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Article
Publication date: 16 January 2017

Heping Chen, Jing Xu, Biao Zhang and Thomas Fuhlbrigge

High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time…

378

Abstract

Purpose

High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process.

Design/methodology/approach

In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed.

Findings

Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes.

Research limitations/implications

The proposed method is practical without any limitation.

Practical implications

The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production.

Originality/value

This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.

Details

Industrial Robot: An International Journal, vol. 44 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 20 December 2022

Janak Suthar, Jinil Persis and Ruchita Gupta

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and…

Abstract

Purpose

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

Design/methodology/approach

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

Findings

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

Originality/value

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 2 January 2018

Kush Aggarwal, R.J. Urbanic and Syed Mohammad Saqib

The purpose of this work is to explore predictive model approaches for selecting laser cladding process settings for a desired bead geometry/overlap strategy…

369

Abstract

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.

Details

Rapid Prototyping Journal, vol. 24 no. 1
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 2 January 2018

Zhonghua Li, Ibrahim Kucukkoc, David Z. Zhang and Fei Liu

Surface roughness is an important evaluation index for industrial components, and it strongly depends on the processing parameters for selective laser molten Ti6Al4V…

2962

Abstract

Purpose

Surface roughness is an important evaluation index for industrial components, and it strongly depends on the processing parameters for selective laser molten Ti6Al4V parts. This paper aims to obtain an optimum selective laser melting (SLM) parameter set to improve the surface roughness of Ti6Al4V samples.

Design/methodology/approach

A response surface methodology (RSM)-based approach is proposed to improve the surface quality of selective laser molten Ti6Al4V parts and understand the relationship between the SLM process parameters and the surface roughness. The main SLM parameters (i.e. laser power, scan speed and hatch spacing) are optimized, and Ti6Al4V parts are manufactured by the SLM technology with no post processes.

Findings

Optimum process parameters were obtained using the RSM method to minimise the roughness of the top and vertical side surfaces. Obtained parameter sets were evaluated based on their productivity and surface quality performance. The validation tests have been performed, and the results verified the effectivity of the proposed technique. It was also shown that the top and vertical sides must be handled together to obtain better top surface quality.

Practical implications

The obtained optimum SLM parameter set can be used in the manufacturing of Ti6Al4V components with high surface roughness requirement.

Originality/value

RSM is used to analyse and determine the optimal combination of SLM parameters with the aim of improving the surface roughness quality of Ti6Al4V components, for the first time in the literature. Also, this is the first study which aims to simultaneously optimise the surface quality of top and vertical sides of titanium alloys.

Details

Rapid Prototyping Journal, vol. 24 no. 1
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 11 September 2019

Swapnil Vyavahare, Soham Teraiya, Deepak Panghal and Shailendra Kumar

Fused deposition modelling (FDM) is the most economical additive manufacturing technique. The purpose of this paper is to describe a detailed review of this technique…

2615

Abstract

Purpose

Fused deposition modelling (FDM) is the most economical additive manufacturing technique. The purpose of this paper is to describe a detailed review of this technique. Total 211 research papers published during the past 26 years, that is, from the year 1994 to 2019 are critically reviewed. Based on the literature review, research gaps are identified and the scope for future work is discussed.

Design/methodology/approach

Literature review in the domain of FDM is categorized into five sections – (i) process parameter optimization, (ii) environmental factors affecting the quality of printed parts, (iii) post-production finishing techniques to improve quality of parts, (iv) numerical simulation of process and (iv) recent advances in FDM. Summary of major research work in FDM is presented in tabular form.

Findings

Based on literature review, research gaps are identified and scope of future work in FDM along with roadmap is discussed.

Research limitations/implications

In the present paper, literature related to chemical, electric and magnetic properties of FDM parts made up of various filament feedstock materials is not reviewed.

Originality/value

This is a comprehensive literature review in the domain of FDM focused on identifying the direction for future work to enhance the acceptability of FDM printed parts in industries.

