Sequence of feasible mechanical assembly operations plays significant role in overall cost optimisation process for manufacturing industry and thus great importance is given to assembly sequence generation from past four decades. Though achieving at least one feasible sequence is focused in the earlier stages of research, the introduction of soft computing techniques attracted the industrial engineers towards cost-effective, optimised assembly sequences to attain economical manufacturing process. The integration of assembly sequence generation methods with computer aided design environment ensures more correctness and flexibility to automate the process. In this paper, a detailed review on various methods, their applications and limitations is presented and well discussed.

This review paper examines the chemical and mechanical properties of natural fiber reinforced polymer bonded composites and the processing techniques are compared for the reinforced composite materials. The chemical and mechanical properties of the different natural fibers composites were compared. Present days natural fibers are attracting many scholars and researchers due to its cost and largely available in nature also processing of these fibers is not hard in comparison to the conventional fiber's production. Also, Environmental awareness and a growing concern with the greenhouse effect have triggered the construction, automotive, and packing industries to watch out for ecofriendly materials that can replace conventional synthetic polymeric fiber's. Natural fibers seem to be a good alternate because they are readily available in fibrous form and can be extracted from herb leaves at very low costs. By these reasons the natural fibers are trusted over the regular fibers.

Purpose In the present era of Industry 4.0, the manufacturing automation is moving toward mass production and mass customization through human–robot collaboration. The purpose of this paper is to describe various human–robot collaborative (HRC) techniques and their applicability for various manufacturing methods along with key challenges. Design/methodology/approach Numerous recent relevant research literature has been analyzed, and various human–robot interaction methods have been identified, and detailed discussions are made on one- and two-way human–robot collaboration. Findings The challenges in implementing human–robot collaboration for various manufacturing process and the challenges in one- and two-way collaboration between human and robot are found and discussed. Originality/value The authors have attempted to classify the HRC techniques and demonstrated the challenges in different modes.

Purpose – The purpose of this paper is to find out the significant influence of assembly predicate consideration on optimal assembly sequence generation (ASG) in terms of search space, computational time and possibility of resulting practically not feasible assembly sequences. An appropriate assembly sequence results in minimal lead time and low cost of assembly. ASG is a complex combinatorial optimisation problem which deals with several assembly predicates to result an optimal assembly sequence. The consideration of each assembly predicate highly influences the search space and thereby computational time to achieve valid assembly sequence. Often, the ignoring an assembly predicate leads to inappropriate assembly sequence, which may not be physically possible, sometimes predicate assumption drastic ally raises the search space with high computational time. Design/methodology/approach – The influence of assuming and considering different assembly predicates on optimal assembly sequence generation have been clearly illustrated with examples using part concatenation method. Findings – The presence of physical attachments and type of assembly liaisons decide the consideration of assembly predicate to reduce the complexity of the problem formulation and overall computational time. Originality/value – Most of the times, assembly predicates are ignored to reduce the computational time without considering their impact on the assembly sequence problem irrespective of assembly attributes. The current research proposes direction towards predicate considerations based on the assembly configurations for effective and efficient ASG.

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The implementation of artificial intelligence techniques is increasing rapidly in recent years to solve numerous engineering problems. Assembly sequence planning is one of the prominent complex combinatorial problem draw attention of industrial engineers to economize the overall manufacturing cost by minimizing the assembly time and energy. Due to large search space and multiple assembly predicate criteria, researchers are motivated towards efficient utilization of AI techniques to address the problem. Literature review on various artificial intelligence techniques for obtaining the optimal assembly sequence planning are analyzed and the limitations of the existed methodologies are discussed in detail. This review provides an outlook for the researchers on various artificial intell1igent techniques which will be useful to carry out research for obtaining the optimum assembly sequence planning while qualifying various assembly predicate criteria.

Rapid prototyping uses layered manufacturing technology to produce functional parts directly from 3D computer-aided design model without involving any tools and human intervention. Due to layer by layer deposition, volumetric error remains in the part which is basically the volumetric difference between computer-aided design model and the fabricated part. This volumetric error causes poor dimensional accuracy and surface finish, which has limited the widespread applications of rapid prototyping. Although rapid prototyping is able to produce functional parts in less build time with less material wastage, today many industries are looking for better surface quality associated with these parts. Literature discloses that the part quality can be improved by selecting proper build orientation that corresponds to minimum volumetric error. In support of this, current study presents a computer-aided design-based novel methodology to precisely measure the volumetric error in layered manufacturing process, in particular fused deposition modeling process. The proposed method accepts computer-aided design model of the part in .CAT format and automatically calculates volumetric error for different build orientations. An Excel function is integrated with it to determine optimum build orientation based on minimum volumetric error. Several simple and complex examples verified the robustness of our proposed methodology. We anticipate that the current invention will help future rapid prototyping users in producing high-quality products through an intelligent process planning.

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