Component Lead Times Research Articles

Laser surface polishing of Ti-6Al-4V parts manufactured by laser powder bed fusion

Poor surface quality of Additively Manufactured (AM) components, can greatly increase the overall cost and lead time of high-performance components. Examples are medical devices where surfaces may contact the patient's skin and hence need to be smooth and aerospace components with high fatigue strength requirements where surface roughness could reduce fatigue life. The average surface roughness (Ra) of AM parts can reach high levels greater than 50 μm and maximum distance between the high peaks and the low valleys of more than 300 μm. As such, there is a need for fast, cost effective and selective finishing methods of AM produced components targeted at high-performance industries. In this paper Ti-6Al-4V Grade 23 ELI, popular for medical devices and aerospace parts production, was L-PBF processed to manufacture parts which were subsequently treated via laser polishing. Here in this work, CO2 laser polishing was used for the surface modification of the Ti-6Al-4V produced samples. The most significant processing parameters were optimised to achieve approximately an 80% reduction in the average surface roughness and a 90% reduction in the peak-to-valley distance with a processing time of 0.1 s/mm2 and cost of 0.2 €/cm2.

Additive manufacturing in maintenance engineering: repair and restoration through dld technology / Fabricação de aditivos em engenharia de manutenção: reparo e restauração através da tecnologia dld

Currently, repair and restoration through direct laser deposition (DLD) technology is one of the newest applications of metal additive manufacturing technology. This technology could revolutionize many industrial sectors, reducing material waste, component lead times and carbon footprint. However, studies on the use of additive manufacturing for repair and restoration through DLD technology are still scarce. This article aimed to identify and categorize the use of additive manufacturing in maintenance through DLD technology through a systematic literature review.

Large-scale metal additive manufacturing: a holistic review of the state of the art and challenges

ABSTRACT Additive Manufacturing (AM) has the potential to completely reshape the manufacturing space by removing the geometrical constraints of commercial manufacturing and reducing component lead time, especially for large-scale parts. Coupling robotic systems with direct energy deposition (DED) additive manufacturing techniques allow for support-free printing of parts where part sizes are scalable from sub-metre to multi-metre sizes. This paper offers a holistic review of large-scale robotic additive manufacturing, beginning with an introduction to AM, followed by different DED techniques, the compatible materials and their typical as-built microstructures. Next, the multitude of robotic build platforms that extend the deposition from the standard 2.5 degrees of freedom (DOF) to 6 and 8 DOF is discussed. With this context, the decomposition and slicing of the computerized model will be described, and the challenges of planning the deposition trajectory will be discussed. The different modalities to monitor and control the deposition in an attempt to meet the geometrical and performance specifications are outlined and discussed. A wide range of metals and alloys have been reported and evaluated for large-scale AM parts. These include steels, Ti, Al, Mg, Cu, Ni, Co–Cr and W alloys. Different post-processing steps, including heat treatments, are discussed, along with their microstructures. This paper finally addresses the authors' perspective on the future of the field and the largest knowledge gaps that need to be filled before the commercial implementation of robotic AM.

Innovative Activities of a Logistics Company in the Global Value Chain

Business model innovation brings new value and revenue to enterprises. For industries with competitive advantages, strengthening the value chain is an important matter. This study explores the workings of the logistics service providers in the bicycle industry in Taiwan who provide an innovative and integrated value chain to customers with new value propositions to create a win-win situation. The study found that logistics company understand the needs and problems of customers. They integrate upstream and downstream customers in the value chain and provide processing services for bicycle manufacturers. The logistics company play an extended machining service provider of upstream suppliers. The innovative business model shortens the lead time of key components from suppliers to bicycle manufacturers. Innovation has changed the old value chain model and has improved the service capabilities of the logistics company.

Supplier Replacement Model in a One-Level Assembly System under Lead-Time Uncertainty

Supplier selection/replacement strategies, purchasing price negotiation and optimized replenishment policies play a key role in efficient supply chain management in today’s dynamic market. Their importance increases even more in Industry 4.0. In this paper, we propose a joint model of replenishment planning and purchasing price negotiation in the context of supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. The real component lead times are stochastic. There is consequently a non-negligible risk that the assembly process may be stopped if all components for assembly are not delivered on the due date. This incurs inventory-related costs, holding and backlogging, which should be minimized. We consider a set of suppliers characterized by their prices and the probability distributions of their lead-times, and we present a model and an approach that optimize not only replenishment policy, but also purchasing prices. For a given unit, it is possible to model several alternative suppliers with alternative pricing and lead-time uncertainties, and evaluate their impacts on the total cost: composed of holding, backlogging and purchasing costs for the assembly system. The findings of this study indicate that it can be beneficial to pay suppliers an additional purchase cost in order to reduce the holding and backlogging costs related to uncertainty. In consequence, decision makers can use the proposed approach to negotiate prices and delivery delays or to select suppliers.

