An investigation into tooling requirements and strategies for FMS operation

A study of the minimum tooling requirements and strategies for efficient operation of Flexible Manufacturing Systems, FMS's, in Assembly set Production, ASP, i.e production in sets of parts to completely assemble one or more product units, is presented in this research work. The main investigating tool is a simulation model. With this model the tool groups to be loaded into machines and fixtured pallet requirements were studied in conjunction with two scheduling rules. One is a FCFS rule and the other is a new rule, called MRPAS, which schedules work on the basis of the number of parts still unfinished belonging to an Assembly Set. The results of the research work show that ASP can be efficiently carried out in FMS's. However this requires that a good system set-up and adequate operating strategies are used. In particular appropriate tooling levels and good tooling configurations,TC's, i.e. combinations of tools in groups to be loaded into the machines, must be established to achieve high FMS performance. Tooling combination and duplication heuristic rules and the simulation model can be used for achieving this aim. The heuristic approach is shown to be necessary due to the impossibility, in a reasonable time, of evaluating the performance of FMS's under the large number of alternative tooling configurations which are possible. The level of fixtured pallets used can also have a great influence on system performance. Appropriate levels of these resources to operate FMS's for given TC's can be established using the methodology developed in this work. It is also important that good scheduling rules are used. In the cases studied, the MRPAS rule produces the best performance expressed as the combination of FMS utilization and production of complete assembly sets. Moreover a very small assembly set batch size, ASBS, i.e. number of AS released together into the FMS, is likely to be preferable. In the cases studied an ASBS of one performed best overall

Bottleneck Management through Strategic Sequencing in Smart Manufacturing Systems

Nowadays, industries put a significant emphasis on finding the optimum order for carrying out jobs in sequence. This is a crucial element in determining net productivity. Depending on the demand criterion, all production systems, including flexible manufacturing systems, follow a predefined sequence of job-based machine operations. The complexity of the problem increases with increasing machines and jobs to sequence, demanding the use of an appropriate sequencing technique. The major contribution of this work is to modify an existing algorithm with a very unusual machine setup and find the optimal sequence which will really minimize the makespan. This custom machine setup completes all tasks by maintaining precedence and satisfying all other constraints. This thesis concentrates on identifying the most effective technique of sequencing which will be validated in a lab environment and a simulated environment. It illustrates some of the key methods of addressing a circular non permutation flow shop sequencing problem with some additional constraints. Additionally, comparisons among the various heuristics algorithms are presented based on different sequencing criteria. The optimum sequence is provided as an input to a real-life machine set up and a simulated environment for selecting the best performing algorithm which is the basic goal of this research. To achieve this goal, at first, a code using python programming language was generated to find an optimum sequence. By analyzing the results, the makespan is increasing with the number of jobs but additional pallet constraint shows, adding more pallets will help to reduce makespan for both flow shops and job shops. Though the sequence obtained from both algorithms is different, for flow shops the makespan remains same for both cases but in the job shop scenario Nawaz, Enscore and Ham (NEH) algorithms always perform better than Campbell Dudek Smith (CDS) algorithms. For job shops with different combinations the makespan decreases mostly for maximum percentage of easy category jobs combined with equal percentage of medium and complex category jobs

Production Engineering and Management

It is our pleasure to introduce the 8th edition of the International Conference on Production Engineering and anagement (PEM), an event that is the result of the joint effort of the OWL University of Applied Sciences and the University of Trieste. The conference has been established as an annual meeting under the Double Degree Master Program “Production Engineering and Management” by the two partner universities. This year the conference is hosted at the university campus in Lemgo, Germany. The main goal of the conference is to offer students, researchers and professionals in Germany, Italy and abroad, an opportunity to meet and exchange information, discuss experiences, specific practices and technical solutions for planning, design, and management of manufacturing and service systems and processes. As always, the conference is a platform aimed at presenting research projects, introducing young academics to the tradition of symposiums and promoting the exchange of ideas between the industry and the academy. This year’s special focus is on Supply Chain Design and Management in the context of Industry 4.0, which are currently major topics of discussion among experts and professionals. In fact, the features and problems of Industry 4.0 have been widely discussed in the last editions of the PEM conference, in which sustainability and efficiency also emerged as key factors. With the further study and development of Direct Digital Manufacturing technologies in connection with new Management Practices and Supply Chain Designs, the 8th edition of the PEM conference aims to offer new and interesting scientific contributions. The conference program includes 25 speeches organized in seven sessions. Two are specifically dedicated to “Direct Digital Manufacturing in the context of Industry 4.0”. The other sessions are covering areas of great interest and importance to the participants of the conference, which are related to the main focus: “Supply Chai n Design and Management”, “Industrial Engineering and Lean Management”, “Wood Processing Technologies and Furniture Production”, and “Management Practices and Methodologies”. The proceedings of the conference include the articles submitted and accepted after a careful double-blind refereeing process

