Using Predictive Modeling and Classification Methods for Single and Overlapping Bead Laser Cladding to Understand Bead Geometry to Process Parameter Relationships

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
R. J. Urbanic ◽  
S. M. Saqib ◽  
K. Aggarwal
Keyword(s):  
Laser Cladding ◽  
Predictive Modeling ◽  
Predictive Models ◽  
Goodness Of Fit ◽  
Process Parameter ◽  
Bead Geometry ◽  
Process Characteristics ◽  
Single Bead ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

Developing a bead shape to process parameter model is challenging due to the multiparameter, nonlinear, and dynamic nature of the laser cladding (LC) environment. This introduces unique predictive modeling challenges for both single bead and overlapping bead configurations. It is essential to develop predictive models for both as the boundary conditions for overlapping beads are different from a single bead configuration. A single bead model provides insight with respect to the process characteristics. An overlapping model is relevant for process planning and travel path generation for surface cladding operations. Complementing the modeling challenges is the development of a framework and methodologies to minimize experimental data collection while maximizing the goodness of fit for the predictive models for additional experimentation and modeling. To facilitate this, it is important to understand the key process parameters, the predictive model methodologies, and data structures. Two modeling methods are employed to develop predictive models: analysis of variance (ANOVA), and a generalized reduced gradient (GRG) approach. To assist with process parameter solutions and to provide an initial value for nonlinear model seeding, data clustering is performed to identify characteristic bead shape families. This research illustrates good predictive models can be generated using multiple approaches.

Development of predictive models for effective process parameter selection for single and overlapping laser clad bead geometry

2018 ◽  
Vol 24 (1) ◽  
pp. 214-228 ◽  
Author(s):  
Kush Aggarwal ◽  
R.J. Urbanic ◽  
Syed Mohammad Saqib
Keyword(s):  
Data Collection ◽  
Predictive Model ◽  
Laser Cladding ◽  
Process Planning ◽  
Process Parameters ◽  
Predictive Models ◽  
Goodness Of Fit ◽  
Bead Geometry ◽  
Content Type ◽  
Non Linear

Purpose The purpose of this work is to explore predictive model approaches for selecting laser cladding process settings for a desired bead geometry/overlap strategy. Complementing the modelling challenges is the development of a framework and methodologies to minimize data collection while maximizing the goodness of fit for the predictive models. This is essential for developing a foundation for metallic additive manufacturing process planning solutions. Design/methodology/approach Using the coaxial powder flow laser cladding method, 420 steel cladding powder is deposited on low carbon structural steel plates. A design of experiments (DOE) approach is taken using the response surface methodology (RSM) to establish the experimental configuration. The five process parameters such as laser power, travel speed, etc. are varied to explore their impact on the bead geometry. A total of three replicate experiments are performed and the collected data are assessed using a variety of methods to determine the process trends and the best modelling approaches. Findings There exist unpredictable, non-linear relationships between the process parameters and the bead geometry. The best fit for a predictive model is achieved with the artificial neural network (ANN) approach. Using the RSM, the experimental set is reduced by an order of magnitude; however, a model with R2 = 0.96 is generated with ANN. The predictive model goodness of fit for a single bead is similar to that for the overlapping bead geometry using ANN. Originality/value Developing a bead shape to process parameters model is challenging due to the non-linear coupling between the process parameters and the bead geometry and the number of parameters to be considered. The experimental design and modelling approaches presented in this work illustrate how designed experiments can minimize the data collection and produce a robust predictive model. The output of this work will provide a solid foundation for process planning operations.

