The Additive Manufacturing Process Setting Feasible Space Exploration and Association With Variable Product Platform Design

2015 ◽  
Author(s):  
Xiling Yao ◽  
Seung Ki Moon ◽  
Guijun Bi
Keyword(s):  
Additive Manufacturing ◽  
Production Cost ◽  
Fuzzy Inference ◽  
Design Parameters ◽  
Product Platform ◽  
Activity Based Costing ◽  
Inference System ◽  
Wide Range ◽  
The Cost ◽  
Feasible Space

Additive manufacturing (AM) has evolved from prototyping to functional part fabrication for a wide range of applications. AM process settings have significant impact to both part quality and production cost, which makes the process setting adjustment a key consideration during product development and manufacturing. This research aims to investigate the relationship among process setting adjustments, costs, and component design parameters. Platform-based product family design and process family planning are used in this research as the strategy to provide product diversity while controlling cost. In this paper, the concept of a variable product platform and its corresponding AM process setting variants are proposed to describe the characteristics of additive manufactured platform modules. AM production cost drivers are identified. A Fuzzy Time-Driven Activity-Based Costing (FTDABC) approach is proposed to estimate the cost increment due to process setting adjustments. Time equations in the FTDABC are computed in a trained Adaptive Neuro-Fuzzy Inference System (ANFIS). The process setting adjustment’s feasible space boundary searching is formulated as an optimization problem, with minimizing the cost increment and maximizing the design parameters’ variability as objective functions. The upper and lower limits of variable platform module’s design parameters are mapped from process setting adjustments in a Mamdani-type expert system. The proposed methodology is illustrated in the analysis of a honeycomb-shaped bumper, which is taken as a variable platform module for a family of R/C racing cars. The result provides boundaries for design parameters, which confines the AM-enabled design space for product platform modules.

A Cost-Driven Design Methodology for Additive Manufactured Variable Platforms in Product Families

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Xiling Yao ◽  
Seung Ki Moon ◽  
Guijun Bi
Keyword(s):  
Design Methodology ◽  
Fuzzy Inference ◽  
Production Costs ◽  
Activity Based Costing ◽  
Platform Design ◽  
Product Families ◽  
Inference System ◽  
Design Changes ◽  
Wide Range ◽  
Functional Part

Additive manufacturing (AM) has evolved from prototyping to functional part fabrication for a wide range of applications. Challenges exist in developing new product design methodologies to utilize AM-enabled design freedoms while limiting costs at the same time. When major design changes are made to a part, undesired high cost increments may be incurred due to significant adjustments of AM process settings. In this research, we introduce the concept of an additive manufactured variable product platform and its associated process setting platform. Design and process setting adjustments based on a reference part are constrained within a bounded feasible space (FS) in order to limit cost increments. In this paper, we develop a cost-driven design methodology for product families implemented with additive manufactured variable platforms. A fuzzy time-driven activity-based costing (FTDABC) approach is introduced to estimate AM production costs based on process settings. Time equations in the FTDABC are computed in a trained adaptive neuro-fuzzy inference system (ANFIS). The process setting adjustment's FS boundary is identified by solving a multi-objective optimization problem. Variable platform design parameter limitations are computed in a Mamdani-type expert system, and then used as constraints in the design optimization to maximize customer perceived utility. Case studies on designing an R/C racing car family illustrate the proposed methodology and demonstrate that the optimized additive manufactured variable platforms can improve product performances at lower costs than conventional consistent platform-based design.

scholarly journals Modeling of a re-heat two-stage adsorption chiller by AI approach

2018 ◽  
Vol 240 ◽  
pp. 05014 ◽  
Author(s):  
Jaroslaw Krzywanski ◽  
Karolina Grabowska ◽  
Marcin Sosnowski ◽  
Anna Żyłka ◽  
Karol Sztekler ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Thermal Conductance ◽  
Cooling Capacity ◽  
Design Parameters ◽  
Distinct Advantage ◽  
Two Stage ◽  
Inference System ◽  
Adsorption Chiller ◽  
Wide Range ◽  
Thermally Driven

