Manufacturing Problems Research Articles

Combinatorial Benders decomposition for single machine scheduling in additive manufacturing with two-dimensional packing constraints

This paper addresses a single-machine scheduling problem in additive manufacturing (AM). We focus on the direct metal laser sintering (DMLS) technology of an AM process, where parts can be produced simultaneously in a batch. In this problem, we consider simultaneously the assignment of parts to batches and the packing of parts onto two-dimensional surfaces of the build chamber to minimize the makespan objective. To solve this problem, a mixed integer linear programming model is formulated, and an approximation algorithm is proposed, followed by an exact algorithm based on combinatorial Benders decomposition method with various Benders cuts and acceleration strategies (Algorithm CBD). Finally, we conduct a comprehensive computational study to verify the efficiency of our proposed Algorithm CBD. The computational results show that our proposed Algorithm CBD can effectively solve the test instances with different sizes.

Flexible job shop scheduling via deep reinforcement learning with meta-path-based heterogeneous graph neural network

The flexible job shop scheduling problem (FJSP) is an important production scheduling problem in intelligent manufacturing. How to model the complex FJSP more accurately and improve the efficiency and generalization of scheduling policies is an urgent challenge to be solved. Therefore, a new end-to-end deep reinforcement learning (DRL) method combined with the meta-path-based heterogeneous graph neural network (MHGNN) is proposed to effectively solve FJSP. The task of solving FJSP is decomposed into two subtasks of operation selection and machine allocation. This dual-task FJSP is represented by introducing a heterogeneous graph and modeled as the dual Markov decision process (DMDP). A MHGNN is proposed to embed the global scheduling states of the dual-task FJSP. A heterogeneous graph neural network (GNN) framework is designed for the dual-task FJSP to efficiently encode the operation nodes and machine nodes, where the extracted embedded feature information is used to represent the operation selection policy and the machine allocation policy. Two policy networks are designed to efficiently predict the operation selection policy and the machine allocation policy, and a soft double-actors critic algorithm is proposed to train these two policy networks, where the trained policies can be used to efficiently solve FJSP instances of different scales. The experiments on three public benchmarks show that the proposed method outperforms the well-known heuristic scheduling rules and some advanced methods for solving FJSP. In particular, when solving large-scale FJSPs, the proposed method performs more prominently in terms of solution quality and solution time.

The Bees Algorithm and Its Applications in Production and Manufacturing

With the advent of the Fourth Industrial Revolution, production and manufacturing processes and systems have become more complex. Obtaining the best performance from them requires efficient and effective optimisation techniques that do not depend on the availability of process or system models. Such models are usually either not obtainable or mathematically intractable due to the high degrees of nonlinearities and uncertainties in the processes and systems to be represented. The Bees Algorithm is a powerful swarm-based intelligent optimisation metaheuristic inspired by the foraging behaviour of honeybees. The algorithm is conceptually elegant and extremely easy to apply. It has attracted users from virtually all fields of engineering and natural, physical, medical and social sciences. This article reviews the original Bees Algorithm and some of its recent enhancements and gives examples of its applications to optimisation problems in production and manufacturing. The aim is to demonstrate the simplicity, effectiveness and versatility of the algorithm and encourage its further adoption by engineers and researchers across the world to realise smart and sustainable manufacturing and production in the age of Industry 4.0 and beyond.

Intelligent Engines: Revolutionizing Manufacturing and Supply Chains with AI

Artificial intelligence (AI) technologies are becoming a reality, with intelligent engines that can learn and simulate human thinking. These engines have three key features: micro-level intelligence with sensors, logic-based intelligence with software tools, and the ability to adapt and learn using algorithms. AI reduces the need for human intervention and cognitive thinking, finding more efficient solutions to complex problems in manufacturing and supply chain industries. AI simulates human cognition using software tools, allowing for the automation of tasks and analysis of complex systems. However, it raises questions about whether problems can be solved differently and the limitations of explicit algorithms.

