Backward assembly planning with DFA analysis

An assembly planning system that operates based on a recursive decomposition of assembly into subassemblies, and analyzes assembly cost in terms of stability, directionality, and manipulability to guide the generation of preferred assembly plans is presented. The planning in this system incorporates the special processes, such as cleaning, testing, labeling, etc. that must occur during the assembly, and handles nonreversible as well as reversible assembly tasks through backward assembly planning. In order to increase the planning efficiency, the system avoids the analysis of decompositions that do not correspond to feasible assembly tasks. This is achieved by grouping and merging those parts that can not be decomposable at the current stage of backward assembly planning due to the requirement of special processes and the constraint of interconnection feasibility. The invention includes methods of evaluating assembly cost in terms of the number of fixtures (or holding devices) and reorientations required for assembly, through the analysis of stability, directionality, and manipulability. All these factors are used in defining cost and heuristic functions for an AO* search for an optimal plan

LDRD final report: Automated planning and programming of assembly of fully 3D mechanisms

This report describes the results of assembly planning research under the LDRD. The assembly planning problem is that of finding a sequence of assembly operations, starting from individual parts, that will result in complete assembly of a device specified as a CAD model. The automated assembly programming problem is that of automatically producing a robot program that will carry out a given assembly sequence. Given solutions to both of these problems, it is possible to automatically program a robot to assemble a mechanical device given as a CAD data file. This report describes the current state of our solutions to both of these problems, and a software system called Archimedes 2 we have constructed to automate these solutions. Because Archimedes 2 can input CAD data in several standard formats, we have been able to test it on a number of industrial assembly models more complex than any before attempted by automated assembly planning systems, some having over 100 parts. A complete path from a CAD model to an automatically generated robot program for assembling the device represented by the CAD model has also been demonstrated

Knowledge Extraction from Work Instructions through Text Processing and Analysis

The objective of this thesis is to design, develop and implement an automated approach to support processing of historical assembly data to extract useful knowledge about assembly instructions and time studies to facilitate the development of decision support systems, for a large automotive original equipment manufacturer (OEM). At a conceptual level, this research establishes a framework for sustainable and scalable approach to extract knowledge from big data using techniques from Natural Language Processing (NLP) and Machine Learning (ML). Process sheets are text documents that contain detailed instructions to assemble a portion of the vehicle, specification of parts and tools to be used, and time study. To maintain consistency in the authorship process, assembly process sheets are required to be written in a standardized structure using controlled language. To realize this goal, 567 work instructions from 236 process sheets are parsed using Stanford parser using Natural Language Toolkit (NLTK) as a platform and a standard vocabulary consisting of 31 verbs is formed. Time study is the process of estimating assembly times from a predetermined motion time system, known as MTM, based on factors such as the activity performed by the associate, difficulty in assembling, parts and tools used, distance covered. The MTM compromises of a set of tables, constructed through statistical analysis and best-suited for batch production. These MTM tables are suggested based on the activity described in the work instruction text. The process of performing time studies for the process sheets is time consuming, labor intensive and error-prone. A set of (IF AND THEN ) rules are developed, by analyzing 1019 time study steps from 236 process sheets, that guide the user to an appropriate MTM table. These rules are computationally generated by a decision tree algorithm, J48, in WEKA, a machine learning software package. A decision support tool is developed to enable testing of the MTM mapping rules. The tool demonstrates how NLP techniques can be used to read work instructions authored in free-form text and provides MTM table suggestions to the planner. The accuracy of the MTM mapping rules is found to be 84.6%

STANDARDIZATION OF PROCESS SHEET INFORMATION TO SUPPORT AUTOMATED TRANSLATION OF ASSEMBLY INSTRUCTIONS AND PRODUCT-PROCESS COUPLING

The design and manufacture of large scale systems, particularly automobiles, is a process that requires collaboration across multiple disciplines. This collaboration is facilitated through efficient and effective data and knowledge storage and transfer. Specifically, the data associated with the final assembly of the vehicle should be generated, organized, and distributed in such a manner that each user of the data is able to use it effectively. Furthermore, the structure for the assembly data should be robust and reliable such that it fosters consistency among various planners for multiple vehicle platforms that are assembled all around the world. This research aims to develop a centralized data structure for assembly planning information that will allow a global automotive production company to create, relate, and store the necessary information for the assembly of multiple vehicle platforms in various locations, performed by associates that speak multiple languages. This is achieved by first observing the existing business processes of the company and identifying which processes are crucial to the production of vehicles, which processes may be omitted, and what processes need to be added. A data model is developed around these processes that allows multiple users to interact with the data as applicable to his or her duties, and a user interface is developed to demonstrate these interactions. The prototype system is demonstrated and validated with respect to the requirements associated with the business processes that need to be accomplished and ease of use

Generation of Optimized Robotic Assembly Sequence using Soft Computing Methods

The assembly process is one of the most time consuming and expensive manufacturing activities. The cost of assembly on an average is 10-30% of the manufacturing cost of a commercial product. The ratio between cost and performance of assembly has gradually increased with respect to the other phases of the manufacturing process and in recent years, this fact has caused a growing interest by industry in this area. Robotic assembly system which comes under the automated assembly system ncorporates the use of robots for performing the necessary assembly tasks. This is one of the most flexible assembly systems to assemble various parts into desired assembly (usable end-product). Robotic assembly systems are the programmable and have the flexibility to handle a wide range of styles and products, to assemble the same products in different ways, and to recover from errors. Robotic assembly has the advantage of greater process capability and scalability. It is faster, more efficient and precise than any conventional process. A variety of optimization tools are available for application to the problem. It is difficult to model the present problem as an n-p problem. Finding the best sequence generation involves the conventional or soft-computing methods by following the procedures of search algorithms