Pulling at the Digital Thread: Exploring the Tolerance Stack Up Between Automatic Procedures and Expert Strategies in Scan to Print Processes

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Tobias Mahan ◽  
Nicholas Meisel ◽  
Christopher McComb ◽  
Jessica Menold
Keyword(s):  
New Product Development ◽  
Point Clouds ◽  
Digital Fabrication ◽  
Physical Models ◽  
Controlled Study ◽  
Markov Modeling ◽  
Digital Models ◽  
Optical Scanner ◽  
Hidden Markov Modeling ◽  
Using Data

While the combination of 3D scanning and printing processes holds much promise for the field of new product development, problems with repeatability and accuracy have limited the wider spread adoption of some digital prototyping tools, such as 3D scanners. Studies have explored the errors inherent in higher fidelity scan to print (S2P) processes, yet few have explored the errors in S2P processes that leverage affordable rapid noncontact scanners. Studies have yet to explore the strategies that designers, who are experienced with additive manufacturing, employ to mitigate errors. To address these gaps, a controlled study was conducted using data from 27 scans collected with a prototypical off-the-shelf noncontact optical scanner. The geometric and dimensional integrity of the digital models was found to be significantly out of tolerance at various phases of the S2P process, as compared to the original physical model. Larger errors were found more consistently in the data acquisition phase of the S2P process, but results indicate these errors were not sufficiently filtered out during the remainder of the process. A behavioral study was conducted with 13 experienced designers in digital fabrication to determine strategies for manually cleaning Point Clouds. Actions such as increase or decrease in brush size and select or de-select points were recorded. These actions were analyzed using hidden Markov modeling, which revealed distinct patterns of behavior. Designer strategies were not beneficial and digital models produced by designers were found to be significantly smaller than original physical models.

Pulling at the Digital Thread: Exploring the Tolerance Stack Up in Scan to Print Processes

2018 ◽  
Author(s):  
Tobias Mahan ◽  
Brenna Doyle ◽  
Nicholas Meisel ◽  
Jessica Menold
Keyword(s):  
Reverse Engineering ◽  
New Product Development ◽  
Digital Model ◽  
Controlled Study ◽  
Seamless Integration ◽  
Prosthetic Devices ◽  
Accuracy And Precision ◽  
3D Printers ◽  
Using Data ◽  
Printing Processes

The rise of affordable rapid non-contact digitizers and rapid prototyping tools, such as 3D printers, is enabling the seamless integration of geometric reverse engineering into the early phases of engineering design. Scanning technology has been widely adopted in bio-reverse engineering and the use of high fidelity non-contact scanners, such as Computed Tomography devices, allows designers, doctors, and researchers to digitally model boney structures, design orthotic and prosthetic devices, and preemptively plan complex surgeries. While the combination of 3D scanning and printing processes holds much promise for the fields of reverse engineering, biodesign, and new product development, problems with repeatability, accuracy, and precision have limited the wider spread adoption of 3D scan to print processes. While some studies have explored the errors inherent in higher fidelity scan to print (S2P) processes, no studies have explored the errors in S2P processes that leverage affordable rapid non-contact digitizers. The purpose of this study was to explore at which phases of the S2P process errors are introduced into the digital model. A controlled study was conducted using data from 27 scans using a common off-the-shelf non-contact optical digitizer and a relatively simple workpiece. Data from the digital thread was collected between each phase of the S2P process and compared against a truth model; the geometric and dimensional integrity of the data was calculated through a comparison between the digital model and the original truth model. Results indicate significant differences between digital models at the various steps of the S2P process.

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices

2021 ◽  
pp. 001872082110328
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Qualitative Analysis ◽  
Clinical Decision Making ◽  
Hidden Markov ◽  
Activity Level ◽  
Markov Modeling ◽  
Support Tool ◽  
Case Complexity ◽  
Hidden Markov Modeling ◽  
Expert And Novice

Objective This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. Background With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. Method An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts ( n = 20) and novices ( n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants’ rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Results Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. Conclusion The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. Application By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

Concurrent Engineering Assessment: A Proposed Framework

1996 ◽  
Vol 210 (4) ◽  
pp. 321-328 ◽  
Author(s):  
J Poolton ◽  
I Barclay
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Concurrent Engineering ◽  
Research Study ◽  
New Product ◽  
Using Data ◽  
Past Experiences ◽  
Depth Interviews

There are few studies that have found an adequate means of assessing firms based on their specific needs for a concurrent engineering (CE) approach. Managers interested in introducing CE have little choice but to rely on their past experiences of introducing change. Using data gleaned from a nine month case study, a British-wide survey and a series of in-depth interviews, this paper summarizes the findings of a research study that examines how firms orientate themselves towards change and how they go about introducing CE to their operations. The data show that there are many benefits to introducing CE and that firms differ with respect to their needs for the CE approach. A tentative means to assess CE ‘needs’ is proposed which is based on the level of complexity of goods produced by firms. The method is currently being developed and extended to provide an applications-based framework to assist firms to improve their new product development performance.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

2021 ◽  
pp. 2150031
Author(s):  
Qin Tao ◽  
Yajing Si ◽  
Fali Li ◽  
Peiyang Li ◽  
Yuqin Li ◽  
...  
Keyword(s):  
Frontal Lobe ◽  
Visual Information ◽  
Hidden Markov ◽  
Decision Feedback ◽  
Markov Modeling ◽  
Feedback Process ◽  
Feedback Systems ◽  
Left Frontal Lobe ◽  
Hidden Markov Modeling ◽  
Independent Decision

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

scholarly journals Digital models and orthodontic diagnosis: what degree of reliability?

2021 ◽  
Vol 8 ◽  
Author(s):  
ABDOUL Hafizou RABE ◽  
Fatima SALEK ◽  
Intissar El IDRISSI ◽  
Fatima ZAOUI ◽  
Hicham BENYAHIA
Keyword(s):  
Systematic Review ◽  
Numerical Models ◽  
Meta Analysis ◽  
Physical Models ◽  
Cochrane Library ◽  
Digital Models ◽  
Significant Difference ◽  
Clinically Significant ◽  
Conventional Models ◽  
Plaster Models

Abstract  Background: Our study aims to evaluate, through a systematic review, the reliability of numerical models compared to conventional models on the main parameters of orthodontic diagnosis  Method: four databases were consulted: PubMed; Google Scholar, Cochrane Library, and Ebscohost. The research included published studies since 2010, meta-analysis studies, randomized and non-randomized controlled trials, prospective and retrospective studies. Results: Among 3811 selected references, only five studies met our inclusion criteria. In the systematic review, there were statistical differences between the digital models and the plaster models. However, this difference is clinically acceptable. On the other hand, there are some limitations, relative to the types of the severity of the congestion, the elapsed time to digitize, and the numerical means. Conclusion: The results of our systematic review have shown that there is no clinically significant difference between the numerical and physical models for the majority of diagnostic parameters.

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