Integration and test plans for complex manufacturing systems

The integration and test phases that are part of the development and manufacturing of complex manufacturing systems are costly and time consuming. As time-to-market is becoming increasingly important, it is crucial to keep these phases as short as possible, whilemaintaining system quality. This is especially true for the time-to-market driven semiconductor industry and for companies providing manufacturing systems to this industry such as ASML, a provider of lithographic systems. The Tangram research project has the goal, to shorten integration and test time by a model-based integration and test approach. The Ph.D. project described in this thesis is part of the Tangram project. To achieve integration and test time reduction, we developed three methods that each solve one of the following three integration and test problems: • Construction of an optimal test plan with respect to time, cost and/or quality. • Construction of an optimal integration plan with respect to time, cost and/or quality. • Construction of an optimal integration and test plan with respect to time, cost and/or quality. The test plan optimization method consists of two steps. The first step is the definition of a model of the test problem. This model consists of tests that can be performed with associated cost and duration, possible faults that can reside in the system with associated fault probability and impact (importance), and the relation between the tests and the possible faults, also denoted as the test coverage for each possible fault. The second step consists of calculating the optimal test plan based on this test model given an objective function and possible constraints on time, cost and/or risk, which is a parameter for the quality of the system. By constructing an AND/OR graph of the problem, where AND nodes denote tests and OR nodes denote system states represented by the ambiguous faults, all possible test sequences of this problem are obtained. An algorithm selects the best solution from this AND/OR graph. This solution is a set of test sequences, where the test sequence that is followed depends on the outcome (pass/fail) of the previous tests. The integration plan optimization method consists of the same two steps as the test plan optimization method. The integration model consists of modules with their development times, interfaces that denote which modules can be integrated with each other, and test phases with their durations. Furthermore, the model consists of the relation between test phases and modules indicating which modules should be integrated before the test phase may start. Also for this problem, an AND/OR graph is constructed. The AND nodes denote integration actions and the OR nodes denote system states represented by the modules that are integrated. An algorithm selects the optimal solution from this AND/OR graph. The optimal solution has the shortest possible integration time. The solution is a tree of integration actions and test phases indicating, for each module, the sequence of integration actions and test phases. The integration and test planning method is a combination of the two previously mentioned methods and also consists of two steps. The integration and test model is a combination of the test model and the integration model, with additional relations between modules and possible faults describing in which modules these possible faults are inserted. During the construction of the integration AND/OR graph, a test AND/OR graph is constructed for each integration AND node. This test AND/OR graph represents the test phase that is performed after that integration action. The start and stop moments of these test phases are determined by the test phase positioning strategy. We developed several test phase positioning strategies according to which test phases are started, for example periodically or when a certain risk level is reached. We applied the methods developed to industrial case studies in ASML to investigate the benefits of these methods. From a case study performed in the manufacturing of lithographic machines, we learned that the duration of a test phase may be reduced by approximately 20% when using the test plan optimization method instead of creating a test plan manually. From a case study performed in the integration phase of a new prototype system, we learned that using the integration planning method may reduce integration time by almost 10% compared to a manually created integration plan. From a case study performed in the integration and test phase of a software system, we learned that the final test phase durationmay be reduced by approximately 40% when applying a risk-based test phase positioning strategy instead of the currently used periodic test phase positioning strategy. We conclude that the methods developed can be used to construct optimal integration and test plans. These optimal integration and test plans are often more efficient than manually created plans, which reduces the time-to-market of a complex system while maintaining the same final system quality. Future research should indicate how to incorporate the methods developed in the complete integration and test process, and how to obtain the information needed to create the integration and test models

A knowledge based expert system for moulded part design

In today's competitive market many consumer products are designed with complex curved shapes to meet customers' demands for styling and ergonomics. These styled products are commonly manufactured using moulding processes because they can produce a wide range of freeform shapes at relatively low cost. However, although injection moulding and metal casting allow a great deal of design freedom they also make significant demands on the designer to ensure that parts are designed with due regard for manufacturability. This paper describes a knowledge based moulding advisor that has been developed to provide design for moulding advice to designers during the design process. The main contributions of the research are the development of a hierarchical knowledge representation to allow moulding advice to be generated at different levels of detail and the integration of the expert system with a geometric part description extracted from a Computer Aided Design (CAD) solid model. A demonstrator for the manufacturing advisor has been implemented using the expert system shell CLIPS and integrated with CAD using feature recognition. The moulding advisor is able to generate tailored design for moulding advice for a range of manufacturing processes and materials and evaluate the manufacturability of a designed part at the feature level. The paper provides a case study for a simple moulded test part

The degree of JIT in mineral oil and asphalt industries in Libya

The focus of this research is to show the degree of the Just-in-time system implementation in mineral oil and asphalt manufacturing industries in Libya and to evaluate the availability of its elements, which are management support, human, inventory management, total quality management and their relationship with the cost and competitiveness. Also, to know the challenges those are faced in this system implementation. A 650 questionnaire was developed for the workers in the mineral oil and asphalt manufacturing industries in Libya and tested using the collected data that was gained from the sample. The interview was made with 16 managers and responsible people in the area to discuss the possibility level of the JIT implementation and the problems that may prevent the implementation. T- Test and F- test used through a simple linear regression and multiple regressions to identify the best model for the research variables. The findings strongly supported the hypotheses that the four main elements are available. The simple linear regression showed that all elements have a significant relationship with costs and only three have a relationship with a competitive position. The findings of this research showed that the lack of the awareness about the importance of JIT in the target factories is the limitation of this research. Also, the finding showed that the overseas suppliers, the lack of management support are the most challenges that prevent JIT implementation. Finally, the recommendations were made according to the findings, which were the importance of introducing the JIT and its advantages to the oil industries in Libya and trying to overcome the obstacles that may prevent its implementation. This research emphasizes that the success of this system depends on both humans and technology, therefore, four elements are suggested as the most important elements to succeed with the JIT implementation in the long term strategy implementation

