Manufacturing-Operation Planning Versus AI Planning

Although AI planning techniques can potentially be useful in several manufacturing domains, this potential remains largely unrealized. Many of the issues important to manufacturing engineers have now seemed interesting to AI researchers -- but, in order to adapt AI planning techniques to manufacturing, it is important to address these issues in a realistic and robust manner. Furthermore, by investigating these issues, AI researchers may be able to discover principles that are relevant for AI planning in general. As an example, in this paper we describe the techniques for manufacturing- operation planning used in IMACS (Interactive Manufacturability Analysis and Critiquing System). We compare and contrast them with the techniques used in classical AI planning systems, and point out that some of the techniques used in IMACS may also be useful in other kinds of planning problems. (Also cross-referenced as UMIACS-TR-95-3

Towards Feature-based Human-robot Assembly Process Planning

The paper proposes a generic approach to automated robotic assembly process planning. Such a novel feature-based model of the assembly process is presented which can be synthesized from the standard CAD model of the product and the description of the applicable resources. As a first step towards automated planning, the paper focuses on generating constraints that ensure plan feasibility, as well as on the formal verification of fully specified plans. Examples are given from the domains of robotic remote laser welding as well as collaborative human-robot mechanical assembly

An integrated approach to process planning and scheduling using genetic algorithms

Centre for Intelligent Systems and their ApplicationsThis thesis presents a new integrated approach to process planning aad job-shop scheduling. The relationship between planning and scheduling is reassessed and the line between the two tasks is made significantly more blurred than in the usual treatment. Scheduling is traditionally seen as the task of finding an optimal way of interleaving a number of fixed plans which are to be executed concurrently and which must share resources. The implicit assumption is that once planning has finished scheduling takes over. In fact there are often many possible choices for the sub-operations in the plans. Very often the real optimisation problem is to simultaaeously optimise all the individual plans alzd the overall schedule. This thesis describes how manufa.cturing planning has been recast to allow solutions to the simultaneous plan and schedule optimisation problem, a problem traditionally considered too hard to tackle at all. A model based on simulated coevolution is developed and it is shown how complex interactions are handled in an emergent way. Results from various implementations are reported. Underlying this new approach is a feature based process planning system that is used to generate the space of all possible legal process plans for a given component. This space is then searched, in parallel with spaces for all other components, using an advanced form of genetic algorithm. The thesis describes the development of the ideas behind this technique and presents in detail the constituent parts of the whole system

Feature-based validation reasoning for intent-driven engineering design

Feature based modelling represents the future of CAD systems. However, operations such as modelling and editing can corrupt the validity of a feature-based model representation. Feature interactions are a consequence of feature operations and the existence of a number of features in the same model. Feature interaction affects not only the solid representation of the part, but also the functional intentions embedded within features. A technique is thus required to assess the integrity of a feature-based model from various perspectives, including the functional intentional one, and this technique must take into account the problems brought about by feature interactions and operations. The understanding, reasoning and resolution of invalid feature-based models requires an understanding of the feature interaction phenomena, as well as the characterisation of these functional intentions. A system capable of such assessment is called a feature-based representation validation system. This research studies feature interaction phenomena and feature-based designer's intents as a medium to achieve a feature-based representation validation system. [Continues.

Research accomplished at the Knowledge Based Systems Lab: IDEF3, version 1.0

An overview is presented of the foundations and content of the evolving IDEF3 process flow and object state description capture method. This method is currently in beta test. Ongoing efforts in the formulation of formal semantics models for descriptions captured in the outlined form and in the actual application of this method can be expected to cause an evolution in the method language. A language is described for the representation of process and object state centered system description. IDEF3 is a scenario driven process flow modeling methodology created specifically for these types of descriptive activities

Development of a cost reduction methodology for start-up SMES developing a novel product.

This thesis is concerned with identifying cost engineering requirements of start-up SMEs in the manufacturing sector and developing a solution to address a number of their major requirements. The focus of the thesis is on cost reduction at the product development stage of a novel product. The aim of the thesis is to develop a cost reduction framework for SMEs developing a novel product in order to transfer necessary cost engineering knowledge to an SME in a structured way. A literature review has been completed to identify potential areas for cost reduction and build an understanding of SMEs’ characteristics in cost engineering requirements. The review confirmed the finding that SMEs lack cost engineering knowledge. Therefore, cost reduction best practices were identified through literature review and analysed for relevance for SMEs. Collaborating with a start-up SME developing a novel product helped to identify SMEs cost engineering requirements. By close observation and participation of the SME, areas lacking knowledge were identified. In addition, potential cost reduction opportunities were examined. Due to the nature of cost reduction activities, it was required to study day to day activities of the collaborating company and become familiarised with development, production and business plans for the product. The identification of the requirements and development of the AS IS model of the SME helped to build an understanding of characteristics and requirements of start-up SMEs. Based on these the cost reduction framework was developed. The framework includes best practice tools and methods which comply with start-up SMEs’ characteristics. Where a suitable method could not be identified, the method was developed in this research to address their requirements. The framework was implemented in the collaborating company and was validated by presenting the results to internal and external experts from industry and academia. Keywords: Start-up SME, Cost Engineering, Manufacturing, Novel product, production cost.PhD in Manufacturin