Analysis of laser cladding process parameter influence on the clad bead geometry

2017 ◽  
Vol 61 (5) ◽  
pp. 883-891 ◽  
Author(s):  
Dara Moazami Goodarzi ◽  
Joonas Pekkarinen ◽  
Antti Salminen
Keyword(s):  
Laser Cladding ◽  
Process Parameter ◽  
Bead Geometry ◽  
Laser Cladding Process ◽  
Parameter Influence

scholarly journals Analysis of the Process Parameter Influence in Laser Cladding of 316L Stainless Steel

2018 ◽  
Vol 2 (3) ◽  
pp. 55 ◽  
Author(s):  
Piera Alvarez ◽  
M. Montealegre ◽  
Jose Pulido-Jiménez ◽  
Jon Arrizubieta
Keyword(s):  
Stainless Steel ◽  
Laser Cladding ◽  
Process Parameters ◽  
Cross Sections ◽  
316L Stainless Steel ◽  
Deposition Process ◽  
Process Parameter ◽  
Optimum Process ◽  
Processing Times ◽  
Manufacturing Technologies

Laser Cladding is one of the leading processes within Additive Manufacturing technologies, which has concentrated a considerable amount of effort on its development. In regard to the latter, the current study aims to summarize the influence of the most relevant process parameters in the laser cladding processing of single and compound volumes (solid forms) made from AISI 316L stainless steel powders and using a coaxial nozzle for their deposition. Process speed, applied laser power and powder flow are considered to be the main variables affecting the laser cladding in single clads, whereas overlap percentage and overlapping strategy also become relevant when dealing with multiple clads. By setting appropriate values for each process parameter, the main goal of this paper is to develop a processing window in which a good metallurgical bond between the delivered powder and the substrate is obtained, trying simultaneously to maintain processing times at their lowest value possible. Conventional metallography techniques were performed on the cross sections of the laser tracks to measure the effective dimensions of clads, height and width, as well as the resulting dilution value. Besides the influence of the overlap between contiguous clads and layers, physical defects such as porosity and cracks were also evaluated. Optimum process parameters to maximize productivity were defined as 13 mm/s, 2500 W, 30% of overlap and a 25 g/min powder feed rate.

Predictive Modeling for Adverse Events and Risk Stratification Programs for People Receiving Cancer Treatment

2021 ◽  
pp. OP.21.00198
Author(s):  
Chelsea K. Osterman ◽  
Hanna K. Sanoff ◽  
William A. Wood ◽  
Megan Fasold ◽  
Jennifer Elston Lafata
Keyword(s):  
Clinical Practice ◽  
Risk Stratification ◽  
Cancer Treatment ◽  
Acute Care ◽  
Predictive Modeling ◽  
Predictive Models ◽  
Emergency Department Visits ◽  
Targeted Interventions ◽  
Risk Algorithm ◽  
Oncology Care

Emergency department visits and hospitalizations are common among people receiving cancer treatment, accounting for a large proportion of spending in oncology care and negatively affecting quality of life. As oncology care shifts toward value- and quality-based payment models, there is a need to develop interventions that can prevent these costly and low-value events among people receiving cancer treatment. Risk stratification programs have the potential to address this need and optimally would consist of three components: (1) a risk stratification algorithm that accurately identifies patients with modifiable risk(s), (2) intervention(s) that successfully reduce this risk, and (3) the ability to implement the risk algorithm and intervention(s) in an adaptable and sustainable way. Predictive modeling is a common method of risk stratification, and although a number of predictive models have been developed for use in oncology care, they have rarely been tested alongside corresponding interventions or developed with implementation in clinical practice as an explicit consideration. In this article, we review the available published predictive models for treatment-related toxicity or acute care events among people receiving cancer treatment and highlight challenges faced when attempting to use these models in practice. To move the field of risk-stratified oncology care forward, we argue that it is critical to evaluate predictive models alongside targeted interventions that address modifiable risks and to demonstrate that these two key components can be implemented within clinical practice to avoid unplanned acute care events among people receiving cancer treatment.

Optimization of a Novel Combined Power/Refrigeration Thermodynamic Cycle

Solar Energy ◽  
2002 ◽  
Author(s):  
Shaoguang Lu ◽  
D. Yogi Goswami
Keyword(s):  
Waste Heat ◽  
Thermodynamic Cycle ◽  
Cycle Performance ◽  
Working Fluid ◽  
Second Law ◽  
Optimum Conditions ◽  
Ambient Temperatures ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient ◽  
Performance Conditions

