Arvind Rangarajan

Efficient Tool Paths and Part Orientation for Face Milling

High speed machining is pushing the limits of feeds and speeds. A different approach for high throughput is described here. The focus is on the maximum feed that can be obtained for a segment; the feed rate losses due to sharp changes in tool path are minimized. The interdependency of individual axis drive speeds for a tool path segment are analyzed. There exists an optimum work angle relative to the axes that reduces losses and increases allowable feeds for particular segments, saving valuable cycle time and balancing feed drive loads. Face milling and roughing steps of end milling are the most attractive application areas.

A decision-based analysis of compressed air usage patterns in automotive manufacturing

This study is an evaluation of the use and supply of compressed air, which is one of the most expensive energy sources in manufacturing, at Ford Motor Companys Livonia (Mich.) Transmission Plant. The aim of the study is to make recommendations to improve environmental and economic efficiency in future facilities. This paper presents a quantitative analysis of three compressed air supply patterns—plant air, point of use (POU), and local generation—as alternatives for future compressed air usage. Environmental Value systems (EnVS) tools are employed to analyze the economic and environmental performance of the three alternative supply patterns by using cost of ownership and environmental impact matrices. The results favor local generation over the other two alternatives in terms of economic and environmental considerations.

Extraction of Manufacturing Rules From Unstructured Text Using a Semantic Framework

Formal ontology and rule-based approaches founded on semantic technologies have been proposed as powerful mechanisms to enable early manufacturability feedback. A fundamental unresolved problem in this context is that all manufacturing knowledge is encoded in unstructured text and there are no reliable methods to automatically convert it to formal ontologies and rules. It is impractical for engineers to write accurate domain rules in a structured semantic languages such as Web Ontology Language (OWL) or Semantic Application Design Language (SADL). Previous efforts in manufacturing research that have targeted extraction of OWL ontologies from text have focused on basic concept names and hierarchies. This paper presents a semantics-based framework for acquiring more complex manufacturing knowledge, primarily rules, in a semantically-usable form from unstructured English text such as those written in manufacturing handbooks. The approach starts with existing domain knowledge in the form of OWL ontologies and applies natural language processing techniques to extract dependencies between different words in the text that contains the rule. Domain-specific triples capturing each rule are then extracted from each dependency graph. Finally, new computer-interpretable rules are composed from the triples. The feasibility of the framework has been evaluated by automatically and accurately generating rules for manufacturability from a manufacturing handbook. The paper also documents the cases that result in ambiguous results. Analysis of the results shows that the proposed framework can be extended to extract domain ontologies which forms part of the ongoing work that also focuses on addressing challenges to automate different steps and improve the reliability of the system.Copyright