Laser metal deposition technique: sustainability and environmental impact

Additive manufacturing (AM) is a term used in describing a set of manufacturing techniques that employs layer upon layer production of parts and components through the application of 3D model data and inputs of raw material. The technology easily comes to mind in the recent times as processing complexities during production is partly responsible for the high cost of parts and components. Industries such as the heavy machinery consumers, aerospace and casting industry now employs the technology as a way of prolonging service of faulty parts through repair and remanufacturing technology. AM has the tendency to change many production set ups through reduction in cost of production, material wastage, energy usage, component lead time etc. AM technologies also have their own challenges despite numerous advantages associated with them. This paper looks at some of the laser metal deposition techniques, their sustainability and environmental impact.

A Primal-Dual Framework for Decomposition and Rounding Methods in ATO Systems

We study assemble-to-order (ATO) problems from the literature. ATO problems with general structure and integrality constraints are well known to be difficult to solve, and we provide new insight into these issues by establishing worst case approximation guarantees through primal-dual analyses and linear programming (LP) rounding. First, we relax the one-period ATO problem using a natural newsvendor decomposition and use the dual solution for the relaxation to derive a lower bound on optimal cost, providing a tight approximation guarantee that grows with the maximum product size in the system. Then, we present an LP rounding algorithm that achieves both asymptotic optimality as demand grows large, and a 1.8 approximation factor for any problem instance. In addition to theoretical guarantees, we perform comprehensive numerical simulations and find that our rounding algorithm outperforms existing techniques and is close to optimal. Finally, we demonstrate that our one-period LP rounding results can be used to develop an asymptotically optimal integral policy for dynamic ATO problems with backlogging and identical component lead-times.

Fatigue performance of additive manufactured TiAl6V4 using electron and laser beam melting

Increasingly, additive manufacturing is considered to reduce weight and lead time of aerospace components. Despite the high potential of this manufacturing technology, reliable mechanical properties are an essential prerequisite for future series production. Therefore, the aim of this work was to contribute to an improved understanding of the factors influencing the mechanical properties of laser powder bed and electron beam melted TiAl6V4. Therefore, tensile tests, fracture toughness tests, fatigue crack growth measurements for different r-ratios as well as high cycle fatigue properties were determined for different surface roughness and heat treatments.

Single-period inventory model for one-level assembly system with stochastic lead times and demand

Replenishment planning of an assembly system with one type of finished product assembled from different types of components is considered. The components are procured from diverse external suppliers to satisfy finished product demand. It is supposed that the component lead times and finished product demand are random discrete variables. The assembly company must determine what are the best quantities of components and when is the right time to order. The objective is to minimise the total cost which is composed of holding component costs, tardiness penalties, lost sales and surplus item costs for finished products. A single-period analytical model is proposed. Several properties of the objective function are proven. They are used to develop a Branch and Bound algorithm. Numerical tests for the algorithm are presented. Five heuristics based on Newsvendor model for lead time and demand are proposed and compared with the Branch and Bound algorithm. These tests show that the suggested Branch and Bound algorithm can solve large size problems within a short time. The proposed heuristics but one are not competitive with the Branch and Bound algorithm. The truncated version of Branch and Bound gives better results. The model suggested is better adapted to actual contract assembler environments, more realistic and can better approximate real-life industrial situations. The proposed exact algorithm provides optimal solutions for all discrete distributions of probabilities of lead times and demand. A new general approach to design such discrete optimisation algorithms is presented.

Asymptotically Optimal Inventory Control for Assemble-to-Order Systems with Identical Lead Times

Optimizing multiproduct assemble-to-order (ATO) inventory systems is a long-standing difficult problem. We consider ATO systems with identical component lead times and a general “bill of materials.” We use a related two-stage stochastic program (SP) to set a lower bound on the average inventory cost and develop inventory control policies for the dynamic ATO system using this SP. We apply the first-stage SP optimal solution to specify a base-stock replenishment policy, and the second-stage SP recourse linear program to make allocation decisions. We prove that our policies are asymptotically optimal on the diffusion scale, so the percentage gap between the average cost from its lower bound diminishes to zero as the lead time grows.

Genetic algorithm for multi-level assembly systems under stochastic lead times

The aim of this paper is to propose tools to adapt and parameterize the Material Requirement Planning (MRP) method under lead time uncertainty. We study multi-level assembly systems with one type of finished products and several types of components. We consider that each component has a fixed unit inventory cost and the finished product has a backlogging cost per unit of time. The lead times of components are discrete random variables, and the costumer's demand of the finished product is known. A general mathematical model for supply planning of multi-level assembly systems is presented. A Genetic Algorithm (GA) method is proposed to minimize the sum of the average inventory holding cost for components and the average backlogging and inventory holding costs for the finished product.

Condition-based maintenance of wind power generation systems considering different turbine types and lead times

Condition monitoring measurements, such as vibration data, acoustic emission data, oil analysis data, power voltage and current data, etc., can be obtained from wind turbine components and be utilised to evaluate and predict the health conditions of the components and the turbines. The objective of condition-based maintenance (CBM) is to optimise the predictive maintenance activities based on the condition monitoring and prediction information to minimise the overall costs of wind power generation systems. In existing work, all the wind turbines are assumed to be of the same type and the lead times of different components are assumed to be constant. This is not the case in many practical applications. In this paper, we develop a CBM approach for wind turbine systems considering different types of wind turbines in a wind farm and different lead times for different turbine components, which lead to more accurate modelling of CBM activities in actual wind farms. In the proposed CBM approach, we present a new CBM policy involving two design variables for each turbine type, a method for turbine failure probability evaluation considering different lead times and a CBM cost evaluation method. Numerical examples are provided to demonstrate the proposed CBM approach.

Performance Evaluation of Machine Tool Probe for In-process Inspection of 2D and 3D Geometries

Abstract Dimensional measurement of work piece in between the manufacturing process is an essential method to achieve the precision components. The machine tool probing system can measure machined features of work piece placed on the machine table after partial or final machining operation. The in-process machine tool probing system is helpful in minimising the lead time of component by eliminating the unloading time on the machine and loading and inspection time on the measuring instrument. It also improve the part quality through process control. In this research, the component inspection is performed by optical machine tool probe when position into the spindle of CNC milling machine. The relative movement between the probe and job are programmed by developing a CNC part program. The probe touches the machined surface as per the program codes and the optical machine interface (Transceiver) receives signal data through infrared optical waves and these signals transformed into the CNC controller through wired network. The CNC controller displays the data points on the screen. These data points were transferred into CAD system for generating lines, curves and thereafter surfaces and these generated surfaces were analysed against CAD model of the designed component for dimensional evaluation. The results obtained by this machine tool probe for 2D features are validated through digital height gauge and 3D features are by coordinate measuring machine (CMM). It was found that the optical probing system can be employed during in-process inspection and can be replaced measurement by costly CMMs for inspection of 3D features of low precision components.

Determination and study of lead times of metallic components in the aerospace industry through a Cox-type hazard model

The aim of this paper is the analysis of the factors that have influence on the lead time of batches of metallic components of aerospace engines. The approach used in this article employs Cox-type hazard models, which are a well-recognized statistical technique for exploring the relationship between a time variable, in this case the lead time, usually called survival variable, and several explanatory variables (covariates). A model that estimates the lead time of different components has been developed using some sample batches, and its validity is checked with a different sample of similar components.

Multi-objective supply planning for two-level assembly systems with stochastic lead times

This paper considers two-level assembly systems whose lead times of components are stochastic with known discrete random distributions. In such a system, supply planning requires determination of release dates of components at level 2 in order to minimize expected holding cost and to maximize customer service. Hnaien et al. [Hnaien F, Delorme X, Dolgui A. Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times. Computers and Operations Research 2010; 37:1835–43] have recently examined this problem, trying to solve it through multi-objective genetic algorithms. However, some reconsideration in their paper is unavoidable. The main problem with Hnaien et al. proposal is their wrong mathematical model. In addition, the proposed algorithms do not work properly in large-scale instances. In the current paper, this model is corrected and solved via a new approach based on NSGA-II that is called Guided NSGA-II. This approach tries to guide search toward preferable regions in the solution space. According to the statistical analyses, the guided NSGA-II has the higher performance in comparison with the basic NSGA-II used by Hnaien et al. Moreover, the wrongly reported characteristics of the Pareto front shape provided by Hnaien et al. are modified.

