Control of thermoforming process parameters to manufacture surfaces with pin-based tooling

Many manufacturing processes used to mass produce parts rely on expensive and time consuming tooling. These processes include sheet metal forming, injection molding, casting, and thermoforming. The time invested in design and development of tooling can be justified for high-production volumes. However, for low-volume production and customized products, the tooling investment cannot be amortized. Flexible tooling has been developed to address the needs of smaller production volumes. Reconfigurable pin tooling is an example of flexible tooling that relies on a matrix of adjustable-height pins to produce approximate surfaces. A key challenge in pin-based tooling is achieving accurate high quality surfaces due to the undulations caused by the pins in mimicking the desired shape. This research studies the effects of process parameters on surface quality. A testbed pin tool and thermoformer are fabricated to support this research. The pin tool comprises of a 10 by 10 matrix of square pins. Each pin measures 0.25 inch by 0.25 inch by 2.5 inches and is actuated manually using screws. Twenty-one exploratory and thirty-two shape specific experiments were conducted with 12 inch by 12 inch polystyrene sheets to check the feasibility of producing undulation-free surfaces. The parameters that influence the quality of the surfaces are heating time, sheet thickness, and sheet to fixture distance. Surface quality is measured by conformance with respect to the tool and the intensity of undulations. The surface-reproducibility and the measurement-repeatability errors were determined to be ±0.0045 mm and ±0.00027 mm respectively. The surface quality can be improved by reducing intensity of undulations by controlling the process parameters. The quality of thermoformed surfaces using the pin tool is a function of heating time and the intended shape

Modeling Energy Expenditure and Recovery in Cycling

The power-duration relationship, comprised of the parameters Critical power (CP) and work capacity (ϒ), has been used to model energy expenditure in cycling. For modeling recovery, the W\u27bal model has been used but lacks validation. Additionally, existing literature has not focused on quantifying or estimating the inherent trial-to-trial variability at the subject level, called the intra-individual variability (IIV), of CP and ϒ, posing challenges in modeling and optimization of performance. Thus, the objectives of this research are (i) to establish a method to quantify the IIV of CP and ϒ as determined from the 3-minute all-out test (3MT), (ii) to develop a testing protocol to understand expenditure and recovery of power and ϒ, (iii) to establish ϒ recovery profiles in terms of recovery power (Prec) and recovery duration (trec), and (iv) to present a case of cycling performance optimization using the energy management system based on athlete-specific models. Competitive amateur cyclists participated in two cycle ergometer studies: (i) repeatability of 3MTs to quantify IIV and (ii) intermittent cycling, in the laboratory to establish ϒ recovery profiles. The studies included an incremental ramp test to determine gas exchange threshold (GET), two or four 3MTs to determine CP and ϒ, and nine intermittent cycling tests to understand recovery of ϒ. From the repeated 3MT study, a new method was proposed to compare any two pairs of the 3MT at the individual level and estimate the IIVs associated with CP and ϒ. In the second study, a statistically significant two-way interaction effect between Prec and trec on ϒ recovery was observed followed by simple main effects seen only with respect to Prec at each trec. This indicates that Prec has a greater influence on the recovery of ϒ in a recovery interval lasting 2-15 minutes that follows a semi-exhaustive exertion interval above CP. The overestimation of the actual ϒ-balance at the end of the recovery interval by the W\u27bal models highlights the need for athlete-specific recovery parameters or models. Finally, the optimization tests conducted with one subject provide encouraging signs for the use of individualized recovery models in real-time in-situ performance optimization

A survey of mathematical models of human performance using power and energy

The ability to predict the systematic decrease of power during physical exertion gives valuable insights into health, performance, and injury. This review surveys the research of power-based models of fatigue and recovery within the area of human performance. Upon a thorough review of available literature, it is observed that the two-parameter critical power model is most popular due to its simplicity. This two-parameter model is a hyperbolic relationship between power and time with critical power as the power-asymptote and the curvature constant denoted by W′. Critical power (CP) is a theoretical power output that can be sustained indefinitely by an individual, and the curvature constant (W′) represents the amount of work that can be done above CP. Different methods and models have been validated to determine CP and W′, most of which are algebraic manipulations of the two-parameter model. The models yield different CP and W′ estimates for the same data depending on the regression fit and rounding off approximations. These estimates, at the subject level, have an inherent day-to-day variability called intra-individual variability (IIV) associated with them, which is not captured by any of the existing methods. This calls for a need for new methods to arrive at the IIV associated with CP and W′. Furthermore, existing models focus on the expenditure of W′ for efforts above CP and do not model its recovery in the sub-CP domain. Thus, there is a need for methods and models that account for (i) the IIV to measure the effectiveness of individual training prescriptions and (ii) the recovery of W′ to aid human performance optimization