Optimization-Based Architecture for Managing Complex Integrated Product Development Projects

By the mid-1990\u27s, the importance of early introduction of new products to both market share and profitability became fully understood. Thus, reducing product time-to-market became an essential requirement for continuous competition. Integrated Product Development (IPD) is a holistic approach that helps to overcome problems that arise in a complex product development project. IPD emphasis is to provide a framework for an effective planning and managing of engineering projects. Coupled with the fact that about 70% of the life cycle cost of a product is committed at early design phases, the motivation for developing and implementing more effective methodologies for managing the design process of IPD projects became very strong. The main objective of this dissertation is to develop an optimization-based architecture that helps guiding the project manager efforts for managing the design process of complex integrated product development projects. The proposed architecture consists of three major phases: system decomposition, process re-engineering, and project scheduling and time-cost trade-off analysis. The presented research contributes to five areas of research: (1) Improving system performance through efficient re-engineering of its structure. The Dependency Structure Matrix (DSM) provides an effective tool for system structure understanding. An optimization algorithm called Simulated Annealing (SA) was implemented to find an optimal activity sequence of the DSM representing a design project. (2) A simulation-based optimization framework that integrates simulated annealing with a commercial risk analysis software called Crystal Ball was developed to optimally re-sequence the DSM activities given stochastic activity data. (3) Since SA was originally developed to handle deterministic objective functions, a modified SA algorithm able to handle stochastic objective functions was presented. (4) A methodology for the conversion of the optimally sequenced DSM into an equivalent DSM, and then into a project schedule was proposed. (5) Finally, a new hybrid time-cost trade-off model based on the trade-off of resources for project networks was presented. These areas of research were further implemented through a developed excel add-in called “optDSM”. The tool was developed by the author using Visual Basic for Application (VBA) programming language

Task planning with uncertainty for robotic systems

In a practical robotic system, it is important to represent and plan sequences of operations and to be able to choose an efficient sequence from them for a specific task. During the generation and execution of task plans, different kinds of uncertainty may occur and erroneous states need to be handled to ensure the efficiency and reliability of the system. An approach to task representation, planning, and error recovery for robotic systems is demonstrated. Our approach to task planning is based on an AND/OR net representation, which is then mapped to a Petri net representation of all feasible geometric states and associated feasibility criteria for net transitions. Task decomposition of robotic assembly plans based on this representation is performed on the Petri net for robotic assembly tasks, and the inheritance of properties of liveness, safeness, and reversibility at all levels of decomposition are explored. This approach provides a framework for robust execution of tasks through the properties of traceability and viability. Uncertainty in robotic systems are modeled by local fuzzy variables, fuzzy marking variables, and global fuzzy variables which are incorporated in fuzzy Petri nets. Analysis of properties and reasoning about uncertainty are investigated using fuzzy reasoning structures built into the net. Two applications of fuzzy Petri nets, robot task sequence planning and sensor-based error recovery, are explored. In the first application, the search space for feasible and complete task sequences with correct precedence relationships is reduced via the use of global fuzzy variables in reasoning about subgoals. In the second application, sensory verification operations are modeled by mutually exclusive transitions to reason about local and global fuzzy variables on-line and automatically select a retry or an alternative error recovery sequence when errors occur. Task sequencing and task execution with error recovery capability for one and multiple soft components in robotic systems are investigated

Hierarchical volumetric object representations for digital fabrication workflows

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 111-114).Modern systems for computer-aided design and manufacturing (CAD/CAM) have a history dating back to drafting boards, early computers, and machine shops with specialized technicians for each stage in a manufacturing workflow. In recent years, personal-scale digital fabrication has challenged many of these workflows' build-in assumptions. A single individual may control the entire workflow, from design to manufacture; they will be using computers that are exponentially more powerful than those in the 1970s; and they may be using a wide variety of tools, machines, and processes. The variety of tools and machines leads to a combinatorial explosion of possible workflows. In addition, tools are based on boundary representations, which are fragile and can easily describe nonsensical objects. This thesis addresses these issues with a set of tools for end-to-end digital fabrication based on volumetric solid models. Workflows are modular, making it easy to add new machines, and a shared core of path-planning operations reduces system complexity. Replacing boundary representations with volumetric representations guarantees that models represent reasonable real-world solids. Adaptively sampled distance fields are used as a generic interchange format. Functional representations are used as a design representation, and we examine scaling behavior and efficient rendering. We present interactive design tools that use these representations as their geometry engine. Data from CT scans is also used to populate these distance fields, showing significant benefits in file size and resolution compared to meshes. Finally, these representations are used as inputs to a modular multimachine CAM workflow. Toolpath generation is implemented, characterized, and tested on a complex solid model. We conclude with a summary of results and recommendations for future research directions.by Matthew Keeter.S.M