Tool flow management in batch manufacturing systems for cylindrical components

The objective of the research is to study the design of and operating strategies for advanced tool flow systems in highly automated turning systems. A prototype workstation has been built to aid this process. The thesis consists of three main parts. In the first part the current flexible manufacturing technology is reviewed with emphasis laid on tool flow and production scheduling problems. The 'State-of-the-Art' turning systems are studied, to highlight the requirement of the computer modelling of tool flow systems. In the second part, the design of a computer model using fast modelling algorithms is reported. The model design has concentrated on the tool flow system performance forecasting and improving. Attention has been given to the full representation of highly automatic features evident in turning systems. A number of contemporary production scheduling rules have been incorporated into the computer model structure, with the objectives of providing a frontend to the tool flow model, and to examine the tool flow problems interactively with the production scheduling rules. The user-interface of the model employs conversational type screens for tool flow network specification and data handling, which enhances its user friendliness greatly. An effective, fast, and easy to handle data base management system for tool, part, machine data entries has been· built up to facilitate the model performance. The third part of the thesis is concerned with the validation and application of the model with industry supplied data to examine system performance, and to evaluate alternative strategies. Conclusions drawn from this research and the recommendations for further work are finally indicated

Hubble Space Telescope Systems Engineering Case Study

The Hubble Space Telescope (HST) is an orbiting astronomical observatory operating in the spectrum from the near-infrared into the ultraviolet. Launched in 1990 and scheduled to operate through 2010, HST carries and has carried a wide variety of instruments producing imaging, spectrographic, astrometric, and photometric data through both pointed and parallel observing programs. Over 100,000 observations of more than 20,000 targets have been produced for retrieval. A macroscopic, cumulative representation of these observations is shown in the figure below to provide a sense of the enormous volume of astronomical data collected by the HST about our universe, our beginnings, and, consequently, about our future. The telescope is already well known as a marvel of science. This case study hopes to represent the facet of the HST that is a marvel of systems engineering, which, in fact, generated the scientific research and observation capabilities now appreciated worldwide

Data and Process Mining Applications on a Multi-Cell Factory Automation Testbed

This paper presents applications of both data mining and process mining in a factory automation testbed. It mainly concentrates on the Manufacturing Execution System (MES) level of production hierarchy. Unexpected failures might lead to vast losses on investment or irrecoverable damages. Predictive maintenance techniques, active/passive, have shown high potential of preventing such detriments. Condition monitoring of target pieces of equipment beside defined thresholds forms basis of the prediction. However, monitored parameters must be independent of environment changes, e.g. vibration of transportation equipments such as conveyor systems is variable to workload. This work aims to propose and demonstrate an approach to identify incipient faults of the transportation systems in discrete manufacturing settings. The method correlates energy consumption of the described devices with the workloads. At runtime, machine learning is used to classify the input energy data into two pattern descriptions. Consecutive mismatches between the output of the classifier and the workloads observed in real time indicate possibility of incipient failure at device level. Currently, as a result of high interaction between information systems and operational processes, and due to increase in the number of embedded heterogeneous resources, information systems generate unstructured and massive amount of events. Organizations have shown difficulties to deal with such an unstructured and huge amount of data. Process mining as a new research area has shown strong capabilities to overcome such problems. It applies both process modelling and data mining techniques to extract knowledge from data by discovering models from the event logs. Although process mining is recognised mostly as a business-oriented technique and recognised as a complementary of Business Process Management (BPM) systems, in this paper, capabilities of process mining are exploited on a factory automation testbed. Multiple perspectives of process mining is employed on the event logs produced by deploying Service Oriented Architecture through Web Services in a real multi-robot factory automation industrial testbed, originally used for assembly of mobile phones

Space physics missions handbook

The purpose of this handbook is to provide background data on current, approved, and planned missions, including a summary of the recommended candidate future missions. Topics include the space physics mission plan, operational spacecraft, and details of such approved missions as the Tethered Satellite System, the Solar and Heliospheric Observatory, and the Atmospheric Laboratory for Applications and Science