Search

The present study analyzes the important characteristics of plastic injection molding machining process. The polypropylene (PP) material has used as a specimen and effect of melt temperature, packing pressure and injection pressure has been investigated on the tensile modulus and elongation. Total 20 experiments have been performed to analyses the results. Response surface methodology (RSM) was adopted for optimization of injection molding process parameters. The experiments were conducted by using central composite design. The analysis of variance (ANOVA) techniques was used for selection of significant and non-significant parameters. The experimental results show that the RSM influence elongation by 87.04%, 11.52%, 1.43% and tensile modulus by 85.35%, 11.4%, 3.25%. Keywords: Injection molding; polypropylene; tensile modulus; elongation

The bat algorithm (BA) is one of prominent swarm-based algorithm that has the suitability in solving only small dimension engineering problems and suffers from drawback of getting trapped in local minimum with slow convergence for multi-dimensional problems. In the context of improving its applicability in solving large scale and constrained engineering design problems, this paper presents a novel upgraded bat algorithm with cuckoo search and Sugeno inertia weight (UBCSIW). In the proposed UBCSIW algorithm, first, the bat algorithm with its competence to exploit the optimal solutions in search space is combined with cuckoo search with its ability to explore best solution globally using Levy flight in the search space. Secondly, a new velocity and position search equation is incorporated in which the bat searches around the best candidate solution. This step helps in establishing adequate balance between exploration and exploitation capability and improving the performance effectively by employing greedy selection to choose the best candidate solution. Finally, Sugeno fuzzy inertia weight is introduced in the velocity updation equation, boosting the flexibility and diversity of bat population and results in stability of results. The effectiveness of the proposed UBCSIW algorithm is tested on 16 standard benchmark functions (unimodal and multimodal) with different dimensions, 12 CEC2015 test functions and 7 well-known constrained engineering design problems. The outputs of the proposed UBCSIW algorithm are validated by comparison with classical BA and other swarm-based state-of-the art algorithms. The simulation results show that proposed UBCSIW algorithm achieves highly competitive results in terms of higher optimization accuracy and improved convergence that outperforms basic BA in all twenty-eight test functions while performs better than other competitive algorithms in 24 functions (13 benchmark and 11 CEC2015 functions).

This paper presents a modified particle swarm optimization (MPSO) algorithm for the evaluation of geometric characteristics defining form and function of planar surfaces. The geometric features of planar surfaces are decomposed into four components; namely straightness, flatness, perpendicularity, and parallelism. A non-linear minimum zone objective function is formulated mathematically for each planar surface geometric characteristic. Finally, the result of the proposed method is compared with previous work on the same problem and with other nature inspired algorithms. The results demonstrate that the proposed MPSO algorithm is more efficient and accurate in comparison to other algorithms and is well suited for effective and accurate evaluation of planar surface characteristics.

Form error evaluation of manufactured parts is one of the crucial aspects of precision coordinate metrology. With the advent of technology, the noncontact data acquisition techniques are replacing the conventional machines like coordinate measuring machine (CMM). This paper presents an optimization technique to evaluate minimum zone form errors, namely straightness, circularity, flatness and cylindricity using constriction factor-based particle swarm optimization (CFPSO) algorithm. Addition of constriction factor helps in accelerating the convergence property of CFPSO. Initially, a simple minimum zone objective function is formulated mathematically for each form error and then optimized using the proposed CFPSO. Primarily, the results of the proposed method for form error evaluation are compared with the literature results. Furthermore, the data obtained from noncontact 3D scanner is processed and the results of form error evaluation using CFPSO algorithm are compared with Steinbichler’s INSPECT PLUS software results. It was found that the results obtained using the proposed CFPSO algorithm are fast and better as compared with other evolutionary techniques like genetic algorithm (GA), previous literatures and software results. Furthermore, to ensure effectiveness of the proposed method statistical analysis ([Formula: see text]-test) was performed. CFPSO results for large dimension of problem show significant difference in computation time as compared with GA. The CFPSO algorithm provides 27.25%, 7.5% and 6.38% improvements in circularity, flatness and cylindricity, respectively, in comparison to RE software results, for determination of minimum zone error. Thus, the methodology presented helps in improving the accuracy and for speeding up of the automated inspection process generally performed by CMMs in industries.