An interactive product development model in remanufacturing environment: a chaos-based artificial bee colony approach

This research presents an interactive product development model in re-manufacturing environment. The product development model defined a quantitative value model considering product design and development tasks and their value attributes responsible to describe functions of the product. At the last stage of the product development process, re-manufacturing feasibility of used components is incorporated. The consummate feature of this consideration lies in considering variability in cost, weight, and size of the constituted components depending on its types and physical states. Further, this research focuses on reverse logistics paradigm to drive environmental management and economic concerns of the manufacturing industry after the product launching and selling in the market. Moreover, the model is extended by integrating it with RFID technology. This RFID embedded model is aimed at analyzing the economical impact on the account of having advantage of a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits. Consideration the computational complexity involved in product development process reverse logistics, this research proposes; Self-Guided Algorithms & Control (S-CAG) approach for the product development model, and Chaos-based Interactive Artificial Bee Colony (CI-ABC) approach for re-manufacturing model. Illustrative Examples has been presented to test the efficacy of the models. Numerical results from using the S-CAG and CI-ABC for optimal performance are presented and analyzed. The results clearly reveal the efficacy of proposed algorithms when applied to the underlying problems. --Abstract, page iv

Innovation and demand in industry dynamics.

The links between three interconnected elements of the Schumpeterian sources of economic change are explored, conceptually and empirically, in this paper: the commitment of industries to invest profits in cumulative R&D efforts; the ability of industries’ R&D to lead to successful innovations; the impact of new products and processes on high entrepreneurial profits. We consider the nature and variety of innovative efforts – distinguishing in particular between strategies of technological and cost competiveness – and we introduce the role of demand in pulling technological change and supporting profits. We develop a simultaneous three-equation model and we test it at industry level – for 38 manufacturing and service sectors – on eight European countries over two time periods from 1994 to 2006. The results show that the model effectively accounts for the dynamics of European industries and highlights the interconnections between the different factors contributing to growth.R&D, Innovation, Profits, Demand, System Three Stages Least Squares.

Modeling of cellular manufacturing systems with productivity consideration: A simulated annealing algorithm.

Cellular Manufacturing (CM) is a manufacturing system in which similar parts and their required machines are grouped into manufacturing cells. The implementation of CM systems leads to increased output, decreased setup time, reduced work-in-process, reduced material handling cost as well as improved system productivity. One problem in the design of CM systems is cell formation (CF). Solving the CF problem in CM systems may lead to the organization or re-organization of manufacturing systems into manufacturing cells and to the determination of the type and number of machines required in each manufacturing cell. Many models have been developed to solve the CF problem over the last two decades. A thorough literature review reveals that most models use an indirect index such as a similarity or dissimilarity index as an objective function. The use of an indirect measure may not reflect the true state of CM systems. In this thesis, a CM system design model that uses a direct measure productivity index is presented. First, a 0-1 integer-programming model that maximizes the ratio of the output to the total material handling cost is developed to form part families and machine groups simultaneously. Second, a simulated annealing (SA) algorithm is developed to solve large-scale problems. This algorithm provides several advantages over some of the existing algorithms. It forms part families and machine groups simultaneously and considers production volume, selling price, and maximum number of machines in each cell. Moreover, it has the ability to determine the optimum number of manufacturing cells; so there is no need to specify the number of manufacturing cells in advance. Several problems selected from the literature are used to test the performance of the developed models. The results showed the superiority of the SA algorithm over the integer-programming model in both productivity and computational time. Furthermore, the majority of the existing models assume that each part has a unique process plan. In real manufacturing systems, however, a part can be produced using different routes and machines, which improves the productivity of CM systems. Hence, the developed models are extended to consider alternative process plans. Source: Dissertation Abstracts International, Volume: 61-09, Section: B, page: 4915. Adviser: S. Taboun. Thesis (Ph.D.)--University of Windsor (Canada), 2000

Computational Modeling Of Low Velocity Impact Loading Of Composite With And Without Electrospun Nanofiber

The objective of this work is the computational study of composite laminates under high velocity impact loadings. Even with the blessings of modern technology, manufacturing and testing of composites do not always seem to be cost effective. To keep pace with the fast moving global market, a new tool is absolutely necessary that can give reliable results quickly. Over the last decades computational study became a strong and effective tool for the researchers for testing composite materials in a virtual world. The virtual model and the material system of the test specimen can be built in a computer using different modeling software. Finite element analysis is the powerful numerical technique which can be used to predict the behavior of the test specimen under impact loading without incurring the cost and time associated with manufacturing and testing the test specimens. The present study includes analytical investigation of the high velocity impact behavior of ten (10) EG layer woven laminates with and without electrospun nanofibers using LS-DYNA. It was observed that composites with nanofibers in between layers perform better in low velocity impact resistance compared to composites without nanofibers