Proceedings of the Institution of Mechanical Engineers,Part B: Journal of Engineering Manufacture

Manufacturing systems involve a huge number of combinatorial problems that must be optimized in an efficient way. One of these problems is related to task scheduling problems. These problems are NP-hard, so most of the complete techniques are not able to obtain an optimal solution in an efficient way. Furthermore, most of real manufacturing problems are dynamic, so the main objective is not only to obtain an optimized solution in terms of makespan, tardiness, and so on but also to obtain a solution able to absorb minor incidences/disruptions presented in any daily process. Most of these industries are also focused on improving the energy efficiency of their industrial processes. In this article, we propose a knowledge-based model to analyse previous incidences occurred in the machines with the aim of modelling the problem to obtain robust and energy-aware solutions. The resultant model (called dual model) will protect the more dynamic and disrupted tasks by assigning buffer times. These buffers will be used to absorb incidences during execution and to reduce the machine rate to minimize energy consumption. This model is solved by a memetic algorithm which combines a genetic algorithm with a local search to obtain robust and energy-aware solutions able to absorb further disruptions. The proposed dual model has been proven to be efficient in terms of energy consumption, robustness and stability in different and well-known benchmarks.

Process management is considered to be an essential approach to improve the performance of an enterprise. The process of an engineering project is considered to be a formalised workflow accompanied by a set of decisions. With decisions being made by taking account of information from various sources, the operation and management of modern engineering projects has to deal with increasing amounts of dynamic and changing project information. Understanding and interpreting this information for use in process management can generate challenges in practice. This might be caused by constraints of time and resource, the distributed structure of the information and a lack of modelled domain knowledge. To address these challenges, the research described in this paper focuses on techniques that support automation of the process management of engineering projects, from a data-driven perspective. The research includes elements of process modelling, monitoring and evaluation of such projects, through a proposed automatic process analysis system. The proposed system works with live and historical data. Within this paper, the design and implementation of the system is described. The use of techniques such as autonomic computing, data mining and KM technologies are shown, and the system functionality is demonstrated through the use of a dataset from an aerospace organisation.

When a composite laminate is tailored to suit its design intent, it is possible to improve the individual ply shapes to reduce component mass. If the laminate is going to be manufactured using an automated deposition system such as an automated fibre placement machine, then the design of the laminate will also influence the material deposition speed. This article identifies methodologies for indicating the likely impact on automated manufacture at the design optimisation stage by evaluating the ratio of ply perimeter to ply surface area when the laminate is defined as a simplified array of cells which are filled or unfilled to create a two-dimensional representation of the ply shape. A set of recommendations are made for using the methodology for improving deposition speed.

Peripheral milling process productivity or quality can be improved by controlling either cutting force or contour error. While each means for improvement is often addressed individually, efforts to control both aspects simultaneously are less common in the literature. This article describes an approach to control both the contour error and force using an adaptive robust controller. The axes dynamic behavior and tool deflection are considered as the two major sources of error expressly considered in the control design and are embedded in a global task coordinate frame representation of contour error. The adaptive control component maintains high-performance control of both force and contour error in the presence of significant model error or external disturbances. The control approach is implemented on a three-axis machine tool for validation. Experimental results indicate that significant improvements to both contour error and force regulation have been achieved.

Automation, driven by informatics, enables manufacturing companies to increase productivity and meet market demands for cost-effective and high-quality products. However, many manufacturing operations across industry verticals continue to be manual even today. One such example is the manual assembly of the final trim and wheels in an automotive production line where there is heavy reliance on human decision-making pertaining to when, where and how to install components on and inside a constantly moving vehicle body. The main aim of this work is to develop a rule-based decision support system that will enable an automation solution to make human-like decisions in moving assembly operations. The wheel loading operation is chosen as a case study and a decision support framework and tool is developed and successfully tested using multiple assembly scenarios generated from experimental data provided by gaming interface sensors installed on the laboratory-based shopfloor. The resulting decision support system has the potential to enable the automation of moving assembly operations in various sectors of the manufacturing industry.

Due to the international business competition of modern manufacturing enterprises, production systems are forced to quickly respond to the emergence of changing conditions. Production control has become more challenging as production systems adapt to frequent demand variation. The neuroendocrine system is a perfect system which plays an important role in controlling and modulating the adaptive behavior of organic cells under stimulus using hormone-regulation principles. Inherited from the hormone-regulation principle, an adaptive control model of production system integrated with a backlog controller and a work-in-progress controller is presented to reduce backlog variation and keep a defined work-in-progress level. The simulation results show that the presented control model is more responsive and robust against demand disturbances such as rush orders in production system.

