry, electronic industry and so on, are very important in the research fields such as industry engineering and CIMS. In this paper, a kind of genetic algorithm based on sectional code for minimizing the total cost of assignment of due date, earliness and tardiness in this kind of scheduling problem is proposed to determine the optimal common due date and the optimal scheduling policy for determining the job number and their processing order on each machine. Also, simulated annealing mechanism and the iterative heuristic fine-tuning operator are introduced into the genetic algorithm so as to construct three kinds of hybrid genetic algorithms with good performance. Numerical computational results focusing on the identical parallel machine scheduling problem and the general parallel machine scheduling problem shows that these algorithms outperform heuristic procedures, and fit for larger scale parallel machine earliness/tardiness scheduling problem. Moreover, with practical application data from one of the largest cotton colored weaving enterprises in China, numerical computational results show that these genetic algorithms are effective and robust, and that especially the performance of the hybrid genetic algorithm based on simulated annealing and the iterative heuristic fine-tuning operator is the best among them.

Enhancing disassembly and recycling planning using life-cycle analysis

Abstract: Both the general public and governmental agencies highly prioritize resource optimization (energy and material) and environmental issues such as ozone, acid rain and global warming in the life-cycle context. Disassembly and recycling are also increasingly important in most industrial countries due to the significant increase in the quantity of used products being discarded. Disassembly of used products has been recognized as necessary to make recycling economically viable in current state-of-the-art reprocessing technology. This emerging trend requires incorporating environmental considerations into design strategies. This study presents a graph-based heuristic method for disassembly analysis of end-of-life products, which incorporates the Eco-Design concept. Product components and their assembly relationships from the bill of material BOM are adopted to split the graph into sub-graphs denoting modular sub-assemblies. The life-cycle analysis LCA is then used to analyze disassembly trees, from which a disassembly sequence can be derived. Designers can use the analytical results to evaluate the dis-assemblability and recyclability of products when they are designed.

STEP-NC compliant process planning as an enabler for adaptive global manufacturing

Abstract: Manufacturing firms are seeking more efficient methods of CNC manufacture. ISO14649 informally known as STEP-NC has been proposed as a high-level hierarchical manufacturing information model as a replacement for the low-level machining instructions of ISO6983 and RS274D. In this paper, the applicability of STEP-NC as an enabler for creating an adaptive global manufacturing system is examined. The overall framework of the system is presented followed by an outline of its information requirements. Suitability of STEP-NC to support each requirement is then studied with the necessary additions highlighted. Finally, a test component is used in conjunction with a prototype of the advanced global manufacturing system to demonstrate the applicability of the STEP-NC standard to support manufacturing information in such a system.

Global output-feedback path tracking of unicycle-type mobile robots

Abstract: We present an output-feedback controller that forces the output (position and orientation) of a unicycle-type mobile robot to track a predefined path. A coordinate transformation is first derived to cancel the velocity quadratic terms. An observer is then designed to globally exponentially/asymptotically estimate the unmeasured velocities. The controller synthesis is based on Lyapunov's direct method and the backstepping technique. Our proposed controller works for both internally damped and un-damped cases. Simulations illustrate the soundness of the proposed controller.

Improvement of quality performance in manufacturing organizations by minimization of production defects

Abstract: The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process attribute chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks’ study after implementing changes, there was a 9% reduction in defects.

Characterization of closed-loop measurement accuracy in precision CNC milling

Abstract: This study investigates the closed-loop measurement error in computer numerical controlled (CNC) milling as they relate to the different inspection techniques. The on-line inspection of machining accuracy using a spindle probe has an inherent shortcoming because the same machine-produced parts are used for inspection. In order to use the spindle probe measurement as a means of correcting deviations in machining, the magnitude of measurement errors needs to be quantified. The empirical verification was made by conducting three sets of cutting experiments, followed by a design of experiment with three levels and three factors on a state-of-the-art CNC machining center. Three different material types and parameter settings were selected to simulate a diverse cutting condition. During the cutting, the cutting force and spindle vibration sensor signals were collected and a tool wear was recorded using a computer vision system. The bore tolerance was gauged by a spindle probe as well as a coordinate-measuring machine. The difference between the two measurements was defined as a closed-loop measurement error and the subsequent analysis was performed to determine the significant factors affecting the errors. The analysis results showed the potential of improving production efficiency and improved part quality.

A hybrid push/pull-dispatching rule for a photobay in a 300 mm wafer fab

Abstract: In this paper, the simulation analysis of an automated material handling system (AMHS) for a photobay in a 300 mm wafer fab was analyzed, considering the effects of the dispatching rules. Discrete-event simulation models were developed in an e-M Plant to study this system. Currently, the combination of the shortest distance with the nearest vehicle (SD_NV) and the first-encounter first-served (FEFS) dispatching rule was used in this system. In order to improve the system performance, a hybrid push/pull (PP) dispatching rule was proposed. The simulation results reveal a substantial improvement of the AMHS performance and reduced the WIP and cycle time as a consequence of implementing a PP dispatching rule.

Experimental studies on a local rescheduling procedure for dynamic routing of autonomous decentralized AGV systems

Abstract: This paper addresses a local rescheduling procedure for a distributed routing system of multiple Automated Guided Vehicles in dynamic environments where the requests for transportation are given in real time. The effectiveness of the proposed method is investigated for a transportation system of 143 nodes and 10–30 AGV systems in a semi-conductor fabricating bay. A distributed and parallel routing system (DPRS) is implemented and tested on an experimental five mobile robot system. The results demonstrate that the proposed method can reduce the total computation time by 39% compared with the conventional method without lowering the performance level. It is also experimentally verified that the rescheduling procedure can deal with disturbances in a significantly short computation time.

Offshoring manufacturing: Implications for engineering jobs and education: A survey and case study

Abstract: One result of globalization is the rapid growth of offshoring, i.e., the outsourcing of functions and jobs to offshore locations. In the USA, offshoring has progressed to the point where it already affects everyday lives, from the cars we drive (of which a large portion of the work and components are outsourced) to computers (which are typically manufactured offshore and shipped back to the USA), to electronic diagnostics (where calls are answered overseas). This phenomenon has implications on our lives and on the jobs that engineers and scientists will assume both now and in the future. Further, it is something that all highly developed and even some lesser-developed countries must face. Consequently, the growth of outsourcing will have a major impact on the educational objectives of engineering programs and the resultant engineering curricula worldwide. This paper presents recent data on product and job offshore migrations and discusses the various dimensions of this phenomenon. In addition to the potential loss of engineering and other high-end technical jobs, sociological and cultural aspects, intellectual property issues, strategic planning concerns, and macro-economic issues are presented. For example, the effects of offshoring on the societal fabric of the countries that are recipients of manufacturing and service center outsourcing, such as China and India, are significant, rapid, and controversial. Offshoring has also begun to change the way that engineering programs in these countries educate their students. In addition, intellectual property issues create a major risk to companies considering outsourcing to certain less-developed countries. These issues can be broadly categorized into: the robustness and strength of intellectual property laws, and the degree to which these laws are implemented and enforced. Further, such factors as currency fluctuations and geo-political conditions can substantially impact outsourcing decisions and profitability. The phenomenon of offshoring, which is now affecting engineering careers, will play a major role in shaping engineering education worldwide. The next generation of engineers will need to possess the ability to work seamlessly across cultures, have outstanding communication skills and be familiar with the principles of project management, logistics, and systems integration. Some educational models that begin to address these requirements will be presented.