Preshaping command inputs to reduce telerobotic system oscillations

The results of using a new technique for shaping inputs to a model of the space shuttle Remote Manipulator System (RMS) are presented. The shapes inputs move the system to the same location that was originally commanded, however, the oscillations of the machine are considerably reduced. An overview of the new shaping method is presented. A description of RMS model is provided. The problem of slow joint servo rates on the RMS is accommodated with an extension of the shaping method. The results and sample data are also presented for both joint and three-dimensional cartesian motions. The results demonstrate that the new shaping method performs well on large, telerobotic systems which exhibit significant structural vibration. The new method is shown to also result in considerable energy savings during operations of the RMS manipulator

Beyond Cost: Product Complexity and the Global Product Development Location Advantage

Discussions of location advantages in global product development are largely based on self-reported surveys, and often agnostic to product characteristics. We build on this previous work by investigating location advantages and the influence of product complexity using negative binomial models. We find that the likelihood of developing products in a country increases as its market size, number of engineering graduates and national capability increases. However, it neither varies with labor cost nor market growth rate. We also find that complex products are more likely to be developed in countries with high national capability, and national capability is directly related to firm capability

Method and apparatus for creating time-optimal commands for linear systems

A system for and method of determining an input command profile for substantially any dynamic system that can be modeled as a linear system, the input command profile for transitioning an output of the dynamic system from one state to another state. The present invention involves identifying characteristics of the dynamic system, selecting a command profile which defines an input to the dynamic system based on the identified characteristics, wherein the command profile comprises one or more pulses which rise and fall at switch times, imposing a plurality of constraints on the dynamic system, at least one of the constraints being defined in terms of the switch times, and determining the switch times for the input to the dynamic system based on the command profile and the plurality of constraints. The characteristics may be related to poles and zeros of the dynamic system, and the plurality of constraints may include a dynamics cancellation constraint which specifies that the input moves the dynamic system from a first state to a second state such that the dynamic system remains substantially at the second state

Modularizing Product Architectures Using Dendrograms

A module is a structurally independent building block of a larger system with well-defined interfaces. A module is fairly loosely connected to the rest of the system allowing an independent development of the module as long as the interconnections at the interfaces are well thought of. [1][2] The advantages of modularity are possible economies of scale and scope and economies in parts sourcing [1]. Modularity also provides flexibility that enables product variations and technology development without changes to the overall design [2]. Same flexibility allows also for independent development of modules, which is useful in concurrent design or overlapped product development [3], collaborative projects, or when buying the module from a supplier [4]. Modularity also eases the management of complex product architectures [2] and therefore also their development. Modularity can also be used to create product families [5] [6] [7]. This saves design and testing costs and can allow for greater variation but one must be aware of possible excess functionality costs if a low cost and low functionality part is replaced by a higher cost part in order to use the same part in both products [8] [9]. Modularity and product platforms have been shown to be useful [e.g. 6] but there seem to be few methods to choose the best modules for a product family or joint development platform. Baldwin and Clark [1] discuss how to modularize but they do not address the problem of what exactly should be included in a module. Ericsson [2] has developed a modularization method called Modular Function Deployment (MFD) but it is intended for single products only, not product families. Also Design Structure Matrix clustering [10] [11] is intended for single products, but it has an advantage that it has been reduced to a repeatable algorithms that 2 can be run by a computer, which enables the modularizing of also complex systems. Stake [11] introduces a clustering algorithm for MFD to group functions according to modularity driver scores. He and Blackenfelt [12] also show how MFD and DSM can be integrated to combine benefits of the two methods but they are still intended for single products only. Kota et al. [13] present a benchmark method to compare own platform to competitor’s platform. The method takes manufacturing, component’s size, and material into account in addition to functionality, but it is not a platforming tool. Stone et al. [14] discuss heuristics to group functions in a function structure [for more about function structure see 15] into modules within a product and Zamirowski and Otto [7] add three additional heuristics to apply across products in a product family. Dahmus [5] et al. apply the heuristics and introduce a modularity matrix to help decide what modules should and what should not be shared across a product family. The weakness of the heuristics is that they are not repeatable since the functional decomposition and the use of heuristics depend on the user’s point of view. Our goal is to overcome these weaknesses by introducing a more systematic method for grouping functions into modules. Another weakness of the existing methods is that they use nominal or ordinal scales instead of more rigorous ratio scales. Sosa et al. [16] use ordinal scale (-2,-1,0,1,2) in component DSMs, Ericsson [2] in MFD, and Stake [11] and Blackenfelt [12] in their combined MFD/DSM approach. Dahmus [5] as well as Zamirowski and Otto [7] suggest the use of Pugh’s concept selection that is also based on ordinal scales. Kmenta and Ishii [17] discuss the problem of performing arithmetic operations on ordinal measures. Stated simply, it produces inconsistent results. Otto and Wood [18] discuss more broadly the strengths and weakness of these different type measures. Kurshid and Sahai [19] present a rigorous treatment of these measures. Ratio scales are most useful because the point zero has meaning, and mathematical operations such as multiplying and dividing have meaning, e.g., meters/second. In this paper, we address the weakness of all the above. We use a more flexible flow method [20] for identifying possible modules in a function structure and our algorithm can be put into a computer. In addition we develop a genuine metric space with a distance function that is based on the flow characteristics and we will use a ratio scale. This algorithm is designed especially for the flow method [20] but it could possibly be used also in conjunction with other modularization methods. The flow method is based on the heuristics introduced by Stone et al. [14] and further developed for product families by Zamirowski and Otto [7]. The difference is that in flow method the focus is on the flows instead of the functions in a function structure. Functions can even be ignored since often the end result (outputs) and the requirements needed to achieve it (inputs) are all that matter. The flow method was designed to identify commonalties between different products. It is more flexible than the function focused heuristics and can therefore be used also in case of joint development of a common module for even very different products. It is also applicable in product family platforming. The problem we address in this study is how to group functions in a functional decomposition, such as a function structure, to form a module commonalty hierarchy that can be used to define common modules across products. The following section will introduce the grouping algorithm. We will then go on to show an example of this method applied to four products. We will end the article with our conclusions and suggestions for further stud