Gas-particle-heat dynamic coupling simulation in directed energy deposition

Powder flow can affect the temperature variations in directed energy deposition (DED). However, the direct coupling mechanism remains unknown. To solve this problem, the heat and mass transfer in additive manufacturing was simulated using dynamic coupling. The interactions between the multiphase flow and heat transfer were established. A comparison with experiment shows that the accuracy of the predictions of the numerical simulation regarding powder size distributions and temperature increases is higher than 95 %. The average temperature increase of the metal powders with different weight functions was highly consistent in the simulation process. As the powder size increases, the average temperature of the powder on the printing plane decreases. This was the reason for the formation of a deeper melt pool in the case of smaller particles in the experiment. The different curvatures between the particle surface and melt pool surface lead to a decreased energy absorption efficiency in DED. The relationship between the powder features and the melt pool size was studied. A decrease in the powder flow rate increased the temperature, leading to a larger melt depth.

Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence-dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

Mechanical Behavior and Shape‐Memory Effects of NixTi1−x Alloys with Tunable Mixing of Ni/Ti Powders by Laser‐Directed Energy Deposition

As it is known, controlling NiTi alloy's microstructure is an urgent problem in additive manufacturing, which directly affects the mechanical properties of the alloy. In laser‐directed energy deposition (LDED) technology, the simultaneous powder feeding equipment with double powder barrels can effectively control the material ratio and realize the generation of NixTi1−x alloys with different atomic ratios. First, the performance differences of NiTi alloy under different thermal treatments are explored. Based on this, the optimal thermal treatment process is used to adjust the microstructure of NiTi alloy fabricated by LDED by changing the Ni/Ti atomic ratio to improve mechanical properties. The results indicate that Ni4Ti3 precipitates can induce the R phase, change the phase transition, and improve the mechanical properties in Ni‐rich NiTi alloys. Besides, the mechanical properties of NixTi1−x alloy can be enhanced by adjusting the Ni/Ti atomic ratio to change the content of Ni4Ti3 precipitates. Ni52Ti48 alloy has a tensile strength of 771 MPa, an elongation of 11.6%, and a recovery rate of 95.2%.

Continuous fiber path optimization in additive manufacturing: A gradient-based B-spline finite element approach

Additive manufacturing of continuous fiber enables the control of local material distribution and mechanical characteristics. This localized control is realized through fiber path generation and optimization, which is one of the core problems in the additive manufacturing of continuous fiber. Current fiber path generation and optimization methods mostly try to infill topology-optimized parts or align the fiber with the maximum stress directions, which may lead to sub-optimality or time-consuming computations. This paper provides a different approach to optimize the fiber path directly by parameterizing it with B-splines and manipulating its control points. A finite element model with B-spline fiber parameterization is established, with an analytical fiber-in-element probability. This framework streamlines the computation of local and global stiffness and mass matrices, enabling effective and efficient prediction of loading responses, stiffness, and natural frequencies. The probability framework also allows efficient calculation of the gradients of the stiffness and mass matrices, leading to a gradient-based fiber path optimization. The effectiveness and accuracy of both the finite element and optimization methods are verified with simulation and experiment case studies. Results demonstrate enhancements in stiffness and strength with less fiber usage compared to a commonly adopted principal stress direction method.

Improving damping characteristics of epoxy by adding copper alloy wastes

Purpose The purpose of this study is to get benefits from manufacturing harmful wastes is by using them as a reinforcement with epoxy matrix composite materials to improve the damping characteristics in applications such as machine bases, rockets, satellites, missiles, navigation equipment and aircraft as large structures, and electronics as such small structures. Vibration causes damaging strains in these components. Design/methodology/approach By adding machining chips with weight percentages of 5, 10, 15 and 20 Wt.%, with three different chip lengths added for each percentage (0.6, 0.8 and 1.18 mm), the three-point bending and damping characteristics tests are utilized to examine how manufacturing waste impacts the mechanical properties. Following that, the optimal lengths and the chip-to-epoxy ratio are determined. The chip dispersion and homogeneity are assessed using a field emission scanning electron microscope. Findings Waste copper alloys can be used to enhance the vibration-dampening properties of epoxy resin. The interface and bonding between the resin and the chip are crucial for enhancing the damping capabilities of epoxy. Controlling the flexural modulus by altering the chip size and quantity can change the damping characteristics because the two variables are inversely related. The critical chip size is 0.8 mm, below which smaller chips cannot evenly transfer, and disperse the vibration force to the epoxy matrix and larger chips may shatter and fracture. Originality/value The main source of problems in machine tools, aircraft and vehicle manufacturing is vibrations generated in the structures. These components suffer harmful strains due to vibration. Damping can be added to these structures to get over these problems. The distribution of energy stored as a result of oscillatory mobility is known as damping. To optimize the serving lifetime of a dynamic suit, this is one of the most important design elements. The use of composites in construction is a modern method of improving a structure's damping capacity. Additionally, it has been demonstrated that composites offer better stiffness, strength, fatigue resistance and corrosion resistance. This research aims to reduce the vibration effect by using copper alloy wastes as dampers.