Details

Rapid Prototyping Journal, vol. 26 no. 1
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 8 January 2020

Cassidy Silbernagel, Adedeji Aremu and Ian Ashcroft

Metal-based additive manufacturing is a relatively new technology used to fabricate metal objects within an entirely digital workflow. However, only a small number of…

Abstract

Purpose

Metal-based additive manufacturing is a relatively new technology used to fabricate metal objects within an entirely digital workflow. However, only a small number of different metals are proven for this process. This is partly due to the need to find a new set of parameters which can be used to successfully build an object for every new alloy investigated. There are dozens of variables which contribute to a successful set of parameters and process parameter optimisation is currently a manual process which relies on human judgement.

Design/methodology/approach

Here, the authors demonstrate the application of machine learning as an alternative method to determine this set of process parameters, the subject of this test is the processing of pure copper in a laser powder bed fusion printer. Data in the form of optical images were collected over the course of traditional parameter optimisation. These images were segmented and fed into a convolutional autoencoder and then clustered to find the clusters which best represented a high-quality result. The clusters were manually scored according to their quality and the results applied to the original set of parameters.

Findings

It was found that the machine-learned clustering and subsequent scoring reflected many of the observations which were found in the traditional parameter optimisation process.

Originality/value

This exercise, as well as demonstrating the effectiveness of the ML approach, indicates an opportunity to fully automate the approach to process optimisation by applying labels to the data, hence, an approach that could also potentially be suited for on-the-fly process optimisation.

Graphical abstract

Details

Rapid Prototyping Journal, vol. 26 no. 4
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 25 February 2022

Chun-Sheng Chen, Hai Wang, Yung-Chin Kao, Po-Jen Lu and Wei-Ren Chen

This paper aims to establish the predictive equations of height, area and volume of printed solder paste during solder paste stencil printing (SPSP) process in surface…

Abstract

Purpose

This paper aims to establish the predictive equations of height, area and volume of printed solder paste during solder paste stencil printing (SPSP) process in surface mount technology (SMT) to better understand the effect of process parameters on the printing quality.

Design/methodology/approach

An experiment plan is proposed based on the response surface method (RSM). Experiments with 30 different combinations of process parameters are performed using a solder paste printer. After printing, the volume, area and height of the printed SAC105 solder paste are measured by a solder paste inspection machine. Using RSM, the predictive equations associated with the printing parameters and the printing quality of the solder paste are formed.

Findings

The optimal printing parameters are 175.08 N printing pressure, 250 mm/s printing speed, 0.1 mm snap-off height and 15.7 mm/s stencil snap-off speed if the target height of solder paste is 100 µm. As the target printing area of solder paste is 1.1 mm × 1.3 mm, the optimized values of the printing parameters are 140.29 N, 100.52 mm/s, 0.63 mm and 20.25 mm/s. When both the target printing height and area are optimized together, the optimal values for the four parameters are 86.67 N, 225.76 mm/s, 0.15 mm and 1.82 mm/s.

Originality/value

A simple RSM-based experimental method is proposed to formulate the predictive polynomial equations for height, area and volume of printed solder paste in terms of important SPSP parameters. The predictive equation model can be applied to the actual SPSP process, allowing engineers to quickly predict the best printing parameters during parameter setting to improve production efficiency and quality.

Details

Soldering & Surface Mount Technology, vol. 34 no. 5
Type: Research Article
ISSN: 0954-0911

Keywords

Article
Publication date: 28 May 2021

Supphachai Nathaphan and Worrasid Trutassanawin

This work aims to investigate the interaction effects of printing process parameters of acrylonitrile butadiene styrene (ABS) parts fabricated by fused deposition modeling…

Abstract

Purpose

This work aims to investigate the interaction effects of printing process parameters of acrylonitrile butadiene styrene (ABS) parts fabricated by fused deposition modeling (FDM) technology on both the dimensional accuracy and the compressive yield stress. Another purpose is to determine the optimum process parameters to achieve the maximum compressive yield stress and dimensional accuracy at the same time.

Design/methodology/approach

The standard cylindrical specimens which produced from ABS by using an FDM 3D printer were measured dimensions and tested compressive yield stresses. The effects of six process parameters on the dimensional accuracy and compressive yield stress were investigated by separating the printing orientations into horizontal and vertical orientations before controlling five factors: nozzle temperature, bed temperature, number of shells, layer height and printing speed. After that, the optimum process parameters were determined to accomplish the maximum compressive yield stress and dimensional accuracy simultaneously.