A distinct advantage of adsorption chillers is their ability to be driven by heat of near ambient temperature. However the performance of the thermally driven adsorption systems is lower than that of other heat driven heating/cooling systems. It is the result of a poor heat transfer coefficient between the bed and the immersed heating surfaces of a built-in heat exchanger system. The aim of this work is to study the effect of thermal conductance values as well as other design parameters on the performance of a re-heat two-stage adsorption chiller. One of the main energy efficiency factors in cooling production, i.e. cooling capacity (CC) for wide-range of both design and operating parameters is analyzed in the paper. Moreover, the work introduces artificial intelligence (AI) approach for the optimization study of the adsorption cooler. The Adaptive Neuro – Fuzzy Inference System (ANFIS) was employed in the work. The developed ANFIS model can be applied for optimizations purposes and may constitute a submodel or a separate module in engineering calculations, capable to predict the CC of the re-heat two-stage adsorption chiller.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Real-Time Control of a Rotary Inverted Pendulum using Robust LQR-based ANFIS Controller

2018 ◽  
Vol 19 (3-4) ◽  
pp. 379-389 ◽  
Author(s):  
Ishan Chawla ◽  
Ashish Singla
Keyword(s):  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Inference System ◽  
Wide Range ◽  
Lqr Controller ◽  
Rotary Inverted Pendulum

AbstractFrom the last five decades, inverted pendulum (IP) has been considered as a benchmark problem in the control literature due to its inherit nature of instability, non-linearity and underactuation. Its applicability in wide range of practical systems, demands the need of a robust controller. It is found in the literature that wide range of controllers had been tested on this problem, out of which the most robust being sliding mode controller while the most optimal being linear quadratic regulator (LQR) controller. The former has a problem of discontinuity and chattering, while the latter lacks the property of robustness. To address the robustness issue in LQR controller, this paper proposes a novel robust LQR-based adaptive neural based fuzzy inference system controller, which is a hybrid of LQR and fuzzy inference system. The proposed controller is designed and implemented on rotary inverted pendulum. Further, to validate the robustness of proposed controller to parametric uncertainties, pendulum mass is varied. Simulation and experimental results show that as compared to LQR controller, the proposed controller is robust to variations in pendulum mass and has shown satisfactory performance.

Modelling of laser processing cut quality by an adaptive network-based fuzzy inference system

2009 ◽  
Vol 223 (10) ◽  
pp. 2369-2381 ◽  
Author(s):  
Sivarao Subramonian ◽  
P Brevern ◽  
N S M El-Tayeb ◽  
V C Vengkatesh
Keyword(s):  
Surface Roughness ◽  
Pressure Vessel ◽  
Fuzzy Inference System ◽  
Laser Processing ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Design Parameters ◽  
Adaptive Network ◽  
Inference System ◽  
Cut Quality

Real-world problems in precision machining now require intelligent systems that integrate knowledge, techniques, and methodologies. Intelligent systems possess human-like expertise within a specific domain to adapt themselves and to learn to do better in making decisions for an intelligent manufacturing system. An intelligent tool called adaptive network-based fuzzy inference system (ANFIS) was used to model and predict the laser cut quality of a 2.5 mm manganese—molybdenum (Mn—Mo) alloy pressure vessel plate in this article. A 3 kW CO2 laser machine with seven selected design parameters was used to carry out 128 experiments based on 2 k factorial design with single replication. Because surface roughness (Ra) was the response parameter, it was targeted to be <15 μm to meet the requirement and benchmark of the pressure vessel manufacturer who sponsored this project. The DIN 2310-5 German laser cutting of metallic materials standard and procedure was referred to for evaluating surface roughness, where experimentally obtained results were used for Ra predictive modelling. Predictions of non-linear laser processing by ANFIS were found to be extremely promising in supplying the desired output, where Ra was predicted to an excellent degree of accuracy, reaching almost 70 per cent with the experimental pure error below 30 per cent.

scholarly journals Reduction of Crude Oil Production Cost in Nigerian Indigenous Company using Activity Based Costing

2019 ◽  
Vol 4 (1) ◽  
pp. 49-53 ◽  
Author(s):  
Segun Adebisi Osetoba ◽  
Nkoi Barinyima ◽  
Rex Amadi
Keyword(s):  
Crude Oil ◽  
Cost Reduction ◽  
Production Cost ◽  
Value Added ◽  
Oil Production ◽  
Activity Based Costing ◽  
Service Company ◽  
Crude Oil Production ◽  
The Cost ◽  
The Impact