A Simplified Method for Deriving Equations of Motion For Continuous Systems with Flexible Members

A method is proposed for deriving dynamical equations for systems with both rigid and flexible components. During the derivation, each flexible component of the system is represented by a "surrogate element" which captures the response characteristics of that component and is easy to mathematically manipulate. The derivation proceeds essentially as if each surrogate element were a rigid body. Application of an extended form of Lagrange's equation yields a set of simultaneous differential equations which can then be transformed to be the exact, partial differential equations for the original flexible system. This method's use facilitates equation generation either by an analyst or through application of software-based symbolic manipulation

Establishing Quantitative Economic Value for Features and Functionality of New Products and New Services (CHAPTER N)

This chapter has two key themes: (1) a list of customer needs is interesting, but insufficient for many development decisions, (2) establishing a quantified, dollar value for each requirement is more helpful. To that end, we present an approach and method to establishing the quantitative monetary value for new product features and performance. This approach is targeted to product development managers and engineers engaged at the “front-end” of the product development process when the decisions about selection and trade-off of product functions and features are made. This approach examines the customer’s business operations and essentially establishing their business case for your product down to the feature and performance levels. This provides for much better trade-off decisions in new product development. This approach also helps to identify whitespace opportunities, those new product and/or service opportunities that are not being served by any current product. Moreover, because the methodology is fine grained, the whitespace opportunities are resolved into clear and actionable product development projects.Center for Innovation in Product Developmen

Assessing Information Waste in Lean Product Development

Lean Product Development seeks to enhance the efficiency of product development projects by reducing and eliminating non-value-adding activities or waste, which can exist on every process level. The value stream through product development processes is a flow of information, and hence waste exists in interpersonal communication. The study elaborates the hypothesis that most information transfers do not add value to the product. It was further theorized that different means of communication are better suited for different kinds of information, at least from the lean point of view. In order to understand the occurrence and ramifications of waste in product development information flows, the information transferred between team members was analyzed in two student product development projects. With the help of a paper-based value stream map, frequencies of waste drivers in information, the share of waste in information transfers, the interdependencies of waste and means of communication, as well as timeliness of information transfers were analyzed. The study’s results show that waste is omnipresent in product development information transfers, as only twelve percent of all information transfers contribute value to the product, and nearly half of the information transfers could have been omitted without a decrease in product value. Assuming that preparing, sending, receiving and retrieving information accounts for most of the time spent in product development processes, an enormous theoretical potential for efficiency enhancements could thus be identified

Engineering Methods for Decision-Making

Presentation on engineering methods for decision-makin

Understanding Enterprise Risk Across an Aquisition Portfolio: A Grounded Theory Approach

Every acquisition program contains risks. But what impact do these risks have on the entire portfolio of acquisition activities? What does risk at the Enterprise level really mean? For example, risk collectively could portend great danger to the acquisition manager’s overall portfolio which might be otherwise masked by traditional program performance and analysis. Alternatively, these risks also might represent opportunities to achieve greater results when analyzed from a portfolio perspective. Initial review of the literature suggests that most leaders are unable to articulate the risk carried by their portfolio of product development activities or what this means to them. However, the same literature suggests they strongly desire this capability. Beginning with a review of the applicable literature in the areas of risk, product development (acquisition) and product portfolio management, portfolio-level risk applications are found to be sparse and ill-conceived. Initial analysis of interviews with portfolio leaders involving military product development activities in portfolios of large, complex, system development will be presented with a discussion of the implications of enterprise risk for product portfolio management