Findings

The maximum compressive properties were achieved when layer height, printing speed and number of shells were maintained at the lowest possible values. The bed temperature should be maintained 109°C and 120°C above the glass transition temperature for horizontal and vertical orientations, respectively.

Practical implications

The optimum process parameters should result in better FDM parts with the higher dimensional accuracy and compressive yield stress, as well as minimal post-processing and finishing techniques.

Originality/value

The important process parameters were prioritized as follows: printing orientation, layer height, printing speed, nozzle temperature and bed temperature. However, the number of shells was insignificant to the compressive property and dimensional accuracy. Nozzle temperature, bed temperature and number of shells were three significant process parameters effects on the dimensional accuracy, while layer height, printing speed and nozzle temperature were three important process parameters influencing compressive yield stress. The specimen fabricated in horizontal orientation supported higher compressive yield stress with wide processing ranges of nozzle and bed temperatures comparing to the vertical orientation with limited ranges.

Article
Publication date: 12 January 2022

Bhanodaya Kiran Babu Nadikudi

The main purpose of the present work is to study the multi response optimization of dissimilar friction stir welding (FSW) process parameters using Taguchi-based grey…

Abstract

Purpose

The main purpose of the present work is to study the multi response optimization of dissimilar friction stir welding (FSW) process parameters using Taguchi-based grey relational analysis and desirability function approach (DFA).

Design/methodology/approach

The welded sheets were fabricated as per Taguchi orthogonal array design. The effects of tool rotational speed, transverse speed and tool tilt angle process parameters on ultimate tensile strength and hardness were analyzed using grey relational analysis, and DFA and optimum parameters combination was determined.

Findings

The tensile strength and hardness values were evaluated from the welded joints. The optimum values of process parameters were estimated through grey relational analysis and DFA methods. Similar kind of optimum levels of process parameters were obtained through two optimization approaches as tool rotational speed of 1150 rpm, transverse speed of 24 mm/min and tool tilt angle of 2° are the best process parameters combination for maximizing both the tensile strength and hardness. Through these studies, it was confirmed that grey relational analysis and DFA methods can be used to find the multi response optimum values of FSW process parameters.

Research limitations/implications

In the present study, the FSW is performed with L9 orthogonal array design with three process parameters such as tool rotational speed, transverse speed and tilt angle and three levels.

Practical implications

Aluminium alloys are widely using in automotive and aerospace industries due to holding a high strength to weight property.

Originality/value

Very limited work had been carried out on multi objective optimization techniques such as grey relational analysis and DFA on friction stir welded joints made with dissimilar aluminium alloys sheets.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 21 November 2018

Abdullah AlFaify, James Hughes and Keith Ridgway

The pulsed-laser powder bed fusion (PBF) process is an additive manufacturing technology that uses a laser with pulsed beam to melt metal powder. In this case, stainless…

Abstract

Purpose

The pulsed-laser powder bed fusion (PBF) process is an additive manufacturing technology that uses a laser with pulsed beam to melt metal powder. In this case, stainless steel SS316L alloy is used to produce complex components. To produce components with acceptable mechanical performance requires a comprehensive understanding of process parameters and their interactions. This study aims to understand the influence of process parameters on reducing porosity and increasing part density.

Design/methodology/approach

The response surface method (RSM) is used to investigate the impact of changing critical parameters on the density of parts manufactured. Parameters considered include: point distance, exposure time, hatching distance and layer thickness. Part density was used to identify the most statistically significant parameters, before each parameter was analysed individually.

Findings

A clear correlation between the number and shape of pores and the process parameters was identified. Point distance, exposure time and layer thickness were found to significantly affect part density. The interaction between these parameters also critically affected the development of porosity. Finally, a regression model was developed and verified experimentally and used to accurately predict part density.

Research limitations/implications

The study considered a range of selected parameters relevant to the SS316L alloy. These parameters need to be modified for other alloys according to their physical properties.

Originality/value

This study is believed to be the first systematic attempt to use RSM for the design of experiments (DOE) to investigate the effect of process parameters of the pulsed-laser PBF process on the density of the SS316L alloy components.

Details

Rapid Prototyping Journal, vol. 25 no. 1
Type: Research Article
ISSN: 1355-2546

Keywords

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