The aim of this study is to investigate the impact of activity based costing in reducing crude oil production cost in Nigerian indigenous oil and gas company. This research work identified strategies to effectively reduce the cost of crude oil production by adopting a cost reduction tool for crude oil production and to establish a good crude oil flow to the surface for production. Activity based costing was the cost reduction tool used for this work. The tool helps to differentiate between value added costing and non-value added costing. Non-value added costs must be reduced or eliminated during production so as to maximise profit. Data was collected from an indigenous oil service company. The collated data were tabulated and graphs were plotted with the aid of Microsoft excel. The analysis revealed a total sum of ₦ 416,978,977 was wrongly spent for a duration of three years on crude oil production due to non-value added costing. The activities are: poor transportation of crude oil, that is, use of mobile tanker for haulage instead of laying 4 inches coated pipes for a distance of 5km and contracting the treatment of produced water to a contractor instead of setting up a water treatment plant. Also, using a diesel engine generator for electric power supply while gas was available as a fuel gas for natural gas consuming generator was a non-value added activity. Lastly, inadequate oil well flowing practice by flowing the well through an adjustable choke for a long period of time instead of using a fixed choke. This is a huge loss for indigenous oil producing fields operated by an indigenous oil service company in Nigeria. The loss was due to inability of the producers/field location owners to set up few equipment to meet up with complete operation standard.

scholarly journals COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals DETERMINATION OF BREAK-EVEN POINT BY USING ABC METHOD ON AGRICULTURAL COMMODITIES (TOBACCO) IN MADURA

2020 ◽  
Vol 9 (2) ◽  
pp. 96
Author(s):  
Ach Baihaki ◽  
Hanafi Hanafi
Keyword(s):  
Qualitative Research ◽  
Production Cost ◽  
Agricultural Commodities ◽  
Activity Based Costing ◽  
Dry Seasons ◽  
Descriptive Approach ◽  
Abc Method ◽  
Break Even Point ◽  
The Cost

<p class="JurnalASSETSABSTRAK"><strong>ABSTRACT</strong></p><p>This study aims to determine the BEP of Maduranesse tobacco by using an activity-based costing method (ABC). The present research is qualitative research with a descriptive approach by using primary based on the expenditure of factors of production by farmers. The result pointed out the expenditure until harvest is equivalent to the cost from harvest to sold out, so by using ABC, production cost that is a baseline to determine BEP will be decreased by efficiency in cost, eliminating several activities, and timing to plant before dry seasons.</p><p class="JurnalASSETSABSTRAK"><strong><em>ABSTRAK</em></strong><em></em></p><p><em>Penelitian ini bertujuan untuk mengetahui Harga BEP komoditas tembakau Madura berdasarkan activity based costing (ABC). Jenis penelitian adalah penelitian kualitatif dengan pendekatan deskriptif dengan menggunakan data primer berdasarkan pengorbanan faktor produksi yang dimiliki petani. Hasil penelitian menunjukkan biaya yang dikorbankan petani sampai siap panen bahwa biaya sampai panen dilakukan itu hampir sama dengan biaya mulai panen hingga pasca panen, sehingga dengan menggunaka ABC, biaya produksi yang menjadi dasar penentuan BEP bisa ditekan dengan melakukan efisiensi biaya, menghilangkan beberapa aktivitas, dan waktu penanaman pada saat menjelang musim kemarau.</em></p>

scholarly journals ANFIS for Tamil Phoneme Classification

2019 ◽  
Vol 8 (6) ◽  
pp. 2907-2914
Keyword(s):  
Neural Networks ◽  
Markov Models ◽  
Fuzzy Inference ◽  
Hybrid Architecture ◽  
Phoneme Recognition ◽  
Inference System ◽  
Phoneme Classification ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Non Linear Systems

Phoneme recognition is an intricate problem lying under non-linear systems. Most research in this area revolve around try to model the pattern of features observed in the speech spectra with the use of Hidden Markov Models (HMM), various types of neural networks like deep recurrent neural networks, time delay neural networks, etc. for efficient phoneme recognition. In this paper, we study the effectiveness of the hybrid architecture, the Adaptive Neuro-Fuzzy Inference System (ANFIS) for capturing the spectral features of the speech signal to handle the problem of Phoneme Recognition. In spite of a wide range of research in this field, here we examine the power of ANFIS for least explored Tamil phoneme recognition problem. The experimental results have shown the ability of the model to learn the patterns associated with various phonetic classes, indicated with recognition improvement in terms of accuracy to its counterparts.

Sign in / Sign up

Export Citation Format

Share Document