In order to improve the performance of the cutting tool, third-generation tools with multi-layered nanocoatings on the rake face are used. During machining, the chip–tool interactions depict that although the tool wear on the rake face is located in the close proximity of the cutting edge, that is, within 800 µm, all the commercially available cutting tools have the coatings on the entire rake face. Taking into account the tribological properties required by the rake face close to the cutting edge, that is, high wear resistance and low friction, this study makes an attempt to identify, characterize and locate the actual wear zones/regions in terms of hard and soft zones in the chip contact area of tungsten carbide (WC) inserts close to the cutting edge in turning. Mamdani fuzzy inference system model was developed, trained with the sample experimental data and tested with the test data. The simulated results showed that the average error values of edge chipping (in X- and Y-directions), nose damage and crater wear (in X- and Y-directions) are about 2.37%, 3.01%, 2.86%, 2.66% and 1.89%, respectively. The fuzzy model developed in this study showed remarkable prediction of the wear zone locations and is also helpful for the researchers to decide the type of coating (hard and soft) along the specified zones for reducing the cost of production.

It is going to be increasingly important for manufacturing system designers to incorporate human activity data and ergonomic analysis with other performance data in digital design modelling and system monitoring. However, traditional methods of capturing human activity data are not sufficiently accurate to meet the needs of digitised data analysis; qualitative data are subject to bias and imprecision, and optically derived data are hindered by occlusions caused by structures or other people in a working environment. Therefore, to meet contemporary needs for more accurate and objective data, inertial non-optical methods of measurement appear to offer a solution. This article describes a case study conducted within the aerospace manufacturing industry, where data on the human activities involved in aircraft wing system installations was first collected via traditional ethnographic methods and found to have limited accuracy and suitability for digital modelling, but similar human activity data subsequently collected using an automatic non-optical motion capture system in a more controlled environment showed better suitability. Results demonstrate the potential benefits of applying not only the inertial non-optical method in future digital modelling and performance monitoring but also the value of continuing to include qualitative analysis for richer interpretation of important explanatory factors.

With the continuous innovation of technology, automated guided vehicles are playing an increasingly important role on manufacturing systems. Both the scheduling of operations on machines as well as the scheduling of automated guided vehicles are essential factors contributing to the efficiency of the overall manufacturing systems. In this article, a hormone regulation–based approach for on-line scheduling of machines and automated guided vehicles within a distributed system is proposed. In a real-time environment, the proposed approach assigns emergent tasks and generates feasible schedules implementing a task allocation approach based on hormonal regulation mechanism. This approach is tested on two scheduling problems in literatures. The results from the evaluation show that the proposed approach improves the scheduling quality compared with state-of-the-art on-line and off-line approaches.

The Arctic region is expected to play an extremely prominent role in the future of the oil and gas industry as growing demand for natural resources leads to greater exploitation of a region that holds about 25% of the world’s oil and gas reserves. It has become clear that ensuring the necessary reliability of Arctic industrial structures is highly dependent on the welding processes used and the materials employed. The main challenge for welding in Arctic conditions is prevention of the formation of brittle fractures in the weld and base material. One mitigating solution to obtain sufficiently low-transition temperatures of the weld is use of a suitable welding process with properly selected parameters. This work provides a comprehensive review with experimental study of modified submerged arc welding processes used for Arctic applications, such as narrow gap welding, multi-wire welding, and welding with metal powder additions. Case studies covered in this article describe welding of Arctic steels such as X70 12.7-mm plate by multi-wire welding technique. Advanced submerged arc welding processes are compared in terms of deposition rate and welding process operational parameters, and the advantages and disadvantages of each process with respect to low-temperature environment applications are listed. This article contributes to the field by presenting a comprehensive state-of-the-art review and case studies of the most common submerged arc welding high deposition modifications. Each modification is reviewed in detail, facilitating understanding and assisting in correct selection of appropriate welding processes and process parameters.

Difficulties in the grinding of Ti-6Al-4V originate from the three basic properties: poor thermal conductivity, high chemical reactivity and low volume specific heat of the material. Under severe grinding conditions, all these factors together lead to the accelerated wheel loading and redeposition of chips over the work surface. Redeposition and wheel loading have a significant effect on the surface finish, grinding forces, power consumption and wheel life. In this study, water-based Al₂O₃ nanofluid as metalworking fluid is applied during the surface grinding of Ti-6Al-4V under minimum quantity lubrication mode after dressing the wheel with different dressing overlap ratios. The severity of the redeposition over the work surface was observed by measuring various surface profiles taken perpendicular to the grinding direction at several locations on the ground surface. The nanofluid application was able to prevent redeposition over work surface that became evident from the measured surface finish parameters that results along the grinding direction. Coefficient of friction was estimated On-Machine using the measured forces for different wheel work speed ratios, depth of cut and dressing overlap ratios. The results showed the effectiveness of nanofluid in reducing friction at high material removal rate (i.e. high depth of cut and high speed ratio) conditions when compared to the dry environment. From the measured forces variation with respect to the number of passes, it became evident that, nanofluid application delayed the frequency of wheel loading and grit fracturing cycle, which leads to the increase in the wheel life.

Three different microstructures, namely ferrite–pearlite, tempered martensite and ferrite–bainite–martensite of 38MnSiVS5 microalloyed steel, were produced using controlled thermomechanical processing. The properties are comparable to quenched and tempered steel. The developed microstructures were turned to evaluate their machinability. Mixed modes of response were observed while ferrite–bainite–martensite microstructure exhibits better machinability by way of good surface texture/finish, the ferrite–pearlite microstructure of least strength encounters smaller cutting force.

This study focused on the effect of drilling parameters such as helix angle, spindle speed and feed rate on surface roughness, flank wear and acceleration of drill vibration velocity. Using design of experiments, 18 experiments were conducted on AISI 304 steel with carbide twist drill bits. A laser Doppler vibrometer was used for online acquisition of cutter vibration data in the form of acousto optic emission signals. A fast Fourier transformer was used to convert the time domain signals into frequency domain. Response surface methodology was used to identify significant parameters in the analysis of surface roughness, flank wear and acceleration of drill vibration velocity. A multi response surface optimization technique was used to find out optimum drilling parameters. Helix angle of 25°, feed rate of 10 mm and spindle speed of 750 r/min were found to be optimum cutting parameters for minimization of surface roughness, flank wear and acceleration of vibration velocity.

Among modern manufacturing methods, lean manufacturing has been a dominant method used in many industrial sectors. The principle of the lean manufacturing is to eliminate the wastes present in various forms of the industry. The adoption of lean manufacturing in an industry could be identified by the leanness measurement through lean assessment. In lean assessment, the evaluator plays a vital role. Selecting the right person for the assessment will reduce the ambiguity, time consumption and computation time. Decision-makers play a vital role in lean assessment. Few studies have been found in the literature to select or identify the decision-maker. In this article, the TOPSIS-Simos method has been proposed to identify a lean resourced employee in the industry. The proposed method is case studied in the manufacturing industries. The lean resourced employee has been identified after computation. The proposed method could be applied to identify the lean resourced employee in all types of manufacturing industries.

In this article, max–min ant colony optimization algorithm is proposed to determine how to allocate jobs and schedule tools with the objective of minimizing the makespan of processing plans in flexible manufacturing system. To expand the application range of max–min ant colony optimization algorithm, tool movement policy is selected as the running mode of flexible manufacturing system, which assumes that tools are shared among work centers and each operation is allowed to be machined by different kinds of tools. In the process of converting this scheduling problem into traveling salesman problem, disjunctive graph is modified to possess more than one path between each neighbor node. Besides providing practical methods of initializing pheromone, selecting node and calculating pheromone increment, max–min ant colony optimization algorithm employs the pheromone updating rule in max–min ant system to limit pheromone amount in a range, of which the upper and lower boundaries are updated after each iteration by formulations involving the current optimal makespan, the average number of optional tools and parameters. Finally, different sizes of processing plans are randomly generated, through which max–min ant colony optimization algorithm is proved effectively to tackle early stagnation and local convergence and thus obtains better solution than ant colony optimization algorithm and bidirectional convergence ant colony optimization algorithm.

This study proposes an adaptability index system for product development to enhance engineering system management. The index system takes into account the need to reduce the time required to market new products. The adaptability index is defined by the relationship between the time to develop a current product and the time to develop a subsequent (next) one, so that the recommended index will be useful when a new product with shorter execution times than those of the previous one is introduced into the product design and manufacturing process. Implementing an adaptability system’s parameters are derived from responsiveness, competitiveness, engineering, and marketing activities as reflected in adaptability indices. The study’s methodology is case study through questionnaires, interviews, and records. The authors recommend increasing adaptability by reducing complexity through computer-integrated manufacturing, standardization, and others methodologies. The significant outcome based on the case study is the assessment that is enabled by the proposed adaptability index. This index will guide engineering companies in the selection of the most relevant development processes, protocols, production tools, and procedures aimed at reducing the time-to-market required to generate new products, thus allowing it to meet market demand, increase product diversification, and reduce product lifetimes.