Improving the interfacial adhesion between recycled carbon fibres and polyphenylene sulphide by bio-inspired dopamine for advanced composites manufacturing

Polyphenylene sulphide composites with recycled carbon fibres are of high interest due to their promising mechanical properties. Poor interface adhesion between recycled carbon fibre and thermoplastic matrix, however, remains a major problem in composite manufacturing. Herein, a novel one-step method using bio-inspired copolymerisation of dopamine to graft silica nanoparticles onto the carbon fibre surface is reported. Comprehensive investigations were conducted to fabricate the treated carbon fibre and characterise the modified samples by analysing the surface morphology and functional groups. The experimental results reveal that the network of bio-inspired adhesive polydopamine and nanoparticles adhering to the fibres can improve inter-laminar shear strength by 28.4% in the resulting composites. In addition, dynamical mechanical analysis results prove that the interfacial bonding at the fibre/matrix interface provides good resistance to thermal cycling. This novel approach introduces the functional groups on the recycled carbon fibre surface, leading to an improvement of surface energy and wettability between the carbon fibres and polyphenylene sulphide matrix and constitutes a robust and green approach to the manufacturing of advanced composites.

A Fusion of Chinese Five Elements and Steampunk in Enhancing the Gaming Experience

In the present era, due to the increasing influence of globalization, individuals from around the globe have the opportunity to select and engage with a greater variety of cultures. further, the theory of cultural fusion was proposed and further expanded upon during a period when video games were increasingly delving into the concept of cultural fusion. Nevertheless, the number of games that successfully blend Chinese and Western cultures is limited. Furthermore, the incorporation of traditional culture in game design tends to be simplistic in terms of visuals and gameplay. To address this, we have developed a game called Element Gears: Chronicles of Balance. This 2D survival adventure game combines traditional Chinese five-element philosophy with steampunk aesthetics, creating a new benchmark for thematic and cultural integration. Our game Gears integrates cultural elements and futuristic ideas into both the gameplay and narrative, rather than merely focusing on them. This fusion is not only visually pleasing, but also intricately integrated into the game mechanics, encompassing aspects such as resource allocation, manufacturing, and strategic survival problems within a dynamically evolving setting. The incorporation of cultural topics extends beyond surface-level depictions, offering players a more profound and engrossing educational and entertainment encounter. By comparing our Element Gears to similar games in the genre, this article highlights how its innovative approach to cultural fusion increases player engagement and creates a deeper and more meaningful gaming experience.

A novel geometric predictive algorithm for assessing compressive elastic modulus in MEX additive processes, based on part nonlinearities and layers stiffness, validated with PETG and PLA materials

MEX (Material Extrusion) is an intrusive technological process that inherently induces alterations in the elastic and mechanical parameters of plastic materials. Manufacturers provide initial mechanical parameters for plastic filaments, which undergo modifications during MEX manufacturing, influenced by intrinsic manufacturing factors such as temperature and pressure changes, as well as geometric and technological parameters of the 3D additive process. These factors, compounded by the inherent geometric nonlinearities in plastic components, directly impact the post-manufacture mechanical and elastic properties of the material. Presently, material characterization in MEX manufacturing relies on manual experimental testing, necessitating new tests for any variation in manufacturing parameters. In this scenario of mechanical uncertainty, rigorously validating component behavior involves costly experimental trials. Intending to solve the problems of MEX components manufacturing, the paper presents an innovative methodology based on the use of a new predictive algorithm created by the researchers capable of obtaining the elastic modulus of a plastic material manufactured with MEX and its mechanical behavior in the elastic zone under compressive loads. The predictive algorithm only needs as input the compressive elastic modulus of the isotropic plastic material filament and the manufacturing parameters of the MEX process. The smart developed algorithm calculates the stiffness of each layer considering the number of holes in the projected area. The innovative predictive algorithm has been experimentally and numerically validated using PETG (Polyethylene Terephthalate Glycol) material and PLA (Polylactic Acid) on test specimens and on a case study of variable topology. The results show deviations from [0.2%–4.3%] for PETG and [0.4%] for PLA concerning the experimental tests and [1.1%–13.5%], to the numerical analyses. In this line, the presented algorithm greatly improves the results obtained by the simulation software since this software currently can not consider the geometric and technological parameters associated with the 3D manufacturing process of the component. The predictive algorithm is valid for each print pattern and manufacturing direction. The new algorithm improves the existing state of the art significantly since this algorithm extends its utility to most plastic polymer materials suitable for MEX 3D printing, provided that the mechanical and elastic properties of the filament are known. Its versatility extends to complex component geometries subjected to uniaxial compression loads, eliminating the need for mechanical analysis software or expensive experimental validations.