A novel combined power/refrigeration thermodynamic cycle is optimized for thermal performance in this paper. The cycle uses ammonia-water binary mixture as a working fluid and can be driven by various heat sources, such as solar, geothermal and low temperature waste heat. It could produce power as well as refrigeration with power output as a primary goal. The optimization program, which is based on the Generalized Reduced Gradient (GRG) algorithm, can be used to optimize for different objective functions. Examples that maximize second law efficiency, work output and refrigeration output are presented, showing the cycle may be optimized for any desired performance parameter. In addition, cycle performance over a range of ambient temperatures was investigated. It was found that for a source temperature of 360K, which is in the range of flat plate solar collectors, both power and refrigeration outputs are achieved under optimum conditions. All performance parameters, including first and second law efficiencies, power and refrigeration output decrease as the ambient temperature goes up. On the other hand, for a source of 440K, optimum conditions do not provide any refrigeration. However, refrigeration can be obtained even for this temperature under non-optimum performance conditions.

scholarly journals Optimasi Pemanfaatan Air Embung Kasih untuk Domestik dan Irigasi Tetes

2019 ◽  
Vol 13 (1) ◽  
pp. 41
Author(s):  
Segel Ginting ◽  
Dadan Rahmandani ◽  
Abid Hendri Indarta
Keyword(s):  
Reduced Gradient ◽  
Generalized Reduced Gradient

Pemerintah membangun Embung Kasih untuk mengatasi terbatasnya sumber air di Desa Tuatuka, Provinsi NTT. Embung tersebut digunakan untuk memenuhi kebutuhan domestik dan irigasi. Pengoperasian embung perlu direncanakan karena volume tampungan terbatas. Optimasi penggunaan air embung diperlukan untuk menentukan jumlah penggunaan air dengan berbagai skenario kondisi hujan. Simulasi penggunaan air tahun 1974 s.d. 2015 dilakukan sebagai evaluasi untuk menilai keberhasilan operasi embung. Penelitian ini dilakukan dengan tujuan untuk menentukan jumlah pemakaian air untuk kebutuhan domestik dan atau irigasi secara optimal. Optimasi dilakukan dengan Metode Generalized Reduced Gradient (GRG) untuk fungsi tujuan memaksimalkan penggunaan air embung. Hasil optimasi diperoleh dengan beberapa skenario. Skenario pertama untuk hujan normal, pemanfaatan air untuk domestik sekitar 2.604 orang atau untuk mengairi lahan seluas 2,746 ha dengan irigasi tetes. Skenario kedua untuk kondisi hujan ekstrim basah, pemanfaatan air untuk domestik sekitar 3.601 orang atau untuk irigasi tetes sekitar 4,698 ha. Skenario ketiga untuk kondisi hujan ekstrim kering, pemanfaatan air untuk domestik sekitar 454 orang atau untuk irigasi tetes sekitar 0,45 ha. Berdasarkan evaluasi hasil optimasi dengan menggunakan simulasi data tahun 1974 s.d. 2015, maka ditetapkan jumlah penggunaan air embung untuk domestik sekitar 454 orang dan irigasi tetes seluas 1 Ha dengan tingkat keandalan operasi embung mencapai 78,57%.

Optimal Design of Semisubmersible’s Form Based on Systems Analysis

1984 ◽  
Vol 106 (4) ◽  
pp. 524-530 ◽  
Author(s):  
S. Akagi ◽  
R. Yokoyama ◽  
K. Ito
Keyword(s):  
Optimal Design ◽  
Computer Aided Design ◽  
Optimization Problem ◽  
Design Method ◽  
Gradient Algorithm ◽  
Interpretive Structural Modeling ◽  
Reduced Gradient ◽  
Optimal Design Method ◽  
Generalized Reduced Gradient ◽  
Aided Design

With the objective of developing a computer-aided design method to seek the optimal semisubmersible’s form, hierarchical relationships among many design objectives and conditions are investigated first based on the interpretive structural modeling method. Then, an optimal design method is formulated as a nonlinear multiobjective optimization problem by adopting three mutually conflicting design objectives. A set of Pareto optimal solutions is derived numerically by adopting the generalized reduced gradient algorithm, and it is ascertained that the designer can determine the optimal form more rationally by investigating the trade-off relationships among design objectives.

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