Presently, parts having intricate custom-made profiles are mostly fabricated using additive manufacturing (AM) processes. It becomes an essential task to verify the accuracy of AM parts so that they meet customer’s need and requirement. In product quality control, quick error comparison of manufactured part and an original CAD model is usually laborious but still a critical issue. Noncontact inspections using 3D scanners were preferred over conventional coordinate measurements, due to the significant amount of point data capturing in very short span of time. One of the important step of the noncontact inspection procedure using the 3D scanner is the correct localization of the datum reference frame. This step would help in the effective alignment of the digitized point data. This paper takes into consideration various 3-2-1 alignment approaches and investigates their influence on the inspection results. The result provides an evidence that an incorrect description of the product reference frame can lead to erroneous estimation of actual part deviations. The results show higher distance distribution for most of the point data of third alignment as the distance distribution is influenced by the worst description of the part reference frame. On the contrary, second alignment shows least distance distribution among point data due to correct reference frame definition.

Selection of material in engineering design process is a difficult and elusive task due to enormous number of dissimilar materials availability. For effective selection of materials, the designers have to take into account a number of definite qualitative and quantitative criteria. In the same context, this paper proposes a hybrid TOPSIS-PSI approach for effective material selection in marine applications. In this paper, the selection index value has been calculated by using logical combination of PSI and TOPSIS algorithm and these values have been ranked in ascending or descending order. The highest preference selection index value has been taken as the best alternative for the marine application. To prove the effectiveness of the proposed hybrid TOPSIS-PSI algorithm, two practical examples are considered and the result shows that the proposed procedure provides satisfactory results when compared with past literature. Furthermore, hybrid procedure is performed for selection of best wt.% combination among hybrid aluminum nanocomposites for marine applications based on its physical, mechanical and corrosive behavior. The result reveals that 9 wt.% and 6 wt.% reinforced hybrid aluminum nanocomposites have optimum combination of all physical, mechanical and corrosion properties, respectively according to hybrid TOPSIS-PSI approach.

Computer-aided tools help in shortening and eradicating numerous repetitive tasks that reduces the gap between digital model and actual product. Use of these tools assists in realizing free-form objects such as custom fit products as described by a stringent interaction with the human body. Development of such a model presents a challenging situation for reverse engineering (RE) which is not analogous with the requirement for generating simple geometric models. Hence, an alternating way of producing more accurate three-dimensional models is proposed. For creating accurate 3D models, point clouds are processed through filtering, segmentation, mesh smoothing and surface generation. These processes help in converting the initial unorganized point data into a 3D digital model and simultaneously influence the quality of model. This study provides an optimum balance for the best accuracy obtainable with maximum allowable deviation to lessen computer handling and processing time. A realistic non trivial case study of free-form prosthetic socket is considered. The accuracy obtained for the developed model is acceptable for the use in medical applications and FEM analysis.

A novel algorithm which is an ensemble of two metaphor-less algorithms is presented in this paper. The algorithm is inspired by Rao-1 and Jaya algorithms. Since the algorithm always plays around with the best and worst solutions; the algorithm is named as Best-Worst-Play (BWP) algorithm. The algorithm does not require any algorithm specific parameters, however, algorithm control parameters are required. To test the effectiveness and performance of the proposed algorithm, a number of unconstrained and constrained benchmark functions are considered. It is found that proposed algorithm has outperformed several well-established metaphor based algorithms. The proposed BWP algorithm may be used by researchers to solve the unconstrained and constrained optimization problems