A note on analytic calculation of planned lead times for assembly systems under POQ policy and service level constraint

The problem of MRP offsetting for assembly systems with random component procurement times is considered. POQ policy is used for the case where all components have identical lead time distributions and holding costs. An analytical optimization model is proposed and solved. This model minimizes the sum of the average holding and setup costs for the components while satisfying a desired service level. Compared with known approaches, this is a multi-period model with no restriction on the number of components. All possible distributions can be used for component lead times. The decision variables are integer; they represent the periodicity and planned lead times for components. The model can be used for initial approximate calculation of safety lead times in many industrial situations. Finally, the advantage of this analytical model is in its simplicity.

A Stochastic Programming Based Inventory Policy for Assemble-to-Order Systems with Application to the W Model

We consider assemble-to-order inventory systems with identical component lead times. We use a stochastic program (SP) to develop an inventory strategy that allows preferential component allocation for minimizing total inventory cost. We prove that the solution of a relaxation of this SP provides a lower bound on total inventory cost for all feasible policies. We demonstrate and test our approach on the W system, which involves three components used to produce two products. (There are two unique parts and a common part. Each product uses the common part and its own unique part.) For the W system, we develop efficient solution procedures for the SP as well as the relaxed SP. We define a simple priority allocation policy that mimics the second-stage SP recourse solution and set base-stock levels according to the first-stage SP solution. We show that our policy achieves the lower bound and is, thus, optimal in two situations: when a certain symmetry condition in the cost parameters holds and when the SP solution satisfies a “balanced capacity” condition. For other cases, numerical results demonstrate that our policy works well and outperforms alternative approaches in many circumstances.

Optimal time phasing and periodicity for MRP with POQ policy

This paper deals with component supply planning for assembly systems where several types of components are ordered from external suppliers to produce a single type of finished product. Actual component lead times are often different from those contracted because of random delays. An MRP approach with periodic order quantity (POQ) policy is used for the supply planning of components. Our aim is to find the optimal values of the order periodicity and planned lead times at the offsetting step of the MRP procedure. The goal is to minimise the sum of the average component holding, finished product backlogging and setup costs. Several theoretical properties of the objective function are proven and an efficient optimization algorithm is developed. The proofs of the explicit form and properties of the objective function as well as the development of the optimization algorithm occupy a large portion of this paper. The method developed can be used for the optimization of time phasing and periodicity for such an MRP system under lead time uncertainties.

Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model

The aim of the present paper is the analysis of the factors that have influence over the lead time of batches of metallic components of aerospace engines. The approach used in this article employs support vector machines (SVMs). They are a set of related supervised learning methods used for classification and regression. In this research a model that estimates whether a batch is going to be finished on the forecasted time or not was developed using some sample batches. The validity of this model was checked using a different sample of similar components. This model allows predicting the manufacturing time before the start of the manufacturing. Therefore a buffer time can be taken into account in order to avoid delays with respect to the customer’s delivery. Further, some other researches have been performed over the data in order to determine which factors have more influence in manufacturing delays. Finally, conclusions of this study are exposed.

A simple and accurate approximation for the order fill rates in lost-sales Assemble-to-Order systems

In this paper we consider an Assemble-to-Order system with multiple end-products. Demands for an end-product follow a Poisson process and each end-product requires a fixed set of components. We are interested in the order fill rates, i.e., the percentage of demands for which all requested components are available from stock. Requested components that are not in stock are supplied via an emergency shipment and the demand for these components is lost for the stockpoint under consideration. The component lead times are deterministic and may differ per component. The inventory of each component is controlled via a base stock policy. We show that the system decomposes into subsystems which can be analyzed independently. Each subsystem can be approximated by a subsystem with exponentially distributed lead times, for which an exact evaluation exists. For big subsystems, however, this method requires considerable computational effort. Therefore, we formulate a simple and accurate approximation for the order fill rates. Our approximation uses two estimates of which one generally gives an underestimation of the order fill rate and the other one an overestimation. A weighing factor is used to combine these two estimates into an approximate value. The approximation is shown to be accurate and requires little computational effort.