DSCU-Net: MEMS Defect Detection Using Dense Skip-Connection U-Net

With the rapid development of intelligent manufacturing and electronic information technology, integrated circuits play a vital role in high-end chips. The semiconductor chip manufacturing process requires precise operation and strict control to ensure chip quality. The traditional manual visual inspection method has a high workforce cost and intense subjectivity and is accompanied by a high level of misdetection and leakage. Computer vision-based wafer defect detection technology is gaining popularity in the industry. However, previous methods still find it challenging to meet the production requirements regarding accuracy. To solve the problem, we propose a defect detection network based on a coding and decoding structure, Dense Skip-Connection U-Net (DSCU-Net), which optimizes the skip connection between the encoder and decoder and enhances the profound fusion of high-level semantics and low-level semantics to improve accuracy. To verify the effectiveness of DSCU-Net, we validate it in actual microelectromechanical systems (MEMS) data, and the results show that DSCU-Net reaches an optimal level. Therefore, the DSCU-Net proposed in this paper effectively solves the defect detection problem in semiconductor chip manufacturing. This method reduces workforce cost and subjectivity interference and improves inspection efficiency and accuracy. It will help to promote further development in the field of intelligent manufacturing and electronic information technology.

Grain refinement of flux-cored wire arc additively manufactured duplex stainless steel through in-situ alloying of Ti

One of the major problems in the wire arc additive manufacturing (WAAM) of duplex stainless steel (DSS) is the epitaxial growth of coarsen columnar ferrite grains crossing the deposition layers, which leads to the mechanical property anisotropy and increases the risk of hot tearing. In this investigation, Ti was in-situ alloyed to the WAAM DSS to promote grain refinement of ferrite. The results revealed that the ferrite transformed from columnar to equiaxed morphology with the increase of Ti content. In the sample with 0.5%Ti addition, some fine equiaxed ferrite grains with an average grain size of 39 μm formed at the fusion line, which inhibited the epitaxial growth of columnar grains. With further increase of Ti addition to 1.0%, the ferrite grains in the as-deposited WAAM DSS completely transformed from columnar to equiaxed structure, with an average grain size of 12 μm at the fusion line and 19 μm at the interior of deposition layer. Ti2N-Al2O3 particles with an average diameter of 400 nm were found in the equiaxed grains at room temperature, which existed in the form of TiN-Al2O3 in the molten pool and promoted the heterogeneous nucleation of ferrite. The analyzation based on Interdependence Theory demonstrated that Ti refined the ferrite in WAAM DSS by promoting the formation rate of the constitutional supercooling zone ahead of the solid/liquid interface and by introducing effective nucleat particles into it.

The product–mold–machine manufacturing problem: Complexity, MILP models and constructive heuristics

This study addresses the product–mold–machine manufacturing problem, a problem intrinsic to the fabrication processes in industries such as tires, plastics, ceramics, and glass. Each product possesses distinct shape and mechanical attributes, resulting from processing its corresponding mold on a specific machine. Uniquely, machines in this problem can handle multiple molds concurrently, implying that their processing time is dictated by the longest mold processing time within the set being used. The goal is to devise a production strategy that reduces the overall periods required to satisfy each product’s demand. This has to be achieved under constraints like limited availability of machines and molds, changeover times, and incompatibility constraints. We establish the problem’s NP-complete nature in the strong sense. Further, we introduce two mixed-integer linear programming (MILP) models and two polynomial-time constructive heuristics. These heuristics offer upper bounds for the optimal makespan and present easily implementable feasible solutions. Our numerical tests, conducted on real-world data, evaluate the efficacy of different solution strategies, specifically those using the preliminary insights from our heuristics to guide the mathematical formulations. The outcomes strongly advocate for leveraging these feasible solutions as starting points when solving the MILP models. Additionally, the notable efficiency and solution quality of our heuristics spotlight them as potent alternatives, especially in settings where traditional optimization tools might be absent.

Results of EAHP’s 2023 shortages survey

Aims and objectiveThe aim of the European Association of Hospital Pharmacists (EAHP)’s 2023 shortages survey was to collect data on causes and mitigation strategies of shortages of medicines and medical...

Innovative Approaches to 3D Printing of PA12 Forearm Orthoses: A Comprehensive Analysis of Mechanical Properties and Production Efficiency.

This research paper aims to explore the mechanical characteristics of polyamide PA12 (PA12) as a 3D material printed utilizing Selective Laser Sintering (SLS) and HP MultiJet Fusion (HP MJF) technologies in order to design and manufacture forearm orthoses. The study assessed the flowability of the materials used and compared the mechanical performance of PA12 with each other using tensile, flexure, and impact tests in five different fabrication orientations: X, Y, Z, tilted 45° XZ, and tilted 45° YZ. The results of the study provide, firstly-the data for testing the quality of the applied polyamide powder blend and, secondly-the data for the design of the orthosis geometry from the aspect of its strength parameters and the safety of construction. The mechanical parameters of SLS specimens had less variation than MJF specimens in a given orientation. The difference in tensile strength between the 3D printing technologies tested was 1.8%, and flexural strength was 4.7%. A process analysis of the forearm orthoses revealed that the HP MJF 5200 system had a higher weekly production capacity than the EOS P396 in a production variance based on obtaining maximum strength parameters and a variance based on maximizing economic efficiency. The results suggest that medical device manufacturers can use additive manufacturing technologies to produce prototypes and small-batch parts for medical applications. This paper pioneers using 3D printing technology with Powder Bed Fusion (PBF) methods in designing and manufacturing forearm orthoses as a low- to medium-volume product. The applied solution addresses the problem of medical device manufacturers with regard to the analysis of production costs and mechanical properties when using 3D printing for certified medical devices.

Effects of Process Parameters in Thermoforming of Unidirectional Fibre-Reinforced Thermoplastics.

Process-induced defects during thermoforming are widespread problems in laminate manufacturing. The aim of this study is to describe the effects of holding time and pressure on several properties of the manufactured laminate. A design of experiments is performed, followed by an analysis of variance to examine significant effects. Subsequently, a regression model is created to predict the laminate's properties, which is also validated. A significant interaction between holding time and pressure is determined for the resulting tensile strength and elongation at break with a p-value of 1.52·10-16 and 0.02, respectively. The highest values of tensile strength and elongation at break are found for low settings of holding time and pressure. The fibre volume fraction is not affected by the process parameters. As holding time and pressure increase, significant fibre misalignment takes place, leading to a decrease of the mechanical properties. The regression model corresponds well with the validation and a tensile strength of 1049 MPa with an elongation at break of 2.3% is reached.

Case Article—Digitizing Spare Parts Supply Chain via 3D Printing: An Operational Cost Analysis

The case presents a sourcing problem and a manufacturing problem faced by an original equipment manufacturer, seeking recommendations for sourcing a diverse range of parts for high-voltage equipment, as well as making decisions on the manufacturing strategy for a component used in a water monitoring system. The case provides an opportunity to explore the qualitative and quantitative aspects of three-dimensional (3D) printing versus traditional manufacturing, specifically in terms of operational cost. Furthermore, this case facilitates discussions on the potential impact of 3D printing on supply chains. It is suitable for use in graduate and undergraduate courses, as it introduces key concepts such as manufacturing and inventory policies, queueing theory, and life cycle analysis. Ultimately, the case is designed to promote a deeper understanding of the challenges and opportunities that manufacturers face in today’s rapidly evolving technological landscape. Supplemental Material: The Teaching Note and supplemental data are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .