Prediction of forces during drilling of composite laminates using artificial neural network: A new approach

Dhawan, Vikas; Debnath, Kishore; Singh, Inderdeep; Singh, Sehijpal

doi:10.5937/fmet1601036d

Cited by 17 publications

(7 citation statements)

References 27 publications

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“…A similar type of trend of flexural modulus and strength has been reported by other researchers also. The flexural strength mainly depends upon the percentage of reinforcement, 28 type of surface treatment of fibers, 29 and processing techniques used to develop these green composites.…”

Section: Resultsmentioning

confidence: 99%

Influence of fiber volume fraction and curing temperature on mechanical properties of jute/PLA green composites

Singh

Dhawan³

2019

Polymers and Polymer Composites

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Rising environmental concerns and depletion of petrochemical resources have resulted in an increased interest in biodegradable natural fiber-reinforced polymer composites. In this research work, jute fiber has been used as a reinforcement and polylactic acid (PLA) as the matrix material to develop jute/PLA green composites with the help of compression molding technique. The effect of fiber volume fraction ranging from 25% to 50% and curing temperature ranging from 160°C to 180°C on different samples were investigated for mechanical properties and water absorption. Results obtained from various tests indicate that with an increase in the fiber volume fraction, tensile and flexural strength increases till 30% fiber fraction, thereafter decreases with further increase in fiber content. Maximum tensile and flexural strength of jute/PLA composites was obtained with 30% fiber volume fraction at 160°C curing temperature. The trend obtained from mechanical properties is further justified through the study of surface morphology using scanning electron microscopy.

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Section: Resultsmentioning

confidence: 99%

Influence of fiber volume fraction and curing temperature on mechanical properties of jute/PLA green composites

Singh

Dhawan³

2019

Polymers and Polymer Composites

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“…of the Object System of the CPS, in Figure 2. The Learning, similarly with the CPS 0 model, aggregating the past, present and predicted data and exploring advanced algorithms of artificial intelligence interface learning strategies [23], fuzzy logic ranking, multi-criteria-decision, deep learning and others, will support the learning capacity. The need of continuous learning (similarly to the CPS 0 models) comes from the permanent evaluation of results along the time, in which each state must be seen as a new instance off all system, having previous state as input, as well as new environment conditions, intended (forced variances) or not (disturbances).…”

Section: Cps Logical Architecturementioning

confidence: 99%

What is a Cyber-Physical System: Definitions and models spectrum

Putnik¹,

Ferreira²,

Lopes³

et al. 2019

FME Transactions

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Each time a relevant proposal occurs, existing perspectives, concepts or fundamentals are confronted by emergent ones. The Industry 4.0 and its promoted production control systems based on Cyber-Physical Systems (CPS), made splash new potentials for the binomial human and technology (equipments and its settings). Several authors explore the envisaged required more physical and digital connection to announce interesting transformative changes, where selfconfigure and adaptive machines sustain the application of corrective decisions. This paper exposes a spectrum of existing CPS definitions and models and contributes with the fundamentals for an effective intelligent CPS (I-CPS), where a double loop learning process, allows its supporting software algorithms to be changed or reprogrammed. Instead, not only selfconfigure machines but its configuring software, too.

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“…Marichamy [15] optimized machining parameters for α -β brass manufactured by stir casting process using Taguchi method for abrasive water jet machining. Dhawan [16] developed a new ANN approach to predict drilling-induced thrust force and torque. Sivaiah [17] tested the cryogenic machining of stainless steel; from the ANOVA analysis feed rate is the most influencing process parameter on the performance characteristics.…”

Section: Introductionmentioning

confidence: 99%

Machining parameter optimization of Al/SiC/Gr hybrid metal matrix composites using ANOVA and grey Relational analysis

Elango¹,

Annamalai²

2020

FME Transactions

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This research work focuses on the impact of graphite addition to the Al/SiC hybrid metal matrix composites manufactured by stir casting process. Three specimens were prepared with Al6061 alloy + 5 wt. % SiC and varying weight percent of graphite. Al6061 matrix material is suitable for machining and welding applications commonly used in construction of aircraft wings and fuselages. Machining of composite materials is often required to meet dimensional tolerances, surface roughness and to create holes. A layered SiC/Gr composite material is used in gas turbine combustor can. Machining of Al/SiC/Gr hybrid composite was done in Esteem ETM 356 lathe using tungsten carbide insert. Optimization of three machining parameters cutting speed, feed rate and depth of cut, was done using four levels. From ANOVA results feed rate (35.83%) is the important factor influencing surface roughness. Grey Relational analysis was done for simultaneous improvement of Material Removal Rate and surface roughness.

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Prediction of forces during drilling of composite laminates using artificial neural network: A new approach

Abstract: Drilling of fiber-reinforced plastics (FRP's) is an inevitable machining

Cited by 17 publications

References 27 publications

Influence of fiber volume fraction and curing temperature on mechanical properties of jute/PLA green composites

Influence of fiber volume fraction and curing temperature on mechanical properties of jute/PLA green composites

What is a Cyber-Physical System: Definitions and models spectrum

Machining parameter optimization of Al/SiC/Gr hybrid metal matrix composites using ANOVA and